Concurrent workloads often extract insights from high-throughput, real-time data streams. Existing stream processing engines isolate each query's resources, ensuring robust performance but incurring high infrastructure costs. In contrast, sharing work reduces the amount of necessary resources but introduces inter-query interference, leading to performance degradation for some queries. We introduce FunShare, a stream-processing system that improves resource efficiency without compromising performance by dynamically grouping queries based on their performance characteristics. FunShare strategically relaxes query interdependencies and minimizes redundant computation while preserving individual query performance. It achieves this by using an adaptive optimization framework that monitors execution metrics, accurately estimates computation overlaps, and reconfigures execution plans on the fly in response to changes in the underlying data streams. Our evaluation demonstrates that FunShare minimizes resource consumption compared to isolated execution while maintaining or improving throughput for all queries.

Interactive applications require processing tens to hundreds of concurrent analytical queries within tight time constraints. In such setups, where high concurrency causes contention, work-sharing databases are critical for improving scalability and for bounding the increase in response time. However, as such databases share data access using full scans and expensive shared filters, they suffer from a data-access bottleneck that jeopardizes interactivity. We present SH2O: a novel data-access operator that addresses the data-access bottleneck of work-sharing databases. SH2O is based on the idea that an access pattern based on judiciously selected multidimensional ranges can replace a set of shared filters. To exploit the idea in an efficient and scalable manner, SH2O uses a three-tier approach: i) it uses spatial indices to efficiently access the ranges without overfetching, ii) it uses an optimizer to choose which filters to replace such that it maximizes cost-benefit for index accesses, and iii) it exploits partitioning schemes and independently accesses each data partition to reduce the number of filters in the access pattern. Furthermore, we propose a tuning strategy that chooses a partitioning and indexing scheme that minimizes SH2O's cost for a target workload. Our evaluation shows a speedup of 1.8-22.2 for batches of hundreds of data-access-bound queries.

To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability Deciding on a static partitioning for a given set of "hot" keys is not sufficient as these keys are not known in advance, and even worse, the data distribution can change unpredictably. Existing algorithms either optimize for a specific distribution or, in order to adapt, assume a centralized partitioner that processes every incoming tuple and observes the whole workload. However, this is not realistic in a distributed environment, where multiple parallel upstream operators exist, as the centralized partitioner itself becomes the bottleneck and limits scalability In this work, we propose Dalton: a lightweight, adaptive, yet scalable partitioning operator that relies on reinforcement learning. By memoizing state and dynamically keeping track of recent experience, Dalton: i) adjusts its policy at runtime and quickly adapts to the workload, ii) avoids redundant computations and minimizes the per-tuple partitioning overhead, and iii) efficiently scales out to multiple instances that learn cooperatively and converge to a joint policy Our experiments indicate that Dalton scales regardless of the input data distribution and sustains 1.3X - 6.7X higher throughput than existing approaches.

Stream Processing Engines (SPEs) have recently begun utilizing heterogeneous coprocessors (e.g., GPUs) to meet the velocity requirements of modern real-time applications. The massive parallelism and high memory bandwidth of GPUs can significantly increase processing throughput in data-intensive streaming scenarios, such as windowed aggregations. However, previous research only focused on the overall architecture of hybrid CPU-GPU streaming systems and the need for efficient in-GPU window operators was overshadowed by the limited interconnect bandwidth.

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are accessed by multiple independent, untrusted entities, which need to interact with shared data. In such scenarios, the potential conflicts of interest incentivize malicious parties to act in a dishonest way and tamper the data to their own benefit. The decentralized nature of the systems renders verifiable data integrity a strenuous but necessary task: The various parties should be able to audit changes and detect tampering when it happens.In this work, we focus on HDFS, the most common storage substrate for Big Data analytics. HDFS is vulnerable to malicious users and participating nodes and does not provide a trustful lineage mechanism, thus jeopardizing the integrity of stored data and the credibility of extracted insights. As a remedy, we present Clouseau, a blockchain-based system that provides verifiable integrity over HDFS, while it does not incur significant overhead at the critical path of read/write operations. During the demonstration, the attendees will have the chance to interact with Clouseau, corrupt data themselves, and witness how Clouseau detects malicious actions.

The public Cloud market has become a monopoly, where a handful of providers - which are by default considered as trusted entities - define the prices, accumulate knowledge from users' data and computations and strengthen their already privileged position. As a remedy we propose CloudAgora, a platform that democratizes the Cloud market by allowing individuals and companies alike to compete on equal terms as potential resource providers, while enabling users to access low-cost storage and computation without having to blindly trust any central authority. During the demo, the attendees will be able to interact with CloudAgora through an easy-to-use UI, which will allow them to act both as users and as providers. As users, the attendees will have the chance to request storage or compute resources, upload data and outsource task processing over remote infrastructures. As providers, they will be able to participate in auctions, serve requests and offer validity proofs upon request. Moreover, the audience will experience first hand how the underlying blockchain technology is used to record commitment policies, publicly verify off-chain services and trigger automatic micropayments.

The IoT era has brought forth a computing paradigm shift from traditional high-end servers to "edge" devices of limited processing and memory capabilities. These devices, together with sensors, regularly produce very high data volumes nowadays. For many real-time applications, storing and indexing an unbounded stream may not be an option. Thus, it is important that we design algorithms and systems that can both work at the edge of the network and be able to answer queries on distributed, streaming data. Moreover, in many streaming scenarios, fresh data tend to be prioritized. A sliding-window model is an important case of stream processing, where only the most recent elements remain active and the rest are discarded. In this work, we study the problem of maintaining basic aggregate statistics over a sliding-window data stream under the constraint of limited memory. As in IoT scenarios the available memory is typically much less than the window size, queries are answered from compact synopses that are maintained in an online fashion. For the efficient construction of such synopses, in this work, we propose wavelet-based algorithms that provide deterministic guarantees and produce almost exact results. Our algorithms can work on any kind of numerical data and do not have the positive-numbers constraint of techniques such as the exponential histograms. Our experimental evaluation indicates that, in terms of accuracy and space-efficiency, our solution outperforms the exponential histograms and deterministic waves techniques.

Modern analytics involve computations over enormous numbers of data records. The volume of data and the stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past years has been the construction of synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum error metric. By approximating sharp discontinuities, wavelet decomposition has proved to be a very effective tool for data reduction. However, existing wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical complexities for large datasets. Furthermore, they cannot efficiently handle the multi-dimensional version of the problem. In order to provide a practical solution, we develop parallel algorithms that take advantage of key-properties of the wavelet decomposition and allocate tasks to multiple workers. To that end, we present (i) a general framework for the parallelization of existing dynamic programming algorithms, (ii) a parallel version of one such DP algorithm, and (iii) two highly efficient distributed greedy algorithms that can deal with data of arbitrary dimensionality. Our extensive experiments on both real and synthetic datasets over Hadoop show that the proposed algorithms achieve linear scalability and superior running-time performance compared to their centralized counterparts.

Modern Big Data processing systems, scheduling platforms and cloud infrastructures employ specialized hardware accelerators such as GPUs, FPGAs, TPUs, ASICs, etc. to optimize the execution of resource intensive workloads such as Machine Learning, Artificial Intelligence or generic Data Analytics tasks. Nevertheless, this support is mostly a user-dependent, manual process that requires careful and educated decisions on both the amount and type of required resources to exploit the underlying hardware and achieve any user-defined higher level policies. In this work we present the initial design of the HeterogeneRous Scheduler (HRS), an intelligent scheduler that can make automated decisions on both how and where to map arbitrary data analytics tasks to underlying cloud hardware that may consist of a mix of hardware accelerators and clusters with general purpose CPUs. We experimentally evaluate the performance trade-offs between hardware accelerators and CPUs where we show that there are cases where one technology outperforms the other. We finally present an initial architecture of HRS where we depict its different components and their interactions with the Big Data framework and the cloud infrastructure.

Modern data analytics involve simple and complex computations over enormous numbers of data records. The volume of data and the increasingly stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past years has been the efficient construction of fixed-space synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum error metric. For data reduction, wavelet decomposition has proved to be a very effective tool, as it can successfully approximate sharp discontinuities and provide accurate answers to queries. However, existing polynomial time wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical time and space complexities for large datasets. In order to provide a practical solution to the problem, we develop parallel algorithms that take advantage of key-properties of the wavelet decomposition and allocate tasks to multiple workers. To that end, we present i) a general framework for the parallelization of existing dynamic programming algorithms, ii) a parallel version of one such DP-based algorithm and iii) a new parallel greedy algorithm for the problem. To the best of our knowledge, this is the first attempt to scale algorithms for wavelet thresholding for maximum error metrics via a state-of-the-art distributed runtime. Our extensive experiments on both real and synthetic datasets over Hadoop show that the proposed algorithms achieve linear scalability and superior running-time performance compared to their centralized counterparts. Furthermore, our distributed greedy algorithm outperforms the distributed version of the current state-of-the-art dynamic programming algorithm by 2 to 4 times, without compromising the quality of results.

Big Data applications receive an ever-increasing amount of attention, thus becoming a dominant class of applications that are deployed over virtualized environments. Cloud environments entail a large amount of complexity relative to I/O performance. The use of Big Data increases the complexity of I/O management as well as its characterization and prediction: As I/O operations become growingly dominant in such applications, the intricacies of virtualization, different storage back ends and deployment setups significantly hinder our ability to analyze and correctly predict I/O performance. To that end, this work proposes an end-to-end modeling technique to predict performance of I/O--intensive Big Data applications running over cloud infrastructures. We develop a model tuned over application and infrastructure dimensions: Primitive I/O operations, data access patterns, storage back ends and deployment parameters. The trained model can be used to predict both I/O but also general task performance. Our evaluation results show that for jobs which are dominated by I/O operations, such as I/O-bound MapReduce jobs, our model is capable of predicting execution time with an accuracy close to 90% that decreases as application processing becomes more complex.

The amount of social networking data that is being produced and consumed daily is huge and it is constantly increasing. A user's digital footprint coming from social networks or mobile devices, such as comments and check-ins contains valuable information about his preferences. The collection and analysis of such footprints using also information about the users' friends and their footprints offers many opportunities in areas such as personalized search, recommendations, etc. When the size of the collected data or the complexity of the applied methods increases, traditional storage and processing systems are not enough and distributed approaches are employed. In this work, we present MoDisSENSE, an open-source distributed platform that provides personalized search for points of interest and trending events based on the user's social graph by combining spatio-textual user generated data. The system is designed with scalability in mind, it is built using a combination of latest state-of-the art big data frameworks and its functionality is offered through easy to use mobile and web clients which support the most popular social networks. We give an overview of its architectural components and technologies and we evaluate its performance and scalability using different query types over various cluster sizes. Using the web or mobile clients, users are allowed to register themselves with their own social network credentials, perform socially enhanced queries for POIs, browse the results and explore the automatic blog creation functionality that is extracted by analyzing already collected GPS traces.

In this work we present MoDisSENSE, a distributed analytics platform for social networking services over mobile devices. MoDisSENSE collects and stores various types of data from heterogeneous sources, such as GPS traces from cell phones, user profile information and comments from social networks connected to the platform. These are combined through spatio-temporal and textual analysis, performed in a distributed fashion, in order to extract knowledge, make smart suggestions and leverage user experience. The datastore follows a hybrid approach to handle both raw and processed data, simultaneously covering the need for scalability and fast query processing. Thus, the platform is able to resolve complex, multi-parameter, socially charged queries over Points of Interest in the order of milliseconds even under heavy load.

Unanticipated load spikes or skewed data access patterns may lead to severe performance degradation in data serving applications, a typical problem of distributed NoSQL data-stores. In these cases, load balancing is a necessary operation. In this demonstration, we present the DBalancer, a generic distributed module that can be installed on top of a typical NoSQL data-store and provide an efficient and highly configurable load balancing mechanism. Balancing is performed by simple message exchanges and typical data movement operations supported by most modern NoSQL data-stores. We present the system's architecture, we describe in detail its modules and their interaction and we implement a suite of different algorithms on top of it. Through a web-based interactive GUI we allow the users to launch NoSQL clusters of various sizes, to apply numerous skewed and dynamic workloads and to compare the implemented load balancing algorithms. Videos and graphs showcasing each algorithm's effect on a number of indicative performance and cost metrics will be created on the fly for every setup. By browsing the results of different executions users will be able to grasp each algorithm's balancing mechanisms and performance impact in a number of representative setups.