International audience

In this paper, we present an advanced analysis of near optimal algorithms that use limited space to solve the frequency estimation, heavy hitters, frequent items, and top-k approximation in the bounded deletion model. We define the family of SpaceSaving± algorithms and explain why the original SpaceSaving± algorithm only works when insertions and deletions are not interleaved. Next, we propose the new Double SpaceSaving±, Unbiased Double SpaceSaving±, and Integrated SpaceSaving± algorithms and prove their correctness. The three proposed algorithms represent different trade-offs, in which Double SpaceSaving± can be extended to provide unbiased estimations while Integrated SpaceSaving± uses less space. Since data streams are often skewed, we present an improved analysis of these algorithms and show that errors do not depend on the hot items. We also demonstrate how to achieve relative error guarantees under mild assumptions. Moreover, we establish that the important mergeability property is satisfied by all three algorithms, which is essential for running the algorithms in distributed settings.

Log Structured Merge Trees (LSM-tree) based key-value stores are widely used in many storage systems to support a variety of operations such as updates, point reads, and range reads. Traditionally, the merge policy of LSM-trees organizes data into multiple levels of exponentially increasing capacity to support high-speed writes. However, we contend that the traditional merge policies are not optimized for reads. In this work, we present Autumn, a scalable and read-optimized LSM-tree based key-value store with near-optimal worst-case point and range read costs. The key idea in improving read performance is to dynamically adjust the capacity ratio between two adjacent levels as more data are stored. As a result, lower levels gradually increase their capacities and more actively merges. In particular, point and range read cost improves from the previous known O(logN) complexity to$O(\sqrt{logN})$in Autumn by applying the novel Garnering merge policy. While the Garnering merge policy optimizes for both point reads and range reads, it maintains high performance for writes by inherently prioritizing the merges in the lower levels, as Garnering schedules more merges for the lower levels. We implemented Autumn on top of RocksDB and LevelDB and experimentally show the gain in performance for real-world workloads.

Translating natural language questions into SQL queries (NL2SQL) is a challenging task of great practical importance. Prior work has extensively studied how to address NL2SQL using Large Language Models (LLMs) with solutions ranging from careful prompt engineering, to fine-tuning existing LLMs, or even training custom models. However, a remaining challenging problem in NL2SQL is the inherent ambiguity in the natural language questions asked by users. In this paper, we introduce Sphinteract, a framework designed to assist LLMs in generating high-quality SQL answers that accurately reflect the user intent. Our key insight to resolve ambiguity is to take into account minimal user feedback interactively. We introduce the Summarize, Review, Ask (SRA) paradigm, which guides LLMs in identifying ambiguities in NL2SQL tasks and generates targeted questions for the user to answer. We propose three different methods of how to process user feedback and generate SQL queries based on user input. Our experiments on the challenging KaggleDBQA and BIRD benchmarks demonstrate that by means of asking clarification questions to the user, LLMs can efficiently incorporate the feedback, resulting in accuracy improvements of up to 42%.

Edge computing while bringing computation and data closer to users in order to improve response time, distributes edge servers in wide area networks resulting in increased communication latency between the servers. Synchronizing globally distributed edge servers, especially in the presence of Byzantine servers, becomes costly due to the high communication complexity of Byzantine fault-tolerant consensus protocols. In this paper, we present Ziziphus, a geo-distributed system that partitions edge servers into fault-tolerant zones where each zone processes transactions initiated by nearby clients locally. Global synchronization among zones is required only in special situations, e.g., migration of clients from one zone to another. On the one hand, the two-level architecture of Ziziphus confines the malicious behavior of nodes within zones requiring a much cheaper protocol at the top level for global synchronization. On the other hand, Ziziphus processes local transactions within zones by edge servers closer to clients resulting in enhanced performance. Ziziphus further introduces zone clusters to enhance scalability where instead of running global synchronization among all zones, only zones of a single cluster are synchronized.

We present Waffle, a datastore that protects an application's data access patterns from a passive persistent adversary. Waffle achieves this without prior knowledge of the input data access distribution, making it the first of its kind to adaptively handle input sequences under a passive persistent adversary. Waffle maintains a constant bandwidth and client-side storage overhead, which can be adjusted to suit the application owner's preferences. This flexibility allows the owner to fine-tune system parameters and strike a balance between security and performance. Our evaluation, utilizing the Yahoo! Cloud Serving Benchmark (YCSB) benchmark and Redis as the backend storage, demonstrates promising results. The insecure baseline outperforms Waffle by a mere 5-6x, whereas Waffle outperforms Pancake-a state-of-the-art oblivious datastore under passive persistent adversaries-by 45-57%, and a concurrent ORAM system, TaoStore, by 102x.

The tutorial focuses on Private Information Retrieval (PIR), which allows clients to privately query public or server-owned databases without disclosing their queries. The tutorial covers the basic concepts of PIR such as its types, construction, and critical building blocks, including homomorphic encryption. It also discusses the performance of PIR, existing optimizations for scalability, real-life applications of PIR, and ways to extend its functionalities.

This errata article points out an implicit assumption in the work of four of us published in VLDB 2022. The SpaceSaving± algorithm in bounded deletion data stream presented in the paper implicitly assumed deletions happen after all insertions. When insertions and deletions are interleaved, that algorithm may severely underestimate item's frequency. We first illustrate this phenomenon by an example and then present a modified algorithm with minor changes to allow interleaving between insertions and deletions. We also include a pointer to a full analysis of the new algorithms.

System operators are often interested in extracting different feature streams from multi-dimensional data streams; and reporting their distributions at regular intervals, including the heavy hitters that contribute to the tail portion of the feature distribution. Satisfying these requirements to increase data rates with limited resources is challenging. This paper presents the design and implementation of Panakos that makes the best use of available resources to report a given feature's distribution accurately, its tail contributors, and other stream statistics (e.g., cardinality, entropy, etc.). Our key idea is to leverage the skewness inherent to most feature streams in the real world. We leverage this skewness by disentangling the feature stream into hot, warm, and cold items based on their feature values. We then use different data structures for tracking objects in each category. Panakos provides solid theoretical guarantees and achieves high performance for various tasks. We have implemented Panakos on both software and hardware and compared Panakos to other state-of-the-art sketches using synthetic and real-world datasets. The experimental results demonstrate that Panakos often achieves one order of magnitude better accuracy than the state-of-the-art solutions for a given memory budget.

International audience

Consider a cloud server that owns a key-value store and provides a private query service to its clients. Preserving client privacy in this setting is difficult because the key-value store ispublic, and a client cannot encrypt or modify it. Therefore, privacy in this context implies hiding the access pattern of a client. Pantheon is a system that cryptographically allows a client to retrieve the value corresponding to a key from apublickey-value store without allowing the server or any adversary to know any information about the key or value accessed. Pantheon devises a single-round retrieval protocol which reduces server-side latency by refining its cryptographic machinery and massively parallelizing the query execution workload. Using these novel techniques, Pantheon achieves a 93X improvement for server-side latency over a state-of-the-art solution.

Today's large-scale data management systems need to address distributed applications' confidentiality and scalability requirements among a set of collaborative enterprises. This paper presents Qanaat , a scalable multi-enterprise permissioned blockchain system that guarantees the confidentiality of enterprises in collaboration workflows. Qanaat presents data collections that enable any subset of enterprises involved in a collaboration workflow to keep their collaboration private from other enterprises. A transaction ordering scheme is also presented to enforce only the necessary and sufficient constraints on transaction order to guarantee data consistency. Furthermore, Qanaat supports data consistency across collaboration workflows where an enterprise can participate in different collaboration workflows with different sets of enterprises. Finally, Qanaat presents a suite of consensus protocols to support intra-shard and cross-shard transactions within or across enterprises.

In this paper, we propose the first deterministic algorithms to solve the frequency estimation and frequent item problems in the bounded-deletion model. We establish the space lower bound for solving the deterministic frequent items problem in the bounded-deletion model, and propose Lazy SpaceSaving ± and SpaceSaving ± algorithms with optimal space bound. We develop an efficient implementation of the SpaceSaving ± algorithm that minimizes the latency of update operations using novel data structures. The experimental evaluations testify that SpaceSaving ± has accurate frequency estimations and achieves very high recall and precision across different data distributions while using minimal space. Our experiments clearly demonstrate that, if allowed the same space, SpaceSaving± is more accurate than the state-of-the-art protocols with up to logU - 1/ logU of the items deleted, where U is the size of the input universe. Moreover, motivated by prior work, we propose Dyadic SpaceSaving ± , the first deterministic quantile approximation sketch in the bounded-deletion model.

International audience

Geo-distributed databases are the state of the art tools for managing cloud-based data. But maintaining hot records in geo-distributed databases such as Google's Spanner can be expensive, as it synchronizes each update across a majority of replicas. Frequent synchronization poses an obstacle to achieve high throughput for contentious updateheavy workloads. While such synchronizations are inevitable for complex data types, simple data types such as aggregate data can benefit from reduced synchronizations. To this end, we propose an alternate data management system, Samya, to manage aggregate cloud resource usage data. Samya disaggregates available resources and stores fractions of these resources across geo-distributed sites. Dis-aggregation allows sites to serve client requests independently without synchronization for each update. Samya incorporates a learning mechanism to predict future resource demands. If the predicted demand is not satisfied locally, a synchronization protocol, Avantan, is executed to redistribute available resources in the system. Avantan is a novel fault-tolerant consensus protocol where sites agree on the global availability of resources prior to redistribution. Experiments conducted on Google Cloud Platform highlight that dis-aggregating data and reducing synchronizations allows Samya to commit 16x to 18x more transactions than state of the art cloud geo-distributed systems such as Spanner and CockroachDB.

Scalability is one of the main roadblocks to business adoption of blockchain systems. Despite recent intensive research on using sharding techniques to enhance the scalability of blockchain systems, existing solutions do not efficiently address cross-shard transactions. In this paper, we introduce SharPer, a scalable permissioned blockchain system. In SharPer, nodes are clustered and each data shard is replicated on the nodes of a cluster. SharPer supports networks consisting of either crash-only or Byzantine nodes. In SharPer, the blockchain ledger is formed as a directed acyclic graph and each cluster maintains only a view of the ledger. SharPer incorporates decentralized flattened protocols to establish cross-shard consensus. The decentralized nature of the cross-shard consensus in SharPer enables parallel processing of transactions with nonoverlapping clusters. Furthermore, SharPer provides deterministic safety guarantees. The experimental results reveal the efficiency of SharPer in terms of performance and scalability especially in workloads with a low percentage of cross-shard transactions.

The unique features of blockchains such as immutability, transparency, provenance, and authenticity have been used by many large-scale data management systems to deploy a wide range of distributed applications including supply chain management, healthcare, and crowdworking in permissioned settings. Unlike permissionless settings, e.g., Bitcoin, where the network is public, and anyone can participate without a specific identity, a permissioned blockchain system consists of a set of known, identified nodes that might not fully trust each other. While the characteristics of permissioned blockchains are appealing to a wide range of largescale data management systems, these systems, have to satisfy four main requirements: confidentiality, verifiability, performance, and scalability. Various approaches have been developed in industry and academia to satisfy these requirements with varying assumptions and costs. The focus of this tutorial is on presenting many of these techniques while highlighting the trade-offs among them. We demonstrate the practicality of such techniques in real-life by presenting three different applications, i.e., supply chain management, large-scale databases, and multi-platform crowdworking environments, and show how those techniques can be utilized to meet the requirements of such applications.

Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the ”gig” economy has given rise to independent platforms that provide competing and complementary services. Workers as well as requesters with specific tasks may need to work for or avail from the services of multiple platforms resulting in the rise of multi-platform crowdworking systems. Recently, there has been increasing interest by governmental, legal and social institutions to enforce regulations, such as minimal and maximal work hours, on crowdworking platforms. Platforms within multi-platform crowdworking systems, therefore, need to collaborate to enforce cross-platform regulations. While collaborating to enforce global regulations requires the transparent sharing of information about tasks and their participants, the privacy of all participants needs to be preserved. In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. We then present Separ, a multi-platform crowdworking system that enforces a large sub-space of practical global regulations on a set of distributed independent platforms in a privacy-preserving manner. Separ, enforces privacy using lightweight and anonymous tokens, while transparency is achieved using fault-tolerant blockchain ledgers shared among multiple platforms. The privacy guarantees of Separ against covert adversaries are formalized and thoroughly demonstrated, while the experiments reveal the efficiency of Separ in terms of performance and scalability.

This errata article discusses and corrects a minor error in our work published in VLDB 2019. The discrepancy specifically pertains to Algorithms 3 and 4. The algorithms presented in the paper are biased towards a commit decision in a specific failure scenario. We explain the error using an example before correcting the algorithm.

Recently the long standing problem of optimal construction of quantile sketches was resolved by K arnin, L ang, and L iberty using the KLL sketch (FOCS 2016). The algorithm for KLL is restricted to online insert operations and no delete operations. For many real-world applications, it is necessary to support delete operations. When the data set is updated dynamically, i.e., when data elements are inserted and deleted, the quantile sketch should reflect the changes. In this paper, we propose KLL ± , the first quantile approximation algorithm to operate in the bounded deletion model to account for both inserts and deletes in a given data stream. KLL ± extends the functionality of KLL sketches to support arbitrary updates with small space overhead. The space bound for KLL ± is [EQUATION], where ∈ and δ are constants that determine precision and failure probability, and α bounds the number of deletions with respect to insert operations. The experimental evaluation of KLL ± highlights that with minimal space overhead, KLL ± achieves comparable accuracy in quantile approximation to KLL.

Recent works in social network stream analysis have shown that a user's online persona attributes (e.g., location, gender, ethnicity, political interest, etc.) can be accurately inferred from the topics the user writes about or engages with. Revealing a user's sensitive attributes could represent a privacy threat to some individuals. Microtargeting (e.g., the Cambridge Analytica scandal), surveillance, and discriminating ads are examples of threats to user privacy caused by sensitive attribute inference. In this paper, we propose Multifaceted privacy, a novel privacy model that aims to obfuscate a user's sensitive attributes while publicly preserving the user's public persona. To achieve multifaceted privacy, we build Aegis, a prototype client-centric social network stream processing system that helps preserve multifaceted privacy, and thus allowing social network users to freely express their online personas without revealing their sensitive attributes of choice. Aegis continuously suggests topics and hashtags to social network users to write about in order to obfuscate their sensitive attributes and hence confuse content-based sensitive attribute inferences. Our experiments show that adding as few as 0 to 4 obfuscation posts (depending on how revealing the original post is) successfully hides a user sensitive attributes without changing the user's public persona attributes.

Modern large-scale data management systems utilize consensus protocols to provide fault tolerance. Consensus protocols are extensively used in the distributed database infrastructure of large enterprises such as Google, Amazon, and Facebook as well as permissioned blockchain systems like IBM’s Hyperledger Fabric. In the last four decades, numerous consensus protocols have been proposed to cover a broad spectrum of distributed database systems. On one hand, distributed networks might be synchronous, partially synchronous, or asynchronous, and on the other hand, infrastructures might consist of crashonly nodes, Byzantine nodes or both. In addition, a consensus protocol might follow a pessimistic or optimistic strategy to process transactions. Furthermore, while traditional consensus protocols assume a priori known set of nodes, in permissionless blockchains, nodes are assumed to be unknown. Finally, consensus protocols have explored a variety of performance trade-offs between the number of phases/messages (latency), the number of required nodes, message complexity, and the activity level of participants. In this tutorial, we discuss consensus protocols that are used in modern large-scale data management systems, classify them into different categories based on their assumptions on network synchrony, failure model of nodes, etc., and elaborate on their main advantages and limitations.

Large scale data management systems utilize State Machine Replication to provide fault tolerance and to enhance performance. Fault-tolerant protocols are extensively used in the distributed database infrastructure of large enterprises such as Google, Amazon, and Facebook. However, and in spite of years of intensive research, existing fault-tolerant protocols do not adequately address hybrid cloud environments consisting of private and public clouds which are widely used by enterprises. In this paper, we consider a private cloud consisting of nonmalicious nodes (crash-only failures) and a public cloud with possible malicious failures. We introduce SeeMoRe, a hybrid State Machine Replication protocol that uses the knowledge of where crash and malicious failures may occur in a public/private cloud environment to improve overall performance. SeeMoRe has three different modes that can be used depending on the private cloud load and the communication latency between the public and private clouds. SeeMoRe can dynamically transition from one mode to another. Furthermore, an extensive evaluation reveals that SeeMoRe’s performance is close to the state of the art crash fault-tolerant protocols while tolerating malicious failures.

The recent adoption of blockchain technologies and open permissionless networks suggest the importance of peer-to-peer atomic cross-chain transaction protocols. Users should be able to atomically exchange tokens and assets without depending on centralized intermediaries such as exchanges. Recent peer-to-peer atomic cross-chain swap protocols use hashlocks and timelocks to ensure that participants comply to the protocol. However, an expired timelock could lead to a violation of the all-or-nothing atomicity property. An honest participant who fails to execute a smart contract on time due to a crash failure, denial of service attacks or even network delays might end up losing assets. Although a crashed participant is the only participant who ends up worse off, current proposals are unsuitable for atomic cross-chain transactions in asynchronous environments where crash failures and network delays are the norm. In this paper, we present AC 3 WN, the first decentralized all-or-nothing atomic cross-chain commitment protocol. The redeem and refund events of the smart contracts that exchange assets are modeled as conflicting events. An open permissionless network of witnesses is used to guarantee that conflicting events could never simultaneously occur and either all smart contracts in an atomic cross-chain transaction are redeemed or all of them are refunded.

International audience

The uprise of Bitcoin and other peer-to-peer cryptocurrencies has opened many interesting and challenging problems in cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains in both permissionless and permissioned settings. The focus of the tutorial is on the distributed systems and database aspects of the recent innovations in blockchains.

Despite recent intensive research, existing blockchain systems do not adequately address all the characteristics of distributed applications. In particular, distributed applications collaborate with each other following service level agreements (SLAs) to provide different services. While collaboration between applications, e.g., cross-application transactions, should be visible to all applications, the internal data of each application, e.g, internal transactions, might be confidential . In this paper, we introduce CAPER , a permissioned blockchain system to support both internal and cross-application transactions of collaborating distributed applications. In CAPER, the blockchain ledger is formed as a directed acyclic graph where each application accesses and maintains only its own view of the ledger including its internal and all cross-application transactions. CAPER also introduces three consensus protocols to globally order cross-application transactions between applications with different internal consensus protocols. The experimental results reveal the efficiency of CAPER in terms of performance and scalability.

Data storage in the Cloud needs to be scalable and fault-tolerant. Atomic commitment protocols such as Two Phase Commit (2PC) provide ACID guarantees for transactional access to sharded data and help in achieving scalability. Whereas consensus protocols such as Paxos consistently replicate data across different servers and provide fault tolerance. Cloud based datacenters today typically treat the problems of scalability and fault-tolerance disjointedly. In this work, we propose a unification of these two different paradigms into one framework called Consensus and Commitment (C&C) framework. The C&C framework can model existing and well known data management protocols as well as propose new ones. We demonstrate the advantages of the C&C framework by developing a new atomic commitment protocol, Paxos Atomic Commit (PAC), which integrates commitment with recovery in a Paxos-like manner. We also instantiate commit protocols from the C&C framework catered to different Cloud data management techniques. In particular, we propose a novel protocol, Generalized PAC (G-PAC) that integrates atomic commitment and fault tolerance in a cloud paradigm involving both sharding and replication of data. We compare the performance of G-PAC with a Spanner-like protocol, where 2PC is used at the logical data level and Paxos is used for consistent replication of logical data. The experimental results highlight the benefits of combining consensus along with commitment into a single integrated protocol.

Although outsourcing data to cloud storage has become popular, the increasing concerns about data security and privacy in the cloud limits broader cloud adoption. Ensuring data security and privacy, therefore, is crucial for better and broader adoption of the cloud. This tutorial provides a comprehensive analysis of the state-of-the-art in the context of data security and privacy for outsourced data. We aim to cover common security and privacy threats for outsourced data, and relevant novel schemes and techniques with their design choices regarding security, privacy, functionality, and performance. Our explicit focus is on recent schemes from both the database and the cryptography and security communities that enable query processing over encrypted data and access oblivious cloud storage systems.

Performing non-aggregate range queries on cloud stored data, while achieving both privacy and efficiency is a challenging problem. This paper proposes constructing a differentially private index to an outsourced encrypted dataset. Efficiency is enabled by using a cleartext index structure to perform range queries. Security relies on both differential privacy (of the index) and semantic security (of the encrypted dataset). Our solution, PINED-RQ develops algorithms for building and updating the differentially private index. Compared to state-of-the-art secure index based range query processing approaches, PINED-RQ executes queries in the order of at least one magnitude faster. The security of PINED-RQ is proved and its efficiency is assessed by an extensive experimental validation.

In this paper, we propose Dynamic Paxos (DPaxos), a Paxos-based consensus protocol to manage access to partitioned data across globally-distributed datacenters and edge nodes. DPaxos is intended to implement a State Machine Replication component in data management systems for the edge. DPaxos targets the unique opportunities of utilizing edge computing resources to support emerging applications with stringent mobility and real-time requirements such as Augmented and Virtual Reality and vehicular applications. The main objective of DPaxos is to reduce the latency of serving user requests, recovering from failures, and reacting to mobility. DPaxos achieves these objectives by a few proposed changes to the traditional Paxos protocol. Most notably, DPaxos proposes a dynamic allocation of quorums ( i.e. , groups of nodes) that are needed for Paxos Leader Election. Leader Election quorums in DPaxos are smaller than traditional Paxos and expand only in the presence of conflicts.

Bitcoin is a successful and interesting example of a global scale peer-to-peer cryptocurrency that integrates many techniques and protocols from cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the cryptocurrency system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains. The focus of the tutorial is on the distributed systems and database technical aspects of the recent innovations in blockchains.

Cloud-based data-intensive applications have to process high volumes of transactional and analytical requests on large-scale data. Businesses base their decisions on the results of analytical requests, creating a need for real-time analytical processing. We propose Janus, a hybrid scalable cloud datastore, which enables the efficient execution of diverse workloads by storing data in different representations. Janus manages big datasets in the context of datacenters, thus supporting scaling out by partitioning the data across multiple servers. This requires Janus to efficiently support distributed transactions. In order to support the different datacenter requirements, Janus also allows diverse partitioning strategies for the different representations. Janus proposes a novel data movement pipeline to continuously ensure up to date data between the different representations. Unlike existing multi-representation storage systems and Change Data Capture (CDC) pipelines, the data movement pipeline in Janus supports partitioning and handles both distributed transactions and diverse partitioning strategies. In this paper, we focus on supporting Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads, and hence use row and column-oriented representations, which are the most efficient representations for these workloads. Our evaluations over Amazon AWS illustrate that Janus can provide real-time analytical results, in addition to processing high-throughput transactional workloads.

Social media streams analysis can reveal the characteristics of people who engage with or write about different topics. Recent works show that it is possible to reveal sensitive attributes (e.g., location, gender, ethnicity, political views, etc.) of individuals by analyzing their social media streams. Although, the prediction of a user's sensitive attributes can be used to enhance the user experience in social media, revealing some attributes like the location could represent a threat on individuals. Users can obfuscate their location by posting about random topics linked to different locations. However, posting about random and sometimes contradictory topics that are not aligned with a user's online persona and posts could negatively affect the followers interested in her profile. This paper represents our vision about the future of user privacy on social media. Users can locally deploy a cyborg, an artificial intelligent system that helps people to defend their privacy on social media. We propose LocBorg, a location privacy preserving cyborg that protects users by obfuscating their location while maintaining their online persona. LocBorg analyzes the social media streams and recommends topics to write about that are similar to a user's topics of interest and aligned with the user's online persona but linked to other locations.

Towards the vision of building artificial intelligence systems that can assist with our everyday life, we introduce a proof of concept for a social media privacy "cyborg" which can locally and privately monitor a person's published content and offer advice or warnings when their privacy is at stake. The idea of a cyborg can be more general, as a separate local entity with its own computational resources, that can automatically perform several online tasks on our behalf. For this demonstration, we assume an attacker that can successfully infer user attributes, solely based on what the user has published (topic-based inference). We focus on Social Media privacy and specifically on the issue of exposing sensitive user-attributes, like location, or race, through published content. We built a privacy cyborg that can monitor a user's posted topics and automatically warn them in real time when a sensitive attribute is at risk of being exposed.

Global-scale data management (GSDM) empowers systems by providing higher levels of fault-tolerance, read availability, and efficiency in utilizing cloud resources. This has led to the emergence of global-scale data management and event processing. However, the Wide-Area Network (WAN) latency separating datacenters is orders of magnitude larger than typical network latencies, and this requires a reevaluation of many of the traditional design trade-offs of data management systems. Therefore, data management problems must be revisited to account for the new design space. In this tutorial, we survey recent developments in GSDM focusing on identifying fundamental challenges and advancements in addition to open research opportunities.

This demonstration introduces the database community to state-of-the-art cryptographic methods that ensure efficient oblivious access to cloud data. In particular, we explore oblivious storage systems which hide both the content of data and data access patterns from an untrusted cloud provider. The demo considers the popular and realistic setting where multiple users from a trusted group asynchronously access and edit potentially overlapping data sets through a trusted proxy. We present a detailed implementation of TaoStore (Sahin et al., S&P 2016), a new tree-based ORAM scheme that processes client requests concurrently and asynchronously in a non-blocking fashion, resulting in substantial gains in throughput, simplicity, and flexibility over previous systems. The demo is presented in the context of a pedagogical game, Guess the Access, which allows participants to play as an adversary trying to guess queries against TaoStore or ObliviStore (Stefanov and Shi, S&P 2013), a recent oblivious storage system which has been shown to leak access patterns. The proposed game will highlight the subtleties and intricacies that underlie the cryptographic methods used to design oblivious storage systems.

Today's web applications and social networks are serving billions of users around the globe. These users generate billions of key lookups and millions of data object updates per second. A single user's social network page load requires hundreds of key lookups. This scale creates many design challenges for the underlying storage systems. First, these systems have to serve user requests with low latency. Any increase in the request latency leads to a decrease in user interest. Second, storage systems have to be highly available. Failures should be handled seamlessly without affecting user requests. Third, users consume an order of magnitude more data than they produce. Therefore, storage systems have to be optimized for read-intensive workloads. To address these challenges, distributed in-memory caching services have been widely deployed on top of persistent storage. In this tutorial, we survey the recent developments in distributed caching services. We present the algorithmic and architectural efforts behind these systems focusing on the challenges in addition to open research questions.

Global-scale data management (GSDM) empowers systems by providing higher levels of fault-tolerance, read availability, and efficiency in utilizing cloud resources. This has led to the emergence of global-scale data management and event processing. However, the Wide-Area Network (WAN) latency separating data is orders of magnitude larger than conventional network latencies, and this requires a reevaluation of many of the traditional design trade-offs of data management systems. Therefore, data management problems must be revisited to account for the new design space. In this tutorial we survey recent developments in GSDM focusing on identifying fundamental challenges and advancements in addition to open research opportunities.

Geo-replication is the process of maintaining copies of data at geographically dispersed datacenters for better availability and fault-tolerance. The distinguishing characteristic of geo-replication is the large wide-area latency between datacenters that varies widely depending on the location of the datacenters. Thus, choosing which datacenters to deploy a cloud application has a direct impact on the observable response time. We propose an optimization framework that automatically derives a geo-replication placement plan with the objective of minimizing latency. By running the optimization framework on real placement scenarios, we learn a set of placement optimizations for geo-replication. Some of these optimizations are surprising while others are in retrospect straight-forward. In this demonstration, we highlight the geo-replication placement optimizations through the DB-Risk game. DB-Risk invites players to create different placement scenarios while experimenting with the proposed optimizations. The placements created by the players are tested on real cloud deployments.

Physical events in the real world are known to trigger reactions and then discussions in online social media. Mining these reactions through online social sensors offers a fast and low cost way to understand what is happening in the physical world. In some cases, however, further study of the affected population's emotional state can improve this understanding. In our study we analyzed how car commuters react on Twitter while stuck in heavy traffic. We discovered that the online social footprint does not necessarily follow a strict linear correlation with the volume of a traffic jam. Through our analysis we offer a potential explanation: people's mood could be an additional factor, apart from traffic severity itself, that leads in fluctuations of the observed reaction in social media. This finding can be important for social sensing applications where external factors, like sentiment, also contribute on how humans react.

Web-based applications face unprecedented workloads demanding the processing of a large number of events reaching to the millions per second. That is why developers are increasingly relying on scalable cloud platforms to implement cloud applications. Chariots exposes a shared log to be used by cloud applications. The log is essential for many tasks like bookkeeping, recovery, and debugging. Logs offer linearizability and simple append and read operations of immutable records to facilitate building complex systems like stream processors and transaction managers. As a cloud platform, Chariots offers fault-tolerance, persistence, and high-availability, transparently. Current shared log infrastructures suffer from the bottleneck of serializing log records through a centralized server which limits the throughput to that of a single machine. We propose a novel distributed log store, called the Fractal Log Store (FLStore), that overcomes the bottleneck of a single-point of contention. FLStore maintains the log within the datacenter. We also propose Chariots, which provides multi-datacenter replication for shared logs. In it, FLStore is leveraged as the log store. Chariots maintains causal ordering of records in the log and has a scalable design that allows elastic expansion of resources.

For data-intensive applications with many concurrent users, modern distributed main memory database management systems (DBMS) provide the necessary scale-out support beyond what is possible with single-node systems. These DBMSs are optimized for the short-lived transactions that are common in on-line transaction processing (OLTP) workloads. One way that they achieve this is to partition the database into disjoint subsets and use a single-threaded transaction manager per partition that executes transactions one-at-a-time in serial order. This minimizes the overhead of concurrency control mechanisms, but requires careful partitioning to limit distributed transactions that span multiple partitions. Previous methods used off-line analysis to determine how to partition data, but the dynamic nature of these applications means that they are prone to hotspots. In these situations, the DBMS needs to reconfigure how data is partitioned in real-time to maintain performance objectives. Bringing the system off-line to reorganize the database is unacceptable for on-line applications.

Cross datacenter replication is increasingly being deployed to bring data closer to the user and to overcome datacenter outages. The extent of the influence of wide-area communication on serializable transactions is not yet clear. In this work, we derive a lower-bound on commit latency. The sum of the commit latency of any two datacenters is at least the Round-Trip Time (RTT) between them. We use the insights and lessons learned while deriving the lower-bound to develop a commit protocol, called Helios, that achieves low commit latencies. Helios actively exchanges transaction logs (history) between datacenters. The received logs are used to decide whether a transaction can commit or not. The earliest point in the received logs that is needed to commit a transaction is decided by Helios to ensure a low commit latency. As we show in the paper, Helios is theoretically able to achieve the lower-bound commit latency. Also, in a real-world deployment on five datacenters, Helios has a commit latency that is close to the optimal.

Attributed graphs are becoming important tools for modeling information networks, such as the Web and various social networks (e.g. Facebook, LinkedIn, Twitter). However, it is computationally challenging to manage and analyze attributed graphs to support effective decision making. In this paper, we propose, Pagrol, a parallel graph OLAP (Online Analytical Processing) system over attributed graphs. In particular, Pagrol introduces a new conceptual Hyper Graph Cube model (which is an attributed-graph analogue of the data cube model for relational DBMS) to aggregate attributed graphs at different granularities and levels. The proposed model supports different queries as well as a new set of graph OLAP Roll-Up/Drill-Down operations. Furthermore, on the basis of Hyper Graph Cube, Pagrol provides an efficient MapReduce-based parallel graph cubing algorithm, MRGraph-Cubing, to compute the graph cube for an attributed graph. Pagrol employs numerous optimization techniques: (a) a self-contained join strategy to minimize I/O cost; (b) a scheme that groups cuboids into batches so as to minimize redundant computations; (c) a cost-based scheme to allocate the batches into bags (each with a small number of batches); and (d) an efficient scheme to process a bag using a single MapReduce job. Results of extensive experimental studies using both real Facebook and synthetic datasets on a 128-node cluster show that Pagrol is effective, efficient and scalable.

The past decade has witnessed an increasing adoption of cloud database technology, which provides better scalability, availability, and fault-tolerance via transparent partitioning and replication, and automatic load balancing and fail-over. However, only a small number of cloud databases provide strong consistency guarantees for distributed transactions, despite decades of research on distributed transaction processing, due to practical challenges that arise in the cloud setting, where failures are the norm, and human administration is minimal. For example, dealing with locks left by transactions initiated by failed machines, and determining a multi-programming level that avoids thrashing without under-utilizing available resources, are some of the challenges that arise when using lock-based transaction processing mechanisms in the cloud context. Even in the case of optimistic concurrency control, most proposals in the literature deal with distributed validation but still require the database to acquire locks during two-phase commit when installing updates of a single transaction on multiple machines. Very little theoretical work has been done to entirely eliminate the need for locking in distributed transactions, including locks acquired during two-phase commit. In this paper, we re-design optimistic concurrency control to eliminate any need for locking even for atomic commitment, while handling the practical issues in earlier theoretical work related to this problem. We conduct an extensive experimental study to evaluate our approach against lock-based methods under various setups and workloads, and demonstrate that our approach provides many practical advantages in the cloud context.

Consolidation of multiple databases on the same server allows service providers to save significant resources because many production database servers are often under-utilized. Recent research investigates the problem of minimizing the number of servers required to host a set of tenants when the working sets of tenants are kept in main memory (e.g., in-memory OLAP workloads, or OLTP workloads), thus the memory assigned to each tenant, as well as the I/O bandwidth and CPU time, are all dictated by the working set size of the tenant. Other research investigates the reverse problem when the number of servers is fixed, but the amount of resources allocated to different tenants on the same server needs to be configured to optimize a cost function. In this paper we investigate the problem when neither the number of servers nor the amount of resources allocated to each tenant are fixed. This problem arises when consolidating OLAP workloads of tenants whose service-level agreements (SLAs) allow for queries to be answered from disk. We study the trade-off between the amount of memory and the I/O bandwidth assigned to OLAP workloads, and develop a principled approach for allocating resources to tenants in a manner that minimizes the total number of servers required to host all tenants while satisfying the SLA of each tenant. We then explain how we modified InnoDB, the storage engine of MySQL, to be able to change the amount of resources allocated to each tenant at runtime, so as to account for fluctuations in workloads. Finally, we evaluate our approach experimentally using the TPC-H benchmark to demonstrate its effectiveness and accuracy.

Cloud computing becomes a very successful paradigm for data computing and storage. Increasing concerns about data security and privacy in the cloud, however, have arisen. Ensuring security and privacy for data management and query processing in the cloud is critical for better and broader uses of the cloud. This tutorial covers recent research on cloud security and privacy, while focusing on the works that protect data confidentiality and query access privacy for sensitive data being stored and queried in the cloud. We provide a comprehensive study of state-of-the-art schemes and techniques for protecting data confidentiality and access privacy, and explain their tradeoffs in security, privacy, functionality and performance.

A multitenant database management system (DBMS) in the cloud must continuously monitor the trade-off between efficient resource sharing among multiple application databases (tenants) and their performance. Considering the scale of \attn{hundreds to} thousands of tenants in such multitenant DBMSs, manual approaches for continuous monitoring are not tenable. A self-managing controller of a multitenant DBMS faces several challenges. For instance, how to characterize a tenant given its variety of workloads, how to reduce the impact of tenant colocation, and how to detect and mitigate a performance crisis where one or more tenants' desired service level objective (SLO) is not achieved.

The First Law of Geography states "Everything is related to everything else, but near things are more related than distant things". This spatial significance has implications in various applications, trend detection being one of them. In this paper we propose a new algorithmic tool, GeoScope , to detect geo-trends. GeoScope is a data streams solution that detects correlations between topics and locations in a sliding window, in addition to analyzing topics and locations independently. GeoScope offers theoretical guarantees for detecting all trending correlated pairs while requiring only sub-linear space and running time. We perform various human validation tasks to demonstrate the value of GeoScope. The results show that human judges prefer GeoScope to the best performing baseline solution 4:1 in terms of the geographical significance of the presented information. As the Twitter analysis demonstrates, GeoScope successfully filters out topics without geo-intent and detects various local interests such as emergency events, political demonstrations or cultural events. Experiments on Twitter show that GeoScope has perfect recall and near-perfect precision.

Advances in operating system and storage-level virtualization technologies have enabled the effective consolidation of heterogeneous applications in a shared cloud infrastructure. Novel research challenges arising from this new shared environment include load balancing, workload estimation, resource isolation, machine replication, live migration, and an emergent need of automation to handle large scale operations with minimal manual intervention. Given that databases are at the core of most applications that are deployed in the cloud, database management systems (DBMSs) represent a very important technology component that needs to be virtualized in order to realize the benefits of virtualization from autonomic management of data-intensive applications in large scale data-centers. The goal of this tutorial is to survey the techniques used in providing elasticity in virtual machine systems, shared storage systems, and survey database research on multitenant architectures and elasticity primitives. This foundation of core Database as a Service advances, together with a primer of important related topics in OS and storage-level virtualization, are central for anyone that wants to operate in this area of research.

Web service providers have been using NoSQL datastores to provide scalability and availability for globally distributed data at the cost of sacrificing transactional guarantees. Recently, major web service providers like Google have moved towards building storage systems that provide ACID transactional guarantees for globally distributed data. For example, the newly published system, Spanner, uses Two-Phase Commit and Two-Phase Locking to provide atomicity and isolation for globally distributed data, running on top of Paxos to provide fault-tolerant log replication. We show in this paper that it is possible to provide the same ACID transactional guarantees for multi-datacenter databases with fewer cross-datacenter communication trips, compared to replicated logging. Instead of replicating the transactional log, we replicate the commit operation itself, by running Two-Phase Commit multiple times in different datacenters and using Paxos to reach consensus among datacenters as to whether the transaction should commit. Doing so not only replaces several inter-datacenter communication trips with intra-datacenter communication trips, but also allows us to integrate atomic commitment and isolation protocols with consistent replication protocols to further reduce the number of cross-datacenter communication trips needed for consistent replication; for example, by eliminating the need for an election phase in Paxos. We analyze our approach in terms of communication trips to compare it against the log replication approach, then we conduct an extensive experimental study to compare the performance and scalability of both approaches under various multi-datacenter setups.

A database management system (DBMS) serving a cloud platform must handle large numbers of application databases (or tenants ) that are characterized by diverse schemas, varying footprints, and unpredictable load patterns. Scaling out using clusters of commodity servers and sharing resources among tenants (i.e., multitenancy ) are important features of such systems. Moreover, when deployed on a pay-per-use infrastructure, minimizing the system's operating cost while ensuring good performance is also an important goal. Traditional DBMSs were not designed for such scenarios and hence do not possess the mentioned features critical for DBMSs in the cloud. We present ElasTraS, which combines three design principles to build an elastically-scalable multitenant DBMS for transaction processing workloads. These design principles are gleaned from a careful analysis of the years of research in building scalable key-value stores and decades of research in high performance transaction processing systems. ElasTraS scales to thousands of tenants, effectively consolidates tenants with small footprints while scaling-out large tenants across multiple servers in a cluster. ElasTraS also supports low-latency multistep ACID transactions , is fault-tolerant, self-managing, and highly available to support mission critical applications. ElasTraS leverages Albatross, a low overhead on-demand live database migration technique, for elastic load balancing by adding more servers during high load and consolidating to fewer servers during usage troughs. This elastic scaling minimizes the operating cost and ensures good performance even in the presence of unpredictable changes to the workload. We elucidate the design principles, explain the architecture, describe a prototype implementation, present the detailed design and implementation of Albatross, and experimentally evaluate the implementation using a variety of transaction processing workloads. On a cluster of 20 commodity servers, our prototype serves thousands of tenants and serves more than 1 billion transactions per day while migrating tenant databases with minimal overhead to allow lightweight elastic scaling. Using a cluster of 30 commodity servers, ElasTraS can scale-out a terabyte TPC-C database serving an aggregate throughput of approximately one quarter of a million TPC-C transactions per minute.

Recent studies on the diffusion of information in social networks have largely focused on models based on the influence of local friends. In this paper, we challenge the generalizability of this approach and revive theories introduced by social scientists in the context of diffusion of innovations to model user behavior. To this end, we study various diffusion models in two different online social networks, Digg and Twitter. We first evaluate the applicability of two representative local influence models and show that the behavior of most social networks users are not captured by these local models. Next, driven by theories introduced in the diffusion of innovations research, we introduce a novel diffusion model called Gaussian Logit Curve Model (GLCM) that models user behavior with respect to the behavior of the general population. Our analysis shows that GLCM captures user behavior significantly better than local models, especially in the context of Digg. Aiming to capture both the local and global signals, we introduce various hybrid models and evaluate them through statistical methods. Our methodology models each user separately, automatically determining which users are driven by their local relations and which users are better defined through adopter categories, therefore capturing the complexity of human behavior.

Social networks, mobility and the cloud represent special and unique opportunities for synergy among several existing and emerging communities that are now often evolving in isolated silos. All three areas hold much promise for the future of computing, and represent significant challenges for large scale data management. As these three areas evolve, their direct influence on significant decisions on each other becomes evident and critical. This panel will bring together a set of renowned researchers who will explore and discuss the synergy and tensions among critical and often intertwined research and application issues that arise in the context of social networks and mobility in a cloud infrastructure setting.

Cloud computing becomes a successful paradigm for data computing and storage. Increasing concerns about data security and privacy in the cloud, however, have emerged. Ensuring security and privacy for data management and query processing in the cloud is critical for better and broader uses of the cloud. This tutorial covers some common cloud security and privacy threats and the relevant research, while focusing on the works that protect data confidentiality and query access privacy for sensitive data being stored and queried in the cloud. We provide a comprehensive study of state-of-the-art schemes and techniques for protecting data confidentiality and access privacy, which make different tradeoffs in the multidimensional space of security, privacy, functionality and performance.

As Internet-based services and mobile computing devices, such as smartphones and tablets, become ubiquitous, society's reliance on them to accomplish critical and time-sensitive tasks, such as information dissemination and collaborative decision making, also increases. Dependence on these media magnifies the damage caused by their disruption, whether malicious or natural. For instance, a natural disaster disrupting cellular and Internet infrastructures impedes information spread, which in turn leads to chaos, both among the victims as well as the aid providers. Decentralized and ad-hoc mechanisms for information dissemination and decision making are paramount to help restore order. We demonstrate InfoPuzzle, a mobile peer-to-peer database that utilizes direct device communication to enable group decision making, or consensus, without reliance on centralized communication services. InfoPuzzle minimizes the system's resource consumption, to prolong the lifetime of the power constrained devices by minimizing communication overhead, computational complexity, and persistent storage size. Due to user mobility and the limited range of point-to-point communication, knowing the exact number of participants is impossible, and therefore traditional consensus or quorum protocols cannot be used. We rely of distinct counting techniques, probabilistic thresholds, and bounded time based approaches to reach agreement. In this demo, we will explore various challenges and heuristics in estimating group participation to aid users in reconciling consensus without centralized services.

We present a framework for concurrency control and availability in multi-datacenter datastores. While we consider Google's Megastore as our motivating example, we define general abstractions for key components, making our solution extensible to any system that satisfies the abstraction properties. We first develop and analyze a transaction management and replication protocol based on a straightforward implementation of the Paxos algorithm. Our investigation reveals that this protocol acts as a concurrency prevention mechanism rather than a concurrency control mechanism. We then propose an enhanced protocol called Paxos with Combination and Promotion (Paxos-CP) that provides true transaction concurrency while requiring the same per instance message complexity as the basic Paxos protocol. Finally, we compare the performance of Paxos and Paxos-CP in a multi-datacenter experimental study, and we demonstrate that Paxos-CP results in significantly fewer aborted transactions than basic Paxos.

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight anonymization is also gaining importance. We present Anónimos, a Linear Programming-based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply Anónimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that Anónimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective anonymization of the sensitive edge-weights. We also demonstrate the composability of different models generated using Anónimos, a property that allows a single anonymized graph to preserve multiple linear properties.

Information diffusion in social networks provide great opportunities for political and social change as well as societal education. Therefore understanding information diffusion in social networks is a critical research goal. This greater understanding can be achieved through data analysis, development of reliable models that can predict outcomes of social processes, and ultimately the creation of applications that can shape the outcome of these processes. In this tutorial, we aim to provide an overview of such recent research based on a wide variety of techniques such as optimization algorithms, data mining, data streams covering a large number of problems such as influence spread maximization, misinformation limitation and study of trends in online social networks.

Scalable database management systems (DBMS)---both for update intensive application workloads as well as decision support systems for descriptive and deep analytics---are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (ii) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-the-art in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.

The ubiquity of location enabled devices has resulted in a wide proliferation of location based applications and services. To handle the growing scale, database management systems driving such location based services (LBS) must cope with high insert rates for location updates of millions of devices, while supporting efficient real-time analysis on latest location. Traditional DBMSs, equipped with multi-dimensional index structures, can efficiently handle spatio-temporal data. However, popular open source relational database systems are overwhelmed by the high insertion rates, real-time querying requirements, and terabytes of data that these systems must handle. On the other hand, Key-value stores can effectively support large scale operation, but do not natively support multi-attribute accesses needed to support the rich querying functionality essential for the LBSs. We present MD-HBase, a scalable data management system for LBSs that bridges this gap between scale and functionality. Our approach leverages a multi-dimensional index structure layered over a Key-value store. The underlying Key-value store allows the system to sustain high insert throughput and large data volumes, while ensuring fault-tolerance, and high availability. On the other hand, the index layer allows efficient multi-dimensional query processing. We present the design of MD-HBase that builds two standard index structuresâ€"the K-d tree and the Quad treeâ€"over a range partitioned Key-value store. Our prototype implementation using HBase, a standard open-source Key-value store, can handle hundreds of thousands of inserts per second using a modest 16 node cluster, while efficiently processing multidimensional range queries and nearest neighbor queries in real-time with response times as low as hundreds of milliseconds.

Multitenant data infrastructures for large cloud platforms hosting hundreds of thousands of applications face the challenge of serving applications characterized by small data footprint and unpredictable load patterns. When such a platform is built on an elastic pay-per-use infrastructure, an added challenge is to minimize the system's operating cost while guaranteeing the tenants' service level agreements (SLA). Elastic load balancing is therefore an important feature to enable scale-up during high load while scaling down when the load is low. Live migration, a technique to migrate tenants with minimal service interruption and no downtime, is critical to allow lightweight elastic scaling. We focus on the problem of live migration in the database layer. We propose Zephyr, a technique to efficiently migrate a live database in a shared nothing transactional database architecture. Zephyr uses phases of on-demand pull and asynchronous push of data, requires minimal synchronization, results no service unavailability and few or no aborted transactions, minimizes the data transfer overhead, provides ACID guarantees during migration, and ensures correctness in the presence of failures. We outline a prototype implementation using an open source relational database engine and an present a thorough evaluation using various transactional workloads. Zephyr's efficiency is evident from the few tens of failed operations, 10-20% change in average transaction latency, minimal messaging, and no overhead during normal operation when migrating a live database.

In this work, we study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or "good") campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. We show that this optimization problem is NP-hard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. We experimentally compare the performance of the greedy method to various heuristics. The experiments reveal that in most cases inexpensive heuristics such as degree centrality compare well with the greedy approach. We also study the influence limitation problem in the presence of missing data where the current states of nodes in the network are only known with a certain probability and show that prediction in this setting is a supermodular problem. We propose a prediction algorithm that is based on generating random spanning trees and evaluate the performance of this approach. The experiments reveal that using the prediction algorithm, we are able to tolerate about 90% missing data before the performance of the algorithm starts degrading and even with large amounts of missing data the performance degrades only to 75% of the performance that would be achieved with complete data.

The identification of popular and important topics discussed in social networks is crucial for a better understanding of societal concerns. It is also useful for users to stay on top of trends without having to sift through vast amounts of shared information. Trend detection methods introduced so far have not used the network topology and has thus not been able to distinguish viral topics from topics that are diffused mostly through the news media. To address this gap, we propose two novel structural trend definitions we call coordinated and uncoordinated trends that use friendship information to identify topics that are discussed among clustered and distributed users respectively. Our analyses and experiments show that structural trends are significantly different from traditional trends and provide new insights into the way people share information online. We also propose a sampling technique for structural trend detection and prove that the solution yields in a gain in efficiency and is within an acceptable error bound. Experiments performed on a Twitter data set of 41.7 million nodes and 417 million posts show that even with a sampling rate of 0.005, the average precision is 0.93 for coordinated trends and 1 for uncoordinated trends.

Database systems serving cloud platforms must serve large numbers of applications (or tenants ). In addition to managing tenants with small data footprints, different schemas, and variable load patterns, such multitenant data platforms must minimize their operating costs by efficient resource sharing. When deployed over a pay-per-use infrastructure, elastic scaling and load balancing, enabled by low cost live migration of tenant databases, is critical to tolerate load variations while minimizing operating cost. However, existing databases---relational databases and Key-Value stores alike---lack low cost live migration techniques, thus resulting in heavy performance impact during elastic scaling. We present Albatross , a technique for live migration in a multitenant database serving OLTP style workloads where the persistent database image is stored in a network attached storage. Albatross migrates the database cache and the state of active transactions to ensure minimal impact on transaction execution while allowing transactions active during migration to continue execution. It also guarantees serializability while ensuring correctness during failures. Our evaluation using two OLTP benchmarks shows that Albatross can migrate a live tenant database with no aborted transactions, negligible impact on transaction latency and throughput both during and after migration, and an unavailability window as low as 300 ms.

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Although such analysis can facilitate better understanding of sociological, behavioral, and other interesting phenomena, there is a growing concern about personal privacy being breached, thereby requiring effective anonymization techniques. In this paper, we consider edge weight anonymization in social graphs. Our approach builds a linear programming (LP) model which preserves properties of the graph that are expressible as linear functions of the edge weights. Such properties form the foundations of many important graph-theoretic algorithms such as shortest paths, k-nearest neighbors, minimum spanning tree, etc. Off-the-shelf LP solvers can then be used to find solutions to the resulting model where the computed solution constitutes the weights in the anonymized graph. As a proof of concept, we choose the shortest paths problem, and experimentally evaluate the proposed techniques using real social network data sets.

Cloud computing is an extremely successful paradigm of service oriented computing and has revolutionized the way computing infrastructure is abstracted and used. Three most popular cloud paradigms include: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The concept however can also be extended to Database as a Service and many more. Elasticity, pay-per-use, low upfront investment, low time to market , and transfer of risks are some of the major enabling features that make cloud computing a ubiquitous paradigm for deploying novel applications which were not economically feasible in a traditional enterprise infrastructure settings. This has seen a proliferation in the number of applications which leverage various cloud platforms, resulting in a tremendous increase in the scale of the data generated as well as consumed by such applications. Scalable database management systems (DBMS) -- both for update intensive application workloads, as well as decision support systems for descriptive and deep analytics -- are thus a critical part of cloud infrastructures.

Data outsourcing or database as a service is a new paradigm for data management in which a third party service provider hosts a database as a service. The service provides data management for its customers and thus obviates the need for the service user to purchase expensive hardware and software, deal with software upgrades and hire professionals for administrative and maintenance tasks. Since using an external database service promises reliable data storage at a low cost it is very attractive for companies. Such a service would also provide universal access, through the Internet to private data storedat reliable and secure sites. A client would store their data, and not need to carry their data with them as they travel. They would also not need to log remotely to their home machines, which may suffer from crashes and be unavailable. However, recent governmental legislations, competition among companies, and database thefts mandate companies to use secure and privacy preserving data management techniques. The data provider, therefore, needs to guarantee that the data is secure, be able to execute queries on the data, and the results of the queries must also be secure and not visible to the data provider. Current research has been focused only on how to index and query encrypted data. However, querying encrypted data is computationally very expensive. \emph{Providing an efficient trust mechanism} to push both database service providers and clients to behave honestly has emerged as one of the most important problem before data outsourcing to become a viable paradigm. In this paper, we describe scalable privacy preserving algorithms for data outsourcing. Instead of encryption, which is computationally expensive, we use distribution on multiple data provider sites and information theoretically proven secret sharing algorithms as the basis for privacy preserving outsourcing. The technical contributions of this paper is the establishment and development of a framework for efficient fault-tolerant scalable and theoretically secure privacy preserving data outsourcing that supports a diversity of database operations executed on different types of data, which can even leverage publicly available data sets.

Frequency counting, frequent elements and top-k queries form a class of operators that are used for a wide range of stream analysis applications. In spite of the abundance of these algorithms, all known techniques for answering data stream queries are sequential in nature. The imminent ubiquity of Chip Multi-Processor (CMP) architectures requires algorithms that can exploit the parallelism of such architectures. In this paper, we first evaluate different naive techniques for intra-operator parallelism, and summarize the insights obtained from the naive techniques. Our experimental analysis of the naive designs shows that intra-operator parallelism is not straightforward and requires a complete redesign of the system. We then propose an efficient and scalable framework for parallelizing frequency counting, frequent elements and top-k queries over data streams. The proposed CoTS (Co-operative Thread Scheduling) framework is based on the principle of threads co-operating rather than contending. Our experiments on a state-of-the-art quad-core chip multiprocessor architecture and synthetic data sets demonstrate the scalability of the proposed framework, and the efficiency is demonstrated by peak processing throughput of more than 60 million elements per second.

Many real-world data stream analysis applications such as network monitoring, click stream analysis , and others require combining multiple streams of data arriving from multiple sources. This is referred to as multi-stream analysis . To deal with high stream arrival rates, it is desirable that such systems be capable of supporting very high processing throughput. The advent of multicore processors and powerful servers driven by these processors calls for efficient parallel designs that can effectively utilize the parallelism of the multicores, since performance improvement is possible only through effective parallelism. In this paper, we address the problem of parallelizing multi-stream analysis in the context of multicore processors. Specifically, we concentrate on parallelizing frequent elements, top- k , and frequency counting over multiple streams. We discuss the challenges in designing an efficient parallel system for multi-stream processing. Our evaluation and analysis reveals that traditional "contention" based locking results in excessive overhead and wait, which in turn leads to severe performance degradation in modern multicore architectures. Based on our analysis, we propose a "cooperation" based locking paradigm for efficient parallelization of frequency counting. The proposed "cooperation" based paradigm removes waits associated with synchronization, and allows replacing locks by much cheaper atomic synchronization primitives. Our implementation of the proposed paradigm to parallelize a well known frequency counting algorithm shows the benefits of the proposed "cooperation" based locking paradigm when compared to the traditional "contention" based locking paradigm. In our experiments, the proposed "cooperation" based design outperforms the traditional "contention" based design by a factor of 2--5.5X for synthetic zipfian data sets.

Frequent elements and top-k queries constitute an important class of queries for data stream analysis applications. Certain applications require answers for both frequent elements and top-k queries on the same stream. In addition, the ever increasing data rates call for providing fast answers to the queries, and researchers have been looking towards exploiting specialized hardware for this purpose. Content Addressable Memory(CAM) provides an efficient way of looking up elements and hence are well suited for the class of algorithms that involve lookups. In this paper, we present a fast and efficient CAM conscious integrated solution for answering both frequent elements and top-k queries on the same stream. We call our scheme CAM conscious Space Saving with Stream Summary (CSSwSS), and it can efficiently answer continuous queries. We provide an implementation of the proposed scheme using commodity CAM chips, and the experimental evaluation demonstrates that not only does the proposed scheme outperforms existing CAM conscious techniques by an order of magnitude at query loads of about 10%, but the proposed scheme can also efficiently answer continuous queries.

Structured P2P systems have been developed for constructing applications at internet scale in cooperative environments and exhibit a number of desirable features such as scalability and self-maintenance. We argue that such systems when augmented with well defined consistency semantics provide an attractive building block for many large scale data processing applications in cluster environments. Towards this end, we study the problem of providing transactional semantics to P-Ring a P2P system which supports efficient range queries. We first extend a commonly used replication protocol in P2P systems to provide well defined guarantees in the presence of concurrent updates and under well defined failure assumptions. A multi-version concurrency control protocol called LSTP which leverages the guarantees of the replication protocol to provide transactional semantics is proposed. LSTP is designed to provide useful consistency semantics over P-Ring for read intensive workloads without sacrificing the scalability and other desirable properties inherent to the system. Under LSTP, read-only transactions are abort-free and non-blocking and the index stores no state for such transactions. We show that LSTP ensures no missed dependencies between transactions and guarantees basic consistency for read-only transactions when update transactions are serializable. The design of LSTP and its provable properties is a proof of concept that P2P systems can be augmented with transactional semantics. Results from a preliminary simulation study are also presented.

Estimating the number of distinct elements in a large multiset has several applications, and hence has attracted active research in the past two decades. Several sampling and sketching algorithms have been proposed to accurately solve this problem. The goal of the literature has always been to estimate the number of distinct elements while using minimal resources. However, in some modern applications, the accuracy of the estimate is of primal importance, and businesses are willing to trade more resources for better accuracy. Throughout our experience with building a distinct count system at a major search engine, Ask.com, we reviewed the literature of approximating distinct counts, and compared most algorithms in the literature. We deduced that Linear Counting, one of the least used algorithms, has unique and impressive advantages when the accuracy of the distinct count is critical to the business. For other estimators to attain comparable accuracy, they need more space than Linear Counting. We have supported our analytical results through comprehensive experiments. The experimental results highly favor Linear Counting when the number of distinct elements is large and the error tolerance is low.

By embedding sensors in mobile devices, it is possible to exploit the ubiquitous presence of these devices to construct applications for large-scale sensing and monitoring of environmental phenomena. To this end, we present Environmental Tomography, a novel approach in which mobile devices participate in the collection of aggregate sensor readings along roads or sidewalks, and these aggregates are used to reconstruct an estimate of the contaminant distribution throughout a region. We demonstrate how our data collection process preserves user location privacy and is robust to sensor and location reading errors. We also show how the estimation process can be formulated as a convex optimization problem that incorporates the physical dynamics of the phenomenon of interest. We study the performance of Environmental Tomography using various road network layouts and realistic models of pollution. Results indicate that estimates generated from path aggregates are of comparable accuracy to estimates generated from significantly greater numbers of individual sensor readings.

Aggregation is among the core functionalities of OLAP systems. Frequently, such queries are issued in decision support systems to identify interesting groups of data. When more than one aggregation function is involved and the notion of interest is not clearly defined, skyline queries provide a robust mechanism to capture the potentially interesting points where (i) users do not need to specify a ranking function and (ii) the result is independent of the dimension scales. To provide better exploration functionalities in OLAP systems, we propose to use skyline queries over aggregated data to identify the most interesting groups. Since aggregation functions have to be ad-hoc to cover a wide variety of user interests, the skyline over the aggregates has to be computed on the fly. Hence any algorithm to compute such a skyline must be fast and be able to progressively produce the result set with potential skyline groups being produced as early as possible. We explore a family of algorithms which try to consume only as many data records as are necessary to compute the skyline and design an optimal algorithm. We further refine the algorithm by taking into account systems issues such as disk behavior which are often ignored but have strong impact on real system performance. Experimental results validate the performance and progressive benefits of our algorithm.

The ubiquitous nature of mobile phones, which are location-aware devices, presents a unique platform for large-scale computing applications. In particular, if mobile phones are coupled with sensors, they can be used for detection and monitoring of environmental phenomena such as pollution and radiation. In this demonstration, we present environmental tomography, a system for ubiquitous environmental sensing with mobile devices. Aggregate sensor measurements are collected by the devices along fixed paths such as roads, and these aggregates are used to reconstruct an estimate of the distribution of the underlying physical phenomenon. Our system is robust to the dynamic characteristics of mobile networks and also preserves the privacy of mobile user locations. We demonstrate a prototype that generates estimate distributions from user specified data collection paths and underlying data distributions. The accuracy of the reconstructed distributions is illustrated both numerically and graphically.

Several data management challenges arise in the context of Internet advertising networks, where Internet advertisers pay Internet publishers to display advertisements on their Web sites and drive traffic to the advertisers from surfers' clicks. Although advertisers can target appropriate market segments, the model allows dishonest publishers to defraud the advertisers by simulating fake traffic to their own sites to claim more revenue. This paper addresses the case of publishers launching fraud attacks from numerous machines, which is the most widespread scenario. The difficulty of uncovering these attacks is proportional to the number of machines and resources exploited by the fraudsters. In general, detecting this class of fraud entails solving a new data mining problem, which is finding correlations in multidimensional data. Since the dimensions have large cardinalities, the search space is huge, which has long allowed dishonest publishers to inflate their traffic, and deplete the advertisers' advertising budgets. We devise the approximate SLEUTH algorithms to solve the problem efficiently, and uncover single-publisher frauds. We demonstrate the effectiveness of SLEUTH both analytically and by reporting some of its results on the Fastclick network, where numerous fraudsters were discovered.

Recently, there has been significant interest in developing space and time efficient solutions for answering continuous summarization queries over data streams. While these techniques are evaluated in a standard CPU setting, many of their applications such as click-fraud detection, and network-traffic summarization typically execute on special networking architectures called Network Processing Units (NPUs). These NPUs interface with special kind of associative memories known as the Ternary Content Addressable Memories (TCAMs). In this paper, we describe how the integrated architecture of NPU and TCAMs can be exploited towards achieving the goal of developing high-speed stream summarization solutions. We analyze popular solutions for the frequent elements problem in data stream, discuss the bottleneck issues and motivate how TCAMs can help alleviate these bottlenecks. A preliminary evaluation on an NPU platform reveals the performance gains of the TCAM-conscious techniques over software implementations.

This paper addresses the problem of efficient maintenance of a materialized skyline view in response to skyline removals. While there has been significant progress on skyline query computation, an equally important but largely unanswered issue is on the incremental maintenance for skyline deletions. Previous work suggested the use of the so called exclusive dominance region (EDR) to achieve optimal I/O performance for deletion maintenance. However, the shape of an EDR becomes extremely complex in higher dimensions, and algorithms for its computation have not been developed. We derive a systematic way to decompose a d-dimensional EDR into a collection of hyper-rectangles. We show that the number of such hyper-rectangles is O(md), where m is the current skyline result size. We then propose a novel algorithm DeltaSky which determines whether an intermediate R-tree MBR intersects with the EDR without explicitly calculating the EDR itself. This reduces the worse case complexity of the EDR intersection check from O(md) to O(md). Thus DeltaSky helps the branch and bound skyline algorithm achieve I/O optimality for deletion maintenance by finding only the newly appeared skyline points after the deletion. We discuss implementation issues and show that DeltaSky can be efficiently implemented using one extra B-Tree. Moreover, we propose two optimization techniques which further reduce the average cost in practice. Extensive experiments demonstrate that DeltaSky achieves orders of magnitude performance gain over alternative solutions.

The primary goal of data stream research is to develop space and time efficient solutions for answering continuous on-line summarization queries. Research efforts over the last decade have resulted in a number of efficient algorithms with varying degrees of space and time complexities. While these techniques are developed in a standard CPU setting, many of their applications such as click-fraud detection and network-traffic summarization typically execute on special networking architectures called Network Processing Units (NPUs). These NPUs interface with special associative memories known as Ternary Content Addressable Memories (TCAMs) to provide gigabit rate forwarding at network routers. In this paper, we describe how the integrated architecture of NPU and TCAMs can be exploited towards achieving the goal of developing high-speed stream summarization solutions. We propose two TCAM-conscious solutions for the frequent elements problem in data streams and present a comprehensive evaluation of these techniques on a state-of-the-art networking platform.

Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach.

Data integration from multiple autonomous data sources has emerged as an important practical problem. The key requirement for such data integration is that owners of such data need to cooperate in a competitive landscape in most of the cases. The research challenge in developing a query processing solution is that the answers to the queries need to be provided while preserving the privacy of the data sources. In general, allowing unrestricted read access to the whole data may give rise to potential vulnerabilities as well as may have legal implications. Therefore, there is a need for privacy preserving database operations for querying data residing at different parties. In this paper, we propose a new query processing technique using third parties in a peer-to-peer system. We propose and evaluate two different protocols for various database operations. Our scheme is able to answer queries without revealing any useful information to the data sources or to the third parties. Analytical comparison of the proposed approach with other recent proposals for privacy-preserving data integration establishes the superiority of the proposed approach in terms of query response times.

Automated storage management is critical for most dataintensive applications running on DBMSs. In large-scale storage subsystems, the workload is expected to vary with time. In order to ensure both QoS and efficient usage of storage resources, variation in the actual physical disks is allowed to support a single virtual disk. Such data migration generates extra IOs and consumes storage resources. Not only does data migration need to be scheduled ahead but it must also be scheduled in such a way that QoS violations do not occur because of the extra migration IOs. In this paper, we present a novel analytic framework, PULSTORE, for autonomically managing the storage to provide performance guarantee during migration.

An XML range query may impose predicates on the numerical or textual contents of the elements and/or their respective path structures. In order to handle content and structure range queries efficiently, an XML query processing engine needs to incorporate effective indexing and summarization techniques to efficiently partition the XML document and locate the results. In this paper, we propose a dynamic summarization and indexing method, FLUX, based on Bloom filters and B+-trees to tackle these problems. The results of our extensive experimental evaluations indicated the efficiency of the proposed system.

Data allocation on multiple I/O devices manifests itself in many computing systems, both centralized and distributed. Data is partitioned on multiple I/O devices and clients issue various types of queries to retrieve relevant information. In this paper, we derive necessary and sufficient conditions for a data allocation method to be optimal for two important types of queries: partial match and bounded disagreement search queries. We formally define these query types and derive the optimality conditions based on coding-theoretic arguments. Although these conditions are fairly strict, we show how to construct good allocation methods for practical realistic situations. Not only are the response times bounded by a small value, but also the identification of the relevant answer set is efficient

We propose an approximate integrated approach for solving both problems of finding the most popular k elements, and finding frequent elements in a data stream coming from a large domain. Our solution is space efficient and reports both frequent and top- k elements with tight guarantees on errors. For general data distributions, our top- k algorithm returns k elements that have roughly the highest frequencies; and it uses limited space for calculating frequent elements. For realistic Zipfian data, the space requirement of the proposed algorithm for solving the exact frequent elements problem decreases dramatically with the parameter of the distribution; and for top- k queries, the analysis ensures that only the top- k elements, in the correct order, are reported. The experiments, using real and synthetic data sets, show space reductions with hardly any loss in accuracy. Having proved the effectiveness of the proposed approach through both analysis and experiments, we extend it to be able to answer continuous queries about frequent and top- k elements. Although the problems of incremental reporting of frequent and top- k elements are useful in many applications, to the best of our knowledge, no solution has been proposed.

Top-k preference queries with multiple attributes are critical for decision-making applications. Previous research has concentrated on improving the computational efficiency mainly by using novel index structures and search strategies. Since current applications need to scale to terabytes of data and thousands of users, performance of such systems is strongly impacted by the amount of available memory. This paper proposes a scalable approach for memory-bounded top-k query processing.

The demands on storage systems are increasingly requiring expressiveness, fault-tolerance, security, distribution, etc. Such functionalities have been traditionally provided by DBMS. We propose a storage management system, SVL that leverages DBMS technology. The primary problem in block storage management is block virtualization, which is essentially an abstraction layer that separates the user view of storage from the implementation of storage. Storage virtualization standardizes storage management in a heterogeneous storage and/or host environment, and plays a crucial role in enhancing storage functionality and utilization. Currently specialized hardware or microcode-based solutions are popular for implementing block storage management systems, commonly referred to as disk controllers. We demonstrate how to take a general purpose commercial RDBMS, rather than a specialized solution, to support block storage management. We exploit the simple semantics of storage management systems to streamline database performance and thus attain acceptability from a storage point of view. This work promises to pave the way for diverse and innovative industrial applications of database management systems.

We consider the problem of finding duplicates in data streams. Duplicate detection in data streams is utilized in various applications including fraud detection. We develop a solution based on Bloom Filters [9], and discuss the space and time requirements for running the proposed algorithm in both the contexts of sliding, and landmark stream windows. We run a comprehensive set of experiments, using both real and synthetic click streams, to evaluate the performance of the proposed solution. The results demonstrate that the proposed solution yields extremely low error rates.

A novel approach for similarity search on protein structure databases is proposed which incorporates the three dimensional coordinates of the main atoms of each amino acid and extracts a geometrical signature along with the direction of the given amino acid. As a result, each protein is presented by a series of feature vectors representing local geometry, shape, direction, and secondary structure assignment of its amino acid constituents. Furthermore, a residue-to-residue distance matrix is calculated and is incorporated into a local alignment dynamic programming algorithm to find the similar portions of two given proteins and finally a sequence alignment step is used as the last filtration step. The optimal superimposition of the detected similar regions is used to assess the quality of the results. The proposed algorithm is fast and accurate and hence could be used for the analysis of large protein structure similarity.

Data cube computation and representation are prohibitively expensive in terms of time and space. Prior work has focused on either reducing the computation time or condensing the representation of a data cube. We introduce range cubing as an efficient way to compute and compress the data cube without any loss of precision. A new data structure, range trie, is used to compress and identify correlation in attribute values, and compress the input dataset to effectively reduce the computational cost. The range cubing algorithm generates a compressed cube, called range cube, which partitions all cells into disjoint ranges. Each range represents a subset of cells with the same aggregation value, as a tuple which has the same number of dimensions as the input data tuples. The range cube preserves the roll-up/drill-down semantics of a data cube. Compared to H-cubing, experiments on real dataset show a running time of less than one thirtieth, still generating a range cube of less than one ninth of the space of the full cube, when both algorithms run in their preferred dimension orders. On synthetic data, range cubing demonstrates much better scalability, as well as higher adaptiveness to both data sparsity and skew.

Peer-to-peer systems are mainly used for object sharing although they can provide the infrastructure for many other applications. We extend the idea of object sharing to data sharing on a peer-to-peer system. We propose a method, which is based on the multidimensional CAN system, for efficiently evaluating range queries. The answers of the range queries are cached at the peers and are used to answer future range queries. The scalability and efficiency of our design is shown through simulation.

Spatial database operations are typically performed in two steps. In the filtering step, indexes and the minimum bounding rectangles (MBRs) of the objects are used to quickly determine a set of candidate objects, and in the refinement step, the actual geometries of the objects are retrieved and compared to the query geometry or each other. Because of the complexity of the computational geometry algorithms involved, the CPU cost of the refinement step is usually the dominant cost of the operation for complex geometries such as polygons. In this paper, we propose a novel approach to address this problem using efficient rendering and searching capabilities of modern graphics hardware. This approach does not require expensive pre-processing of the data or changes to existing storage and index structures, and it applies to both intersection and distance predicates. Our experiments with real world datasets show that by combining hardware and software methods, the overall computational cost can be reduced substantially for both spatial selections and joins.

Although traditional databases and data warehouses have been exploited widely to manage persistent data, a large number of applications from sensor network need functional supports for transient data in the continuous data stream. One of the crucial functions is to summarize the data items within a sliding window. A sliding window contains a fixed width span of data elements. The data items are implicitly deleted from the sliding window, when it moves out of the window scope. Several one-dimensional histograms have been proposed to store the succinct time information in a sliding window. Such histograms, however, only handle the data items with attribute values in unary domains. In this paper, we explore the problem of extending the value to a multi-valued domain. A two-dimensional histogram, the hybrid histogram, is proposed to support sliding window queries on a practical multi-valued domain. The basic building block of the hybrid histogram is the exponential histogram. The hybrid histogram is maintained to capture the changes of data distribution. To further compress the exponential histograms, we propose a condensed exponential histogram without losing the error bound. Results of an extensive experimental study are included to evaluate the benefits of the proposed technique.

We present a family of epidemic algorithms for maintaining replicated database systems. The algorithms are based on the causal delivery of log records where each record corresponds to one transaction instead of one operation. The first algorithm in this family is a pessimistic protocol that ensures serializability and guarantees strict executions. Since we expect the epidemic algorithms to be used in environments with low probability of conflicts among transactions, we develop a variant of the pessimistic algorithm which is optimistic in that transactions commit as soon as they terminate locally and inconsistencies are detected asynchronously as the effects of committed transactions propagate through the system. The last member of the family of epidemic algorithms is pessimistic and uses voting with quorums to resolve conflicts and improve transaction response time. A simulation study evaluates the performance of the protocols.

Maintaining approximate aggregates and summaries over data streams is crucial to handle the OLAP query workload that arises in applications, such as network monitoring and telecommunications. Furthermore, since the entire data is not available at all times the maintenance task must be done incrementally. We show that R(elaxed)Hist(ogram) is an appropriate summarization under data stream scenario. In order to reduce query estimation errors, we propose adaptive approaches which not only capture the data distribution, but also integrate independent query patterns. We introduce a workload decay model to efficiently capture global workload information and ensure that the query patterns from the recent past are weighted more than queries that are further in the past. We verify experimentally that our approach successfully adapts to continuously changing workload as well as data streams.

With the advances in wireless communications and mobile device technologies, location-based applications or services will become an essential part of future applications. We have developed a location-based application called NAPA (Nearest Available Parking lot Application) that assists users to find the nearest parking space on campus. NAPA is an example of an application which combines a number of new features, such as location-based, wireless communication and a directory service like LDAP (Lightweight Directory Access Protocol).

Queries with common sequences of disk accesses can make maximal use of a buffer pool. We developed middleware to promote the necessary conditions in concurrent query streams, and achieved a speedup of 2.99 in executing a workload derived from the TCP-H benchmark.

As online spatial datasets grow both in number and sophistication, it becomes increasingly difficult for users to decide whether a dataset is suitable for their tasks, especially when they do not have prior knowledge of the dataset. The GeoBrowsing service developed for the ADL project provides users an effective and efficient way to explore the content of a spatial dataset. In this paper, we identify a set of spatial relations that need to be supported in browsing applications, namely, the contains, contained and the overlap relations. We prove a storage lower bound to answer queries about the contains relation accurately at a given grid resolution. We then present three storage-efficient approximation algorithms which we believe to be the first to estimate query selectivities about these spatial relations. Experimental results show that these algorithms provide highly accurate estimates in real time for a wide range of datasets with various characteristics.

The ability to provide accurate and efficient result estimations of user queries is very important for the query optimizer in database systems. In this paper, we show that the traditional estimation techniques with data reduction points of view do not produce satisfiable estimation results if the query patterns are dynamically changing. We further show that to reduce query estimation error, instead of accurately capturing the data distribution, it is more effective to capture the user query patterns. In this paper, we propose query estimation techniques that can adapt to user query patterns for more accurate estimates of the size of selection or range queries over databases.

With the growing popularity of mobile computing devices and wireless communications, managing dynamically changing information about moving objects is becoming feasible. In this paper we implement a system that manages such information and propose an efficient range query algorithm with a filtering step which efficiently determines if a polyline corresponding to the trajectory of a moving object intersects with a given range. We study the performance of the system, which shows that despite the filtering step, for moderately large ranges, the range query algorithm we propose outperforms the algorithm without filtering.

Data warehouses support the analysis of historical data. This often involves aggregation over a period of time. Furthermore, data is typically incorporated in the warehouse in the increasing order of a time attribute, e.g., date of sale or time of a temperature measurement. In this paper we propose a framework to take advantage of this append only nature of updates due to a time attribute. The framework allows us to integrate large amounts of new data into the warehouse and generate historical summaries efficiently. Query and update costs are virtually independent from the extent of the data set in the time dimension, making our framework an attractive aggregation approach for append-only data streams. A specific instantiation of the general approach is developed for MOLAP data cubes, involving a new data structure for append-only arrays with pre-aggregated values. Our framework is applicable to point data and data with extent, e.g., hyper-rectangles.

Conical queries are a novel type of query with an increasing number of applications. Traditional index structures and retrieval mechanisms, in general, have been optimized for rectangular and circular queries, rather than conical queries. In this paper, we focus on conical queries which can be defined as a multi-dimensional cone in a multi-dimensional data space. We develop a model for expressing such queries and suggest efficient techniques for evaluating them. In particular, we explore the retrieval problem in the context of conical query processing and develop multi-disk allocation methods specifically for processing conical queries.

Develops a general framework for approximate nearest-neighbor queries. We categorize the current approaches for nearest-neighbor query processing based on either their ability to reduce the data set that needs to be examined, or their ability to reduce the representation size of each data object. We first propose modifications to well-known techniques to support the progressive processing of approximate nearest-neighbor queries. A user may therefore stop the retrieval process once enough information has been returned. We then develop a new technique based on clustering that merges the benefits of the two general classes of approaches. Our cluster-based approach allows a user to progressively explore the approximate results with increasing accuracy. We propose a new metric for evaluation of approximate nearest-neighbor searching techniques. Using both the proposed and the traditional metrics, we analyze and compare several techniques with a detailed performance evaluation. We demonstrate the feasibility and efficiency of approximate nearest-neighbor searching. We perform experiments on several real data sets and establish the superiority of the proposed cluster-based technique over the existing techniques for approximate nearest-neighbor searching.

Query size estimation is crucial for many database system components. In particular, query optimizers need efficient and accurate query size estimation when deciding among alternative query plans. In this paper we propose a novel sampling technique based on the golden rule of sampling, introduced by von Neumann in 1947, for estimating range queries. The proposed technique randomly samples the frequency domain using the cumulative frequency distribution and yields good estimates without any a priori knowledge of the actual underlying distribution of spatial objects. We show experimentally that the proposed sampling technique gives smaller approximation error than the Min-Skew histogram based and wavelet based approaches for both synthetic and real datasets. Moreover, the proposed technique can be easily extended for higher dimensional datasets.

With the proliferation of multimedia data, there is increasing need to support the indexing and searching of high dimensional data. Recently, a vector approximation based technique called VA-file has been proposed for indexing high dimensional data. It has been shown that the VA-file is an effective technique compared to the current approaches based on space and data partitioning. The VA-file gives good performance especially when the data set is uniformly distributed. Real data sets are not uniformly distributed, are often clustered, and the dimensions of the feature vectors in real data sets are usually correlated. More careful analysis for nonuniform or correlated data is needed for effectively indexing high dimensional data. We propose a solution to these problems and propose the VA+-file, a new technique for indexing high dimensional data sets based on vector approximations. We conclude with an evaluation of nearest neighbor queries and show that the VA+-file technique res...

Data cubes are designed to pro vide aggregate information that can be used to analyze the contents of databases and data w arehouses.A range query applies an aggregation operation (e.g., SUM, A VERAGE) over all selected cells in a data cube, where the selection is speci ed by providing ranges of values on dierent dimensions.Previous approaches to process the range sum query on data cubes (e.g., pre x sum) have l o w query costs but have high update costs.We use the discrete wavelet transformation to decompose a data cube into w avelet coecien ts of dierent resolutions.The resulting decomposed data cube can be used for progressiv eand appro ximatequery processing.This new approach can handle clustered and sparse data gracefully and can provide ecient performance for both queries and updates, which m a k es the wavelet decomposition technique suitable for dynamic data cube environments.

Similarity search in time-series databases has received significant attention lately. Popular techniques for efficient retrieval of time sequences in time-series databases has been to use Discrete Fourier Transform (DFT). Recently, the Discrete Wavelet Transform (DWT) has gained popular interest in database domain and several proposals have been made to replace DFT by DWT for similarity search over time-series databases. In this paper, we explore the feasibility of replacing DFT by DWT with a comprehensive analysis of the DFT and DWT as matching functions in time-series databases. Our results show that although the DWT based technique has several advantages, e.g., the DWT has complexity of O(N) whereas DFT is O(N log N ), DWT does not reduce relative matching error and does not increase query precision in similarity search as suggested by previous works [1]. We conclude that, by exploring the conjugate property of DFT in real domain, the DFT-based and DWT-based techniques yiel...

Multidimensional data cubes are used in large data warehouses as a tool for online aggregation of information. As the number of dimensions increases, supporting efficient queries as well as updates to the data cube becomes difficult. Another problem that arises with increased dimensionality is the sparseness of the data space. In this paper we develop a new data structure referred to as the pCube (data cube for progressive querying), to support efficient querying and updating of multidimensional data cubes in large data warehouses. While the pCube concept is very general and can be applied to any type of query, we mainly focus on range queries that summarize the contents of regions of the data cube. pCube provides intermediate results with absolute error bounds (to allow trading accuracy for fast response time), efficient updates, scalability with increasing dimensionality, and pre-aggregation to support summarization of large ranges. We present both a general solution and an implementation of pCube and report the results of experimental evaluations.

Modern databases increasingly integrate new kinds of information, such as multimedia information in the form of image, video, and audio data. Both the dimensionality and the amount of data that need to be processed is increasing rapidly, increasing the demand for the efficient retrieval of large amounts of multi-dimensional data. Declustering techniques for multi-disk architectures have been effectively used for storage. In this paper, we first establish that besides exploiting the parallelism, a careful organization of each disk must be considered for fast searching. We introduce the notion of page allocation and data space mapping which can be used to organize and retrieve multidimensional data. We develop these notions based on three different partitioning strategies: regular grid partitioning, concentric hypercubes and hyperpyramids. We develop techniques that satisfy efficient retrieval by optimizing the number of buckets retrieved by the query, disk arm movement and I/O parallelism. We prove that concentric hypercube-based mapping satisfies the optimal clustering and optimal parallelism. We develop a technique based on hyperpyramid partitioning that reduces the number of buckets retrieved by the query and has efficient inter- and intra-disk organizations. We evaluate the performance of proposed techniques by comparing them with the current approaches. The new techniques lead to very significant improvement over the existing techniques, and result in fast retrieval of multi-dimensional data.

Summarization of intermediary query result sets plays an important role when users browse through digital library collections. Summarization enables users to quickly digest the results of their queries, and provides users with important information they can use to narrow their search interactively. Techniques from the field of data analysis may be applied to the problem of generating summaries of query results efficiently. Such techniques should permit the incorporation of classification hierarchies in order to provide powerful browsing environments for digital library users.

Data partitioning and declustering have been extensively used in the past to parallelize I/O for range queries. Numerous declustering and disk allocation techniques have been proposed in the literature. However most of these techniques were primarily designed for two-dimensional data and for balanced partitioning of the data space. As databases increasingly integrate multimedia information in the form of image, video, and audio data, it is necessary to extend the declustering techniques for multidimensional data. We first establish that traditional declustering techniques do not scale for high-dimensional data. We then propose several new partitioning schemes based on concentric hyperspaces. We then develop disk allocation methods for each of the proposed schemes. We conclude with an evaluation of range queries based on these schemes and show that partitioning based on concentric hyperspaces has a significant advantage over a balanced partitioning approach for parallel I/O.

Range sum queries on data cubes are a powerful tool for analysis. A range sum query applies an aggregation operation (e.g., SUM) over all selected cells in a data cube, where the selection is specified by providing ranges of values for numeric dimensions. Many application domains require that information provided by analysis tools be current or "near-current." Existing techniques for range sum queries on data cubes, however, can incur update costs on the order of the size of the data cube. Since the size of a data cube is exponential in the number of its dimensions, rebuilding the entire data cube can be very costly. We present an approach that achieves constant time range sum queries while constraining update costs. Our method reduces the overall complexity of the range sum problem.

Novel techniques based on the Discrete Fourier Transform are proposed to estimate the size of relations resulting from join operations. For the special case of self join the proposed algorithm gives the exact join size using logarithmic space. A generalization to compute the join of arbitrary relations is then used to develop two tree-based techniques that provide a spectrum of algorithms which interpolate storage requirements versus accuracy of the estimation obtained. Finally, we present experimental results to exhibit the effectiveness of our approach. 1

Various proposals have been made for declustering 2D tiled data on multiple I/O devices. Strictly optimal solutions only exist under very restrictive conditions on the tiling of the 2D space or for very few I/O devices. In this paper, we explore allocation methods where no strictly optimal solution exists. We propose a general class of allocation methods, referred to as cyclic allocation methods, and show that many existing methods are instances of this class. As a result, various seemingly ad hoc and unrelated methods are presented in a single framework. Furthermore, the framework is used to develop new allocation methods that give better performance than any previous method and that approach the best feasible performance.

Pharos is a .sca&zbledistributed architecture for locating heterogeneous informatdon sources.!7he system incorporates a hierarcfiical metaduta structure into a multi-level rettieval system.Queries are resolved through an iterative decisionmaking process.The first step retrieves coarse-grain metadata, about all sources, stored on iocal, mastively replicated, high-level servers.Further steps retrieve more detailed matadata, about a greatly reduced set of souwes, stored on remote, sparsety replicated, topic-based mid-level servers.We present results of a simulation which indicate the feasibility of the architecture.We describe the structure, distribution, and retrieval of the metadata in Pharos to enable users to locate desirable information soumes over the Internet.

Non-uniformity in data extents is a general characteristic of spatial data. Indexing such non-uniform data using conventional spatial index structures such as R/sup */-trees is inefficient for two reasons: (1) the non-uniformity increases the likelihood of overlapping index entries, and (2) clustering of non-uniform data is likely to index more dead space than clustering of uniform data. To reduce the impact of these anomalies, we propose a new scheme that promotes data objects to higher levels in tree-based index structures. We examine two criteria for promotion of data objects and evaluate their relative merits using an R*-tree. In experiments on cartographic data, we observe that our promotion criteria yield up to 45% improvement in query performance for an R*-tree.

We present incremental view maintenance algorithms for a data warehouse derived from multiple distributed autonomous data sources. We begin with a detailed framework for analyzing view maintenance algorithms for multiple data sources with concurrent updates. Earlier approaches for view maintenance in the presence of concurrent updates typically require two types of messages: one to compute the view change due to the initial update and the other to compensate the view change due to interfering concurrent updates. The algorithms developed in this paper instead perform the compensation locally by using the information that is already available at the data warehouse. The first algorithm, termed SWEEP, ensures complete consistency of the view at the data warehouse in the presence of concurrent updates. Previous algorithms for incremental view maintenance either required a quiescent state at the data warehouse or required an exponential number of messages in terms of the data sources. In contrast, this algorithm does not require that the data warehouse be in a quiescent state for incorporating the new views and also the message complexity is linear in the number of data sources. The second algorithm, termed Nested SWEEP, attempts to compute a composite view change for multiple updates that occur concurrently while maintaining strong consistency.

The Alexandria project has been initiated to build a digital library for map and satellite images.Designed for content-based retrieval, the relevant information in each image is encoded in the form of a multi-dimensional feature vector.Though representing images by feature vectors ~eatly facilitates user queries, indexing these vectors degrades performance when the number of dimensions is large.We consider 2 popular techniques (DFT and SVD) to reduce the dimension of feature vectors, and study their retrieval performance with respect to recall and precision.We End that though SVD generally out-performs DFT, DFT compares favorably in a limited range suitable for browsing large image databases.

In recent years, numerous transaction models have been proposed to address the problems posed by advanced database applications, but only a few of these models are being used in commercial products. In this paper, we make the case that such models may be too centered around databases to be useful in real environments. Advanced applications raise a variety of issues that are not addressed at all by transaction models. These same issues, however, are the basis for existing workflow systems, which are having considerable success as commercial products in spite of not having a solid theoretical foundation. We explore some of these issues and show that, in many aspects, workflow models are a superset of transaction models and have the added advantage of incorporating a variety of ideas that have so far remained outside the scope of traditional transaction processing.

Replicated data management protocols have been proposed that exploit a logically structured set of copies. These protocols have the advantage that they provide limited fault-tolerance at low communication cost. The proposed protocols can be viewed as analogues of the read-one write-all protocol in the context of logical structures. In this paper, we start by generalizing these protocols in two ways for logical structures. First, the quorum-based approach is applied to develop protocols that use structured read and write quorums, thus attaining a high degree of data availability for both read and write operations. Next, the reconfiguration or views approach is developed for these structures, resulting in protocols that attain high degrees of availability at significantly low communication cost for read operations. In this sense, the proposed protocols have the advantages of the read-one write-all protocol for low-cost read operations as well as the majority quorum protocol for high data availability. Finally, we generalize the reconfiguration approach to allow for the dynamic reconfiguration of the database system from one replica management protocol to another. This allows database systems to adapt to an evolving and dynamic application environment.

In the presence of semantic information, serializability is too strong a correctness criterion and unnecessarily restricts concurrency. We use the semantic information of a transaction to provide different atomicity views of the transaction to other transactions. The proposed approach improves concurrency and allows interleavings among transactions which are non-serializable, but which nonetheless preserve the consistency of the database and are acceptable to other users. We develop a graph-based tool whose acyclicity is both a necessary and sufficient condition for the correctness of an execution. Our theory encompasses earlier proposals that incorporate semantic information of transactions. Furthermore it is the first approach that provides an efficient graph based tool for recognizing correct schedules without imposing any restrictions on the application domain. Our approach is widely applicable to many advanced database applications such as systems with long-lived transactions and collaborative environments.

Serializability is the standard correctness criterion for concurrency control. To ensure correctness in the presence of failures, recoverability is also imposed. Pragmatic considerations result in further constraints, for instance, the existing log-based recovery implementations that use before-images warrant that transaction executions be strict. Strict executions are restrictive, thus sacrificing concurrency and throughput. In this paper we identify the relation between the recovery mechanism and the restrictions imposed by concurrency control protocols. In particular, we propose a new inverse operation that can be integrated with the underlying recovery mechanism. In order to establish the viability of our approach, we demonstrate the new implementation by making minor modifications to the conventional recovery architecture. This inverse operation is also designed to avoid the undesirable phenomenon of cascading aborts when transactions execute conflicting write operations.

The characteristics of geographic data and the nature of geographic research require the participation of many agents. Data is generated by multiple sources (satellites, ground observation, weather stations, photography, etc.), accessed, processed and transformed by many users and available for use to an even larger population of users. Lack of coordination among all these different agents may render large amounts of work useless. Most existing GIS (Geographic Information Systems) do not provide any support for cooperative work, which adds to the problem. To overcome this serious limitation while still allowing users to take advantage of GIS technology, we propose GOOSE, a system implemented as a top layer for existing GIS. GOOSE provides the tools for constructing large geographic models in a cooperative environment with potentially many users and participants.

There is growing evidence that for a wide variety of database workloads and system configurations, locking-based concurrency control outperforms other types of concurrency control strategies. However, in the presence of increased data contention, locking protocols such as two-phase locking perform poorly. In this paper, we analyze a family of locking-based protocols that employ a new relationship between locks called ordered sharing. Using a centralized database simulation model, we demonstrate that these protocols exhibit comparable performance to that of traditional locking-based protocols when data contention is low, and they exhibit superior performance when data contention is high. Furthermore, we show that the performance of these protocols improves as resources become more plentiful. This is particularly significant because the performance of two-phase locking degrades as a result of data contention, not resource contention. Thus, introducing additional resources improves the performance of the proposed protocols, though it does not benefit two-phase locking significantly.>

The problem of disk allocation addresses the issue of how to distribute a file on several disks in order to maximize concurrent disk accesses in response to a partial match query. In this paper a coding-theoretic analysis of this problem is presented, and both necessary and sufficient conditions for the existence of strictly optimal allocation methods are provided. Based on a class of optimal codes, known as maximum distance separable codes, strictly optimal allocation methods are constructed. Using the necessary conditions proved, we argue that the standard definition of strict optimality is too strong and cannot be attained, in general. Hence, we reconsider the definition of optimality. Instead of basing it on an abstract definition that may not be attainable, we propose a new definition based on the best possible allocation method. Using coding theory, allocation methods that are optimal according to our proposed criterion are developed.

The semantics of objects and transactions in database systems are investigated. User-defined predicates called consistency assertions are used to specify user programs. Three new correctness criteria are proposed. The first correctness criterion consistency is based solely on the users' specifications and admit nonserializable executions that are acceptable to the users. Integrity constraints of the database are maintained through consistency assertions. The second correctness criterion orderability is a generalization of view serializability and represents a weak notion of equivalence to a serial schedule. Finally, the third correctness criterion strong order-ability is introduced as a generalization of conflict serializability. Unlike consistency, the notions of orderability allow users to operate an isolation as maintenance of the integrity constrainst now becomes the responsibility of the database system.

The authors present a propagation mechanism, called the commit propagation mechanism (CPM), which increases the availability of data for read-only transactions. The proposed mechanism is piggy-backed on the messages used in the two-phase commit protocol. The CPM was combined with the standard quorum protocol in two different replicated database systems. In a fully replicated database, CPM allows any read-only transaction to execute locally at a single site without the need for any communication overhead. In a partially replicated database, CPM either ensures that the set of copies residing at a site are mutually consistent, or indicates which copies violate such consistency.>

The 'Half-

In this paper, we propose locking protocols that are useful for real-time databases. Our approach is motivated from two main observations. First, locking protocols are widely accepted and used in most database systems. Second, in real-time databases it has been shown that the blocking behavior of transactions in locking protocols results in performance degradation. We use a new relationship between locks called ordered sharing to eliminate blocking that arises in the traditional locking protocols. Ordered sharing eliminates blocking of read and write operations but may result in delayed commitment. Since in real-time databases, timeliness and not response time is the crucial factor, or protocols exploit this delay to allow transactions to execute within the slacks of delayed transactions. We compare the performance of the proposed protocols with the two phase locking protocol for real-time databases. Our experiments indicate that the propose protocols significantly reduce the percentage of missed deadlines in the system for a variety of workloads.

In this paper, we present a low-cost fault-tolerant protocol for managing replicated data. We impose a logical tree structure on the set of copies of an object and develop a protocol that uses the information available in the logical structure to reduce the communication requirements for read and write operations. The tree quorum protocol is a generalization of the static voting protocol with two degrees of freedom for choosing quorums. In general, this results in significantly lower communication costs for comparable data availability. The protocol exhibits the property of graceful degradation, i.e., communication costs for executing operations are minimal in a failure-free environment but may increase as failures occur. This approach in designing distributed systems is desirable since it provides fault-tolerance without imposing unnecessary costs on the failure-free mode of operations.

A family of locking-based protocols is analyzed that use a novel mode of locks called ordered sharing. Using a centralized database simulation model, it is demonstrated that these protocols exhibit comparable performance to that of traditional locking-based protocols when data contention is low and exhibit superior performance when data contention is high. It is shown that the performance of these protocols improves as physical resources become more plentiful. This is particularly significant since two-phase locking degrades due to data and not resource contention. Thus, introducing additional resources improves the performance of the proposed protocols while it does not benefit two-phase locking significantly.>

In this paper we present a coding-theoretic analysis of the disk allocation problem. We provide both necessary and sufficient conditions for the existence of strictly optimal allocation methods. Based on a class of optimal codes, known as maximum distance separable codes, strictly optimal allocation methods are constructed. Using the necessary conditions proved, we argue that the standard definition of strict optimality is too strong, and cannot be attained in general. A new criterion for optimality is therefore defined whose objective is to design allocation methods that yield a response time of one for all queries with a minimum number of specified attributes. Using coding theory, we determined this minimum number for binary files, assuming that the number of disks is a power of two. In general, our approach provides better allocation methods than previous techniques.

In this paper, we propose a new mode for locks that permits sharing in a constrained manner. We develop a family of locking protocols, the strictest of which is the two phase locking protocol while the most permissive recognizes all conflict-preserving serializable histories. This is the first locking-based protocol that can recognize the entire class of conflict-preserving serializable histories.

A fragmentation method that reduces the storage overhead of replicated objects is proposed. A data-management protocol for these fragmented objects is presented, and it is shown that this protocol is a generalization of quorum algorithms for replicated data in which objects are not fragmented. Although the protocol reduces storage requirements, it does not achieve a high level of resiliency for both read and write operations. By integrating a propagation mechanism with the protocol, the same level of resiliency is achieved for both read and write operations as other quorum protocols, while the storage cost is reduced.>

The authors propose a paradigm for developing, describing, and proving the correctness of concurrency control protocols for replicated databases in the presence of failures or communication restrictions. The approach used is to hierarchically divide the problem of achieving one-copy serializability by introducing the notion of a 'group' that is a higher level of abstraction than transactions. Instead of dealing with the overall problem, the paradigm breaks it into two simpler ones: (1) a local policy for each group that ensures a total order of all transactions in that group; and (2) a global policy that ensures a correct serialization of all groups. The paradigm is used to demonstrate the similarities between several concurrency control protocols by comparing the way they achieve correctness.>

In a replicated database, a data item may have copies residing on several sites. A replica control protocol is necessary to ensure that data items with several copies behave as if they consist of a single copy, as far as users can tell. We describe a new replica control protocol that allows the accessing of data in spite of site failures and network partitioning. This protocol provides the database designer with a large degree of flexibility in deciding the degree of data availability, as well as the cost of accessing data.

We propose a paradigm for developing, describing and proving the correctness of concurrency control protocols for replicated databases in the presence of failures or communication restrictions. Our approach is to hierarchically divide the problem of achieving one-copy serializability by introducing the notion of a “group” that is a higher level of abstraction than transactions. Instead of dealing with the overall problem of serializing all transactions, our paradigm divides the problem into two simpler ones. (1) A local policy for each group that ensures a total order of all transactions in that group. (2) A global policy that ensures a correct serialization of all groups. We use the paradigm to demonstrate the similarities between several concurrency control protocols by comparing the way they achieve correctness.

Article Free Access Share on Availability in partitioned replicated databases Authors: Amr El Abbadi View Profile , Sam Toueg View Profile Authors Info & Claims PODS '86: Proceedings of the fifth ACM SIGACT-SIGMOD symposium on Principles of database systemsJune 1985 Pages 240–251https://doi.org/10.1145/6012.15418Published:01 June 1985Publication History 51citation369DownloadsMetricsTotal Citations51Total Downloads369Last 12 Months14Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A data management protocol for executing transactions on a replicated database is presented. The protocol ensures one-copy serializability. i.e., the concurrent execution of transactions on a replicated database is equivalent to some serial execution of the same transactions on a non-replicated database. The protocol tolerates a large class of failures, including: processor and communication link crashes, partitioning of the communication network, lost messages, and slow responses of processors and communication links. Processor and link recoveries are also handled. The protocol implements the reading of a replicated object efficiently by reading the nearest available copy of the object. When reads outnumber writes, the protocol performs better than other known protocols.