Abstract Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ignore the growing field of online banking. However, there is a lack of publicly available data for both. The lack of publicly available data hinders the progress of the field and limits the investigation of potential solutions. With this work, we: (a) introduce FraudNLP , the first anonymised, publicly available dataset for online fraud detection, (b) benchmark machine and deep learning methods with multiple evaluation measures, (c) argue that online actions do follow rules similar to natural language and hence can be approached successfully by natural language processing methods.

The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges.

The Medical Informatics Platform (MIP) of the Human Brain Project (HBP) is tasked with providing its users diverse high quality clinical data and tools for medical analysis, while complying with the national legislation about privacy and security. Data, which is provided by a large number of hospitals, tends to be heterogeneous and also has a constantly changing schema, due to hospitals' need to capture more information. In this paper we provide a look in the MIP's data ingestion pipeline and focus on steps taken by our team to properly integrate clinical data from heterogeneous sources while ensuring its quality throughout the processing pipeline. We have developed tools both for meta-data management and quality control.

Computing a (Union of Conjunctive Queries - UCQ) rewriting R for an input query and ontology and evaluating it over the given dataset is a prominent approach to query answering over ontologies. However, R can be large and complex in structure hence additional techniques, like query subsumption and data constraints, need to be employed in order to minimise Rew and lead to an efficient evaluation. Although sound in theory, how to efficiently and effectively implement many of these techniques in practice could be challenging. For example, many systems do not implement query subsumption. In the current paper we present several practical techniques for UCQ rewriting minimisation. First, we present an optimised algorithm for eliminating redundant (w.r.t. subsumption) queries as well as a novel framework for rewriting minimisation using data constraints. Second, we show how these techniques can also be used to speed up the computation of R in the first place. Third, we integrated all our techniques in our query rewriting system IQAROS and conducted an extensive experimental evaluation using many artificial as well as challenging real-world ontologies obtaining encouraging results as, in the vast majority of cases, our system is more efficient compared to the two most popular state-of-the-art systems.

The efficient processing of XQuery still poses significant challenges. A particularly effective technique to improve XQuery processing performance consists of using materialized views to answer queries. In this work, we consider the problem of choosing the best views to materialize within a given space budget in order to improve the performance of a query workload. The paper is the first to address the view selection problem for queries and views with value joins and multiple return nodes. The challenges we face stem from the expressive power and features of both the query and view languages and from the size of the search space of candidate views to materialize. While the general problem has prohibitive complexity, we propose and study a heuristic algorithm and demonstrate its superior performance compared to the state of the art.

We demonstrate a generic and extensible cost-based optimization and execution system for XPath queries, named GeCOEX, using a comprehensive suite of query analyzing and administrative tools, named QuOAX. GeCOEX supports many different physical operator implementations and XML storage engines and is agnostic to the underlying physical data model. Its optimizer is the first generic cost-based optimizer for XPath queries that always picks the cheapest estimated plan, among a very large number of possible plans, for a wide range of XPath queries and different datasets in a very small fraction of the time required for efficient execution. The QuOAX suite provides administration tools that allow the user to add new -- or deactivate already deployed -- physical operator implementations, physical operator cost models and rewriting rules and also to make use of different XML storage and XML statistics estimators. QuOAX also provides query plan analysis and visualization tools that allow users to visualize the physical plan chosen by the optimizer or all possible generated physical plans for a given query and to execute any of those plans. QuOAX helps users to i) easily test new XPath processing techniques, comparing them directly with existing ones and identifying the situations to which they show promise, ii) improve the effectiveness of the optimizer and iii) find out the appropriate access methods or indices that are beneficial for a specific workload.

Active data warehouses have emerged as a new business intelligence paradigm where data in the integrated repository is refreshed in near real-time. This shift of practices achieves higher consistency between the stored information and the latest updates, which in turn influences crucially the output of decision making processes. In this paper we focus on the changes required in the implementation of Extract Transform Load (ETL) operations which now need to be executed in an online fashion. In particular, the ETL transformations frequently include the join between an incoming stream of updates and a disk-resident table of historical data or metadata. In this context we propose a novel Semi-Streaming Index Join (SSIJ) algorithm that maximizes the throughput of the join by buffering stream tuples and then judiciously selecting how to best amortize expensive disk seeks for blocks of the stored relation among a large number of stream tuples. The relation blocks required for joining with the stream are loaded from disk based on an optimal plan. In order to maximize the utilization of the available memory space for performing the join, our technique incorporates a simple but effective cache replacement policy for managing the retrieved blocks of the relation. Moreover, SSIJ is able to adapt to changing characteristics of the stream (i.e. arrival rate, data distribution) by dynamically adjusting the allocated memory between the cached relation blocks and the stream. Our experiments with a variety of synthetic and real data sets demonstrate that SSIJ consistently outperforms the state-of-the-art algorithm in terms of the maximum sustainable throughput of the join while being also able to accommodate deadlines on stream tuple processing.

We consider the problem of rewriting XQuery queries using multiple materialized XQuery views. The XQuery dialect we use to express views and queries corresponds to tree patterns (returning data from several nodes, at different granularities, ranging from node identifiers to full XML subtrees) with value joins. We provide correct and complete algorithms for finding minimal rewritings, in which no view is redundant. Our work extends the state of the art by considering more flexible views than the mostly XPath 1.0 dialects previously considered, and more powerful rewritings. We implemented our algorithms and assess their performance through a set of experiments.

We address two open problems involving algebraic execution of full-text search queries. First, we show how to correctly apply traditional database rewrite optimizations to full-text algebra plans with integrated scoring, and explain why existing techniques fail. Second, we show how our techniques are applied in a generic scoring framework that supports a wide class of scoring algorithms, including algorithms seen in the literature and user-defined scoring.

We study the problem of querying XML data sources that accept only a limited set of queries, such as sources accessible by Web services which can implement very large (potentially infinite) families of XPath queries. To compactly specify such families of queries we adopt the Query Set Specifications, a formalism close to context-free grammars. We say that query Q is expressible by the specification P if it is equivalent to some expansion of P. Q is supported by P if it has an equivalent rewriting using some finite set of P's expansions. We study the complexity of expressibility and support and identify large classes of XPath queries for which there are efficient (PTIME) algorithms. Our study considers both the case in which the XML nodes in the results of the queries lose their original identity and the one in which the source exposes persistent node ids.

The creation of a generic and modular query optimization and processing infrastructure can provide significant benefits to XML data management. Key pieces of such an infrastructure are the physical operators that are available to the execution engine, to turn queries into execution plans. Such operators, to be efficient, need to implement sophisticated algorithms for logical XPath or XQuery operations. Moreover, to enable a cost-based optimizer to choose among them correctly, it is also necessary to provide cost models for such operator implementations. In this paper we present two novel families of algorithms for XPath physical operators, called LookUp (LU) and Sort-Merge-based (SM), along with detailed cost models. Our algorithms have significantly better performance compared to existing techniques over any one of a variety of different XML storage systems that provide a set of common primitive access methods. To substantiate the robustness and efficiency of our physical operators, we evaluate their individual performance over four different XML storage engines against operators that implement existing XPath processing techniques. We also demonstrate the performance gains for twig processing of using plans consisting of our operators compared to a state of the art holistic technique, specifically Twig2Stack. Additionally, we evaluate the precision of our cost models, and we conduct an analysis of the sensitivity of our algorithms and cost models to a variety of parameters.

Shared online databases allow community members to collaboratively maintain knowledge. Collaborative editing though inevitably leads to inconsistencies as different members enter erroneous data or conflicting opinions. Ideally community members should be able to see and resolve these inconsistencies in a collaborative fashion. However most current online databases do not support inconsistency resolution. Instead they try to by-pass the problem by either ignoring inconsistencies and treating data as if they were not conflicting or by requiring inconsistencies to be resolved outside the system. To address this limitation, we propose Ricolla; an online database system that, by treating inconsistencies as first-class citizens, supports a natural workflow for the management of conflicting data. The system captures inconsistencies (so that community members can easily inspect them) and remains fully functional in their presence, thus enabling inconsistency resolution in an ¿as-you-go¿ fashion. Moreover it supports several schemes for the resolution of inconsistencies, allowing among others users to collaboratively resolve certain conflicts while disagreeing on others.

Wireless sensor networks are becoming increasingly popular for a variety of applications. Users are frequently faced with the surprising discovery that readings produced by the sensing elements of their motes are often contaminated with outliers. Outlier readings can severely affect applications that rely on timely and reliable sensory data in order to provide the desired functionality. As a consequence, there is a recent trend to explore how techniques that identify outlier values can be applied to sensory data cleaning. Unfortunately, most of these approaches incur an overwhelming communication overhead, which limits their practicality. In this paper we introduce an in-network outlier detection framework, based on locality sensitive hashing, extended with a novel boosting process as well as efficient load balancing and comparison pruning mechanisms. Our method trades off bandwidth for accuracy in a straightforward manner and supports many intuitive similarity metrics.

Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays. In this paper, we first propose Double Index NEsted-loops Reactive join (DINER), a new adaptive two-way join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel reentrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus, better exploiting temporary delays when new data are not available. We then extend the applicability of the proposed technique for a more challenging setup: handling more than two inputs. Multiple Index NEsted-loop Reactive join (MINER) is a multiway join operator that inherits its principles from DINER. Our experiments using real and synthetic data sets demonstrate that DINER outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory. Our experiments also shows that in the presence of multiple inputs, MINER manages to produce a high percentage of early results, outperforming existing techniques for adaptive multiway join.

Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data is provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus improving pipelining by smoothing join result production and by masking source or network delays. In this paper we propose double index nested loops reactive join (DINER), a new adaptive join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel re-entrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus better exploiting temporary delays when new data is not available. Our experiments using real and synthetic data sets demonstrate that DINER outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory.

Recent work has demonstrated that readings provided by commodity sensor nodes are often of poor quality. In order to provide a valuable sensory infrastructure for monitoring applications, we first need to devise techniques that can withstand "dirty" and unreliable data during query processing. In this paper we present a novel aggregation framework that detects suspicious measurements by outlier nodes and refrains from incorporating such measurements in the computed aggregate values. We consider different definitions of an outlier node, based on the notion of a user-specified minimum support, and discuss techniques for properly routing messages in the networkin order to reduce the bandwidth consumption and the energy drain during the query evaluation. In our experiments using real and synthetic traces we demonstrate that: (i) a straightforward evaluation of a user aggregate query leads to practically meaningless results due to the existence of outliers; (ii) our techniques can detect and eliminate spurious readings without any application specific knowledge of what constitutes normal behavior; (iii) the identification of outliers, when performed inside the network, significantly reduces bandwidth and energy drain compared to alternative methods that centrally collect and analyze all sensory data; and (iv) we can significantly reduce the cost of the aggregation process by utilizing simple statistics on outlier nodes and reorganizing accordingly the collection tree.

We present a complete XPath cost-based optimization and execution framework and demonstrate its effectiveness and efficiency for a variety of queries and datasets. The framework is based on a logical XPath algebra with novel features and operators and a comprehensive set of rewriting rules that together enable us to algebraically capture many existing and novel processing strategies for XPath queries. An important part of the framework is PSA, a very efficient cost-based plan selection algorithm for XPath queries. In the presented experimental evaluation, PSA picked the cheapest estimated query plan in 100% of the cases. Our cost-based query optimizer independent of the underlying physical data model and storage system and of the available logical operator implementations, depending on a set of well-defined APIs. We also present an implementation of those APIs, including primitive access methods, a large pool of physical operators, statistics estimators and cost models, and experimentally demonstrate the effectiveness of our end-to-end query optimization system.

We study the problem of querying XML data sources that accept only a limited set of queries, such as sources accessible by Web services which can implement very large (potentially infinite) families of XPath queries. To compactly specify such families of queries we adopt the Query Set Specifications [14], a formalism close to context-free grammars. We say that query Q is expressible by the specification P if it is equivalent to some expansion of P . Q is supported by P if it has an equivalent rewriting using some finite set of P 's expansions. We study the complexity of expressibility and support and identify large classes of XPath queries for which there are efficient (PTIME) algorithms. Our study considers both the case in which the XML nodes in the results of the queries lose their original identity and the one in which the source exposes persistent node ids.

In this paper we discuss a robust aggregation framework that can detect spurious measurements and refrain from incorporating them in the computed aggregate values. Our framework can consider different definitions of an outlier node, based on a specified minimum support. Our experimental evaluation demonstrates the benefits of our approach.

In this paper we propose fully distributed trust-based policies for p2p systems to regulate the exchange of different type of services and discourage misbehaviour. In a system of peers with different service valuations and capabilities, the resource allocation and server selection policies are based on local reputation vectors, whose elements are the reputations of the peers in providing each service of the system. Our studies demonstrate that proposed policies lead to the dynamic formation of coalitions (cooperation) among peers, which mutually profit by the exchange of their services, without pre-existing knowledge of one another's capabilities and values of services. In this way utilities of all peers progressively increase. Only misbehaving (non contributive) peers do not benefit by such a system, as proposed policies efficiently recognize and block misbehaviour.

A lot of research has been conducted by the database community on methods and techniques for efficient XPath processing, with great success. Despite the progress made, significant opportunities for optimization of XPath still exist. One key to further improvements is to utilize more effectively existing facilities of relational RDBSes for the processing of XPath queries. After taking a comprehensive look at such facilities, we present techniques for XPath processing that work by identifying the best relational join algorithms, indices and file organization strategies for XPath queries. Our techniques both reduce the latency of the resulting SQL translations and guarantee their pipelined execution. We also propose a new technique for XML reconstruction from relations-mapped XML that "splits the difference" between schema-aware and schema-oblivious XML-to-relational mapping for a significant performance improvement. An extensive experimental study confirms the performance benefits of our optimization techniques and shows that a system implementing these techniques on top of a commercial RDBMS is competitive with respect to query performance with other native and relational-based state-of-the-art XPath processing systems, commercial as well as research prototypes.

We present MiniCount, the first efficient sound and complete algorithm for finding maximally contained rewritings of conjunctive queries with count, using conjunctive views with count and conjunctive views without aggregation. An efficient and scalable solution to this problem yields significant benefits for data warehousing and decision support systems, as well as for powerful data integration systems.We first present a naive rewriting algorithm implicit in the recent theoretical results by Cohen et al. [5] and identify three independent sources of exponential complexity in the naive algorithm, including an expensive containment check. Then we present and discuss MiniCount and prove it sound and complete. We also present an experimental study that shows Mini- Count to be orders of magnitude faster than the naive algorithm, and to be able to scale to large numbers of views

We describe the QURSED system for the declarative specification and automatic generation of Web-based query forms and reports ( QFR s) for semistructured XML data. In QURSED, a QFR is formally described by its query set specification ( QSS ) which captures the complex query and reporting capabilities of the QFR and the associations of the query set specification with visual elements that implement these capabilities on a Web page. The design-time component of QURSED, called QURSED Editor, semi-automates the development of the query set specification and its association with visual elements by translating intuitive visual actions taken by a developer into appropriate specification fragments . The run-time component of QURSED produces XQuery statements by synthesizing fragments from the query set specification that have been activated during the interaction of the end-user with the QFR and renders the query results in interactive reports as specified by the QSS . We describe the techniques and algorithms employed by QURSED with emphasis on how it accommodates the intricacies introduced by the semistructured nature of the underlying data. We present the formal model of the query set specification, as well as its generation via the QURSED Editor, and focus on the techniques and heuristics the Editor employs for translating visual designer input into meaningful specifications. We also present the algorithms QURSED employs for query generation and report generation. An online demonstration of the system is available at http://www.db.ucsd.edu/qursed/.

No abstract available.

QURSED enables the development of web-based query forms and reports (QFRs) that query and report semistructured XML data, i.e., data that are characterized by nesting, irregularities and structural variance. The query aspects of a QFR are captured by its query set specification, which formally encodes multiple parameterized condition fragments and can describe large numbers of queries. The run-time component of QURSED produces XQuery-compliant queries by synthesizing fragments from the query set specification that have been activated during the interaction of the end-user with the QFR. The design-time component of QURSED, called QURSED Editor, semi-automates the development of the query set specification and its association with the visual components of the QFR by translating visual actions into appropriate query set specifications. We describe QURSED and illustrate how it accommodates the intricacies that the semistructured nature of the underlying database introduces. We specifically focus on the formal model of the query set specification, its generation via the QURSED Editor and its coupling with the visual aspects of the web-based form and report.

Enosys Markets offers a state-of-the-art data integration software platform to support the development of the next generation of eBusiness applications that deliver value by providing new levels of function for customer relationship management, e-commerce, supply chain management, and decision support. These applications require that data be integrated from information sources that exist both within and across organizational boundaries. The Enosys Markets data integration architecture and product family provides a complete end-to-end XML-based solution for integrating and querying distributed information sources. It incorporates advanced research into XML and database technology. We present the product architecture and components, discuss the key technical challenges, and outline the technical concepts and innovations employed in the Enosys platform.

Article XML query forms (XQForms): declarative specification of XML query interfaces Share on Authors: Michalis Petropoulos Dept. of Computer Science and Engineering, University of California, San Diego Dept. of Computer Science and Engineering, University of California, San DiegoView Profile , Vasilis Vassalos Information Systems Dept., New York University and Enosys Markets, Inc Information Systems Dept., New York University and Enosys Markets, IncView Profile , Yannis Papakonstantinou Dept. of Computer Science and Engineering, University of California, San Diego and Enosys Markets, Inc Dept. of Computer Science and Engineering, University of California, San Diego and Enosys Markets, IncView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 642–651https://doi.org/10.1145/371920.372170Online:01 April 2001Publication History 17citation517DownloadsMetricsTotal Citations17Total Downloads517Last 12 Months4Last 6 weeks1 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 SiteGet Access

We address the problem of query rewriting for TSL, a language for querying semistructured data. We develop and present an algorithm that, given a semistructured query q and a set of semistructured views V, finds rewriting queries, i.e., queries that access the views and produce the same result as q. Our algorithm is based on appropriately generalizing containment mappings, the chase, and query composition — techniques that were developed for structured, relational data. We also develop an algorithm for equivalence checking of TSL queries.

Introduction The TSIMMIS system [1] integrates data from multiple heterogeneous sources and provides users with seamless integrated views of the data. It translates a user query on the integrated views into a set of source queries and postprocessing steps that compute the answer to the user query from the results of the source queries. TSIMMIS uses a mediation architecture [11] to accomplish this (Figure 1). User Source 1 Source 2 Source N Mediator Figure 1: The TSIMMIS Architecture Many other data integration systems like Garlic [2, 9] and Information Manifold [4] employ a similar architecture. One of the distinguishing features of TSIMMIS is its use of a semi-structured data model (called the Object Exchange Model or OEM [7]) for dealing with the heterogeneity of the data sources. In particular, it employs source wrappers [3] that provide a uniform OEM interface to the mediator. In SIGMO

In order to access information from a variety of heterogeneous information sources, one has to be able to translate queries and data from one data model into another. This functionality is provided by so-called (source) wrappers [4,8] which convert queries into one or more commands/queries understandable by the underlying source and transform the native results into a format understood by the application. As part of the TSIMMIS project [1, 6] we have developed hard-coded wrappers for a variety of sources (e.g., Sybase DBMS, WWW pages, etc.) including legacy systems (Folio). However, anyone who has built a wrapper before can attest that a lot of effort goes into developing and writing such a wrapper. In situations where it is important or desirable to gain access to new sources quickly, this is a major drawback. Furthermore, we have also observed that only a relatively small part of the code deals with the specific access details of the source. The rest of the code is either common among wrappers or implements query and data transformation that could be expressed in a high level, declarative fashion.