A Join Index for XML Data Warehouses

XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimize them. In this paper, we propose a new join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. A theoretical study and experimental results demonstrate the efficiency of our join index. They also show that native XML DBMSs can compete with XML-compatible, relational DBMSs when warehousing and analyzing XML data.Comment: 2008 International Conference on Information Resources Management (Conf-IRM 08), Niagra Falls : Canada (2008

Constructing Large-Scale Semantic Web Indices for the Six RDF Collation Orders

The Semantic Web community collects masses of valuable and publicly available RDF data in order to drive the success story of the Semantic Web. Efficient processing of these datasets requires their indexing. Semantic Web indices make use of the simple data model of RDF: The basic concept of RDF is the triple, which hence has only 6 different collation orders. On the one hand having 6 collation orders indexed fast merge joins (consuming the sorted input of the indices) can be applied as much as possible during query processing. On the other hand constructing the indices for 6 different collation orders is very time-consuming for large-scale datasets. Hence the focus of this paper is the efficient Semantic Web index construction for large-scale datasets on today's multi-core computers. We complete our discussion with a comprehensive performance evaluation, where our approach efficiently constructs the indices of over 1 billion triples of real world data

Indexing query graphs to speedup graph query processing

Subgraph/supergraph queries although central to graph analytics, are costly as they entail the NP-Complete problem of subgraph isomorphism. We present a fresh solution, the novel principle of which is to acquire and utilize knowledge from the results of previously executed queries. Our approach, iGQ, encompasses two component subindexes to identify if a new query is a subgraph/supergraph of previously executed queries and stores related key information. iGQ comes with novel query processing and index space management algorithms, including graph replacement policies. The end result is a system that leads to significant reduction in the number of required subgraph isomorphism tests and speedups in query processing time. iGQ can be incorporated into any sub/supergraph query processing method and help improve performance. In fact, it is the only contribution that can speedup significantly both subgraph and supergraph query processing. We establish the principles of iGQ and formally prove its correctness. We have implemented iGQ and have incorporated it within three popular recent state of the art index-based graph query processing solutions. We evaluated its performance using real-world and synthetic graph datasets with different characteristics, and a number of query workloads, showcasing its benefits

GraphCache: A Caching System for Graph Queries

Graph query processing is essential for graph analytics, but can be very time-consuming as it entails the NP-Complete problem of subgraph isomorphism. Traditionally, caching plays a key role in expediting query processing. We thus put forth GraphCache (GC), the first full-edged caching system for general subgraph/supergraph queries. We contribute the overall system architecture and implementation of GC. We study a number of novel graph cache replacement policies and show that different policies win over different graph datasets and/or queries; we therefore contribute a novel hybrid graph replacement policy that is always the best or near-best performer. Moreover, we discover the related problem of cache pollution and propose a novel cache admission control mechanism to avoid cache pollution. Furthermore, we show that GC can be used as a front end, complementing any graph query processing method as a pluggable component. Currently, GC comes bundled with 3 top-performing filter-then-verify (FTV) subgraph query methods and 3 well-established direct subgraph-isomorphism (SI) algorithms - representing different categories of graph query processing research. Finally, we contribute a comprehensive performance evaluation of GC. We employ more than 6 million queries, generated using different workload generators, and executed against both real-world and synthetic graph datasets of different characteristics, quantifying the benefits and overheads, emphasizing the non-trivial lessons learned

Structural Summaries as a Core Technology for Efficient XML Retrieval

The Extensible Markup Language (XML) is extremely popular as a generic markup language for text documents with an explicit hierarchical structure. The different types of XML data found in today’s document repositories, digital libraries, intranets and on the web range from flat text with little meaningful structure to be queried, over truly semistructured data with a rich and often irregular structure, to rather rigidly structured documents with little text that would also fit a relational database system (RDBS). Not surprisingly, various ways of storing and retrieving XML data have been investigated, including native XML systems, relational engines based on RDBSs, and hybrid combinations thereof. Over the years a number of native XML indexing techniques have emerged, the most important ones being structure indices and labelling schemes. Structure indices represent the document schema (i.e., the hierarchy of nested tags that occur in the documents) in a compact central data structure so that structural query constraints (e.g., path or tree patterns) can be efficiently matched without accessing the documents. Labelling schemes specify ways to assign unique identifiers, or labels, to the document nodes so that specific relations (e.g., parent/child) between individual nodes can be inferred from their labels alone in a decentralized manner, again without accessing the documents themselves. Since both structure indices and labelling schemes provide compact approximate views on the document structure, we collectively refer to them as structural summaries. This work presents new structural summaries that enable highly efficient and scalable XML retrieval in native, relational and hybrid systems. The key contribution of our approach is threefold. (1) We introduce BIRD, a very efficient and expressive labelling scheme for XML, and the CADG, a combined text and structure index, and combine them as two complementary building blocks of the same XML retrieval system. (2) We propose a purely relational variant of BIRD and the CADG, called RCADG, that is extremely fast and scales up to large document collections. (3) We present the RCADG Cache, a hybrid system that enhances the RCADG with incremental query evaluation based on cached results of earlier queries. The RCADG Cache exploits schema information in the RCADG to detect cached query results that can supply some or all matches to a new query with little or no computational and I/O effort. A main-memory cache index ensures that reusable query results are quickly retrieved even in a huge cache. Our work shows that structural summaries significantly improve the efficiency and scalability of XML retrieval systems in several ways. Former relational approaches have largely ignored structural summaries. The RCADG shows that these native indexing techniques are equally effective for XML retrieval in RDBSs. BIRD, unlike some other labelling schemes, achieves high retrieval performance with a fairly modest storage overhead. To the best of our knowledge, the RCADG Cache is the only approach to take advantage of structural summaries for effectively detecting query containment or overlap. Moreover, no other XML cache we know of exploits intermediate results that are produced as a by-product during the evaluation from scratch. These are valuable cache contents that increase the effectiveness of the cache at no extra computational cost. Extensive experiments quantify the practical benefit of all of the proposed techniques, which amounts to a performance gain of several orders of magnitude compared to various other approaches

Transform Based And Search Aware Text Compression Schemes And Compressed Domain Text Retrieval

In recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. While a good fraction of this information is of transient interest, useful information of archival value will continue to accumulate. We need ways to manage, organize and transport this data from one point to the other on data communications links with limited bandwidth. We must also have means to speedily find the information we need from this huge mass of data. Sometimes, a single site may also contain large collections of data such as a library database, thereby requiring an efficient search mechanism even to search within the local data. To facilitate the information retrieval, an emerging ad hoc standard for uncompressed text is XML which preprocesses the text by putting additional user defined metadata such as DTD or hyperlinks to enable searching with better efficiency and effectiveness. This increases the file size considerably, underscoring the importance of applying text compression. On account of efficiency (in terms of both space and time), there is a need to keep the data in compressed form for as much as possible. Text compression is concerned with techniques for representing the digital text data in alternate representations that takes less space. Not only does it help conserve the storage space for archival and online data, it also helps system performance by requiring less number of secondary storage (disk or CD Rom) accesses and improves the network transmission bandwidth utilization by reducing the transmission time. Unlike static images or video, there is no international standard for text compression, although compressed formats like .zip, .gz, .Z files are increasingly being used. In general, data compression methods are classified as lossless or lossy. Lossless compression allows the original data to be recovered exactly. Although used primarily for text data, lossless compression algorithms are useful in special classes of images such as medical imaging, finger print data, astronomical images and data bases containing mostly vital numerical data, tables and text information. Many lossy algorithms use lossless methods at the final stage of the encoding stage underscoring the importance of lossless methods for both lossy and lossless compression applications. In order to be able to effectively utilize the full potential of compression techniques for the future retrieval systems, we need efficient information retrieval in the compressed domain. This means that techniques must be developed to search the compressed text without decompression or only with partial decompression independent of whether the search is done on the text or on some inversion table corresponding to a set of key words for the text. In this dissertation, we make the following contributions: (1) Star family compression algorithms: We have proposed an approach to develop a reversible transformation that can be applied to a source text that improves existing algorithm\u27s ability to compress. We use a static dictionary to convert the English words into predefined symbol sequences. These transformed sequences create additional context information that is superior to the original text. Thus we achieve some compression at the preprocessing stage. We have a series of transforms which improve the performance. Star transform requires a static dictionary for a certain size. To avoid the considerable complexity of conversion, we employ the ternary tree data structure that efficiently converts the words in the text to the words in the star dictionary in linear time. (2) Exact and approximate pattern matching in Burrows-Wheeler transformed (BWT) files: We proposed a method to extract the useful context information in linear time from the BWT transformed text. The auxiliary arrays obtained from BWT inverse transform brings logarithm search time. Meanwhile, approximate pattern matching can be performed based on the results of exact pattern matching to extract the possible candidate for the approximate pattern matching. Then fast verifying algorithm can be applied to those candidates which could be just small parts of the original text. We present algorithms for both k-mismatch and k-approximate pattern matching in BWT compressed text. A typical compression system based on BWT has Move-to-Front and Huffman coding stages after the transformation. We propose a novel approach to replace the Move-to-Front stage in order to extend compressed domain search capability all the way to the entropy coding stage. A modification to the Move-to-Front makes it possible to randomly access any part of the compressed text without referring to the part before the access point. (3) Modified LZW algorithm that allows random access and partial decoding for the compressed text retrieval: Although many compression algorithms provide good compression ratio and/or time complexity, LZW is the first one studied for the compressed pattern matching because of its simplicity and efficiency. Modifications on LZW algorithm provide the extra advantage for fast random access and partial decoding ability that is especially useful for text retrieval systems. Based on this algorithm, we can provide a dynamic hierarchical semantic structure for the text, so that the text search can be performed on the expected level of granularity. For example, user can choose to retrieve a single line, a paragraph, or a file, etc. that contains the keywords. More importantly, we will show that parallel encoding and decoding algorithm is trivial with the modified LZW. Both encoding and decoding can be performed with multiple processors easily and encoding and decoding process are independent with respect to the number of processors

Techniques efficaces basées sur des vues matérialisées pour la gestion des données du Web (algorithmes et systèmes)

Le langage XML, proposé par le W3C, est aujourd hui utilisé comme un modèle de données pour le stockage et l interrogation de grands volumes de données dans les systèmes de bases de données. En dépit d importants travaux de recherche et le développement de systèmes efficace, le traitement de grands volumes de données XML pose encore des problèmes des performance dus à la complexité et hétérogénéité des données ainsi qu à la complexité des langages courants d interrogation XML. Les vues matérialisées sont employées depuis des décennies dans les bases de données afin de raccourcir les temps de traitement des requêtes. Elles peuvent être considérées les résultats de requêtes pré-calculées, que l on réutilise afin d éviter de recalculer (complètement ou partiellement) une nouvelle requête. Les vues matérialisées ont fait l objet de nombreuses recherches, en particulier dans le contexte des entrepôts des données relationnelles.Cette thèse étudie l applicabilité de techniques de vues matérialisées pour optimiser les performances des systèmes de gestion de données Web, et en particulier XML, dans des environnements distribués. Dans cette thèse, nos apportons trois contributions.D abord, nous considérons le problème de la sélection des meilleures vues à matérialiser dans un espace de stockage donné, afin d améliorer la performance d une charge de travail des requêtes. Nous sommes les premiers à considérer un sous-langage de XQuery enrichi avec la possibilité de sélectionner des noeuds multiples et à de multiples niveaux de granularités. La difficulté dans ce contexte vient de la puissance expressive et des caractéristiques du langage des requêtes et des vues, et de la taille de l espace de recherche de vues que l on pourrait matérialiser.Alors que le problème général a une complexité prohibitive, nous proposons et étudions un algorithme heuristique et démontrer ses performances supérieures par rapport à l état de l art.Deuxièmement, nous considérons la gestion de grands corpus XML dans des réseaux pair à pair, basées sur des tables de hachage distribuées. Nous considérons la plateforme ViP2P dans laquelle des vues XML distribuées sont matérialisées à partir des données publiées dans le réseau, puis exploitées pour répondre efficacement aux requêtes émises par un pair du réseau. Nous y avons apporté d importantes optimisations orientées sur le passage à l échelle, et nous avons caractérisé la performance du système par une série d expériences déployées dans un réseau à grande échelle. Ces expériences dépassent de plusieurs ordres de grandeur les systèmes similaires en termes de volumes de données et de débit de dissémination des données. Cette étude est à ce jour la plus complète concernant une plateforme de gestion de contenus XML déployée entièrement et testée à une échelle réelle.Enfin, nous présentons une nouvelle approche de dissémination de données dans un système d abonnements, en présence de contraintes sur les ressources CPU et réseau disponibles; cette approche est mise en oeuvre dans le cadre de notre plateforme Delta. Le passage à l échelle est obtenu en déchargeant le fournisseur de données de l effort de répondre à une partie des abonnements. Pour cela, nous tirons profit de techniques de réécriture de requêtes à l aide de vues afin de diffuser les données de ces abonnements, à partir d autres abonnements.Notre contribution principale est un nouvel algorithme qui organise les vues dans un réseau de dissémination d information multi-niveaux ; ce réseau est calculé à l aide d outils techniques de programmation linéaire afin de passer à l échelle pour de grands nombres de vues, respecter les contraintes de capacité du système, et minimiser les délais de propagation des information. L efficacité et la performance de notre algorithme est confirmée par notre évaluation expérimentale, qui inclut l étude d un déploiement réel dans un réseau WAN.XML was recommended by W3C in 1998 as a markup language to be used by device- and system-independent methods of representing information. XML is nowadays used as a data model for storing and querying large volumes of data in database systems. In spite of significant research and systems development, many performance problems are raised by processing very large amounts of XML data. Materialized views have long been used in databases to speed up queries. Materialized views can be seen as precomputed query results that can be re-used to evaluate (part of) another query, and have been a topic of intensive research, in particular in the context of relational data warehousing. This thesis investigates the applicability of materialized views techniques to optimize the performance of Web data management tools, in particular in distributed settings, considering XML data and queries. We make three contributions.We first 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. Our work is the first to address the view selection problem for a rich subset of XQuery. 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.Second, we consider the management of large XML corpora in peer-to-peer networks, based on distributed hash tables (or DHTs, in short). We consider a platform leveraging distributed materialized XML views, defined by arbitrary XML queries, filled in with data published anywhere in the network, and exploited to efficiently answer queries issued by any network peer. This thesis has contributed important scalability oriented optimizations, as well as a comprehensive set of experiments deployed in a country-wide WAN. These experiments outgrow by orders of magnitude similar competitor systems in terms of data volumes and data dissemination throughput. Thus, they are the most advanced in understanding the performance behavior of DHT-based XML content management in real settings.Finally, we present a novel approach for scalable content-based publish/subscribe (pub/sub, in short) in the presence of constraints on the available computational resources of data publishers. We achieve scalability by off-loading subscriptions from the publisher, and leveraging view-based query rewriting to feed these subscriptions from the data accumulated in others. Our main contribution is a novel algorithm for organizing subscriptions in a multi-level dissemination network in order to serve large numbers of subscriptions, respect capacity constraints, and minimize latency. The efficiency and effectiveness of our algorithm are confirmed through extensive experiments and a large deployment in a WAN.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Optimizing graph query performance by indexing and caching

Subgraph/supergraph queries, though central to graph analytics, are costly as they entail the NP-Complete problem of subgraph isomorphism. To expedite graph query processing, the community has contributed a wealth of approaches that gradually form two categories, i.e., heuristic subgraph isomorphism (SI) methods and algorithms following “filter-then-verify” paradigm (FTV). However, they both bear performance limitations. And a significant drawback of current studies lies in that they throw away the results obtained when executing previous graph queries. To this end, the current work shall present a fresh solution named iGQ, principle of which is to acquire and utilize knowledge from the results of previously executed queries. iGQ encompasses two component subindexes to identify if a new query is a subgraph or supergraph of previously executed queries, such that the stored knowledge will be turned on to accelerate the execution of the new query graph through reducing the subgraph isomorphism tests to be performed. The correctness of iGQ is assured by formal proof. Moreover, iGQ affords the elegance of double use for subgraph and supergraph query processing, bridging the two separate research threads in the community. On the other hand, using cache to accelerate query processing has been prevalent in data management systems. In the realm of graph structured queries, however, little work has been done. Meanwhile, modern big data applications are emerging and demanding the high performance of graph query processing. Therefore, this thesis shall put forth a full-fledged graph caching system coined GraphCache for graph queries. From the ground up, GraphCache is designed as a semantic graph cache that could harness both subgraph and supergraph cache hits, expanding the traditional hits confined by exact match. GraphCache is featured by well-defined subsystems and interfaces, allowing for the flexibility of plugging in any general subgraph/supergraph query solution, be it an FTV algorithm or SI method. Furthermore, GraphCache incorporates the iGQ as the engine of query processing, where previously issued queries are leveraged to expedite graph query processing. With the continuous arrival of queries and the finite memory space, GraphCache requires mechanisms to effectively manage the space, which in turn emerges the problem of cache replacement. But none of the existing replacement policies are developed specifically for graph cache. This work hence proposes a number of graph query aware strategies with different trade-offs and emphasizes a novel hybrid replacement policy with competitive performance. Following the established research in literature, GraphCache handles graph queries against a static dataset, i.e., all graphs in the underlying dataset keep untouched during the continual arrival and execution of queries. However, in real-world applications, the graph dataset naturally evolves/changes over time. This poses a significant challenge for the current graph caching technique and hence gives rise to the requirement of advanced systems that are capable of accelerating subgraph/supergraph queries against dynamic datasets. To address the problem, this work shall contribute an upgraded graph caching system, namely GraphCache+, stressing the newly plugged in subsystems and components of dealing with the consistency of graph cache. GraphCache+ is characterized by its two cache models that represent different designs of ensuring graph cache consistency, as well as the novel logics of alleviating subgraph and supergraph query processing with formal proof of correctness. Additionally, this work is bundled with comprehensive performance evaluations of GraphCache/GraphCache+ with over 6 million queries against both real-world and synthetic datasets with different characteristics, revealing a number of non-trivial lessons. In overall, this work contributes to the community from three perspectives: it provides a fresh idea to expedite graph query processing, applicable for both SI methods and FTV algorithms; it presents GraphCache, to the best of our knowledge the first full-fledged graph caching system for general subgraph/supergraph queries; it explores the topic of graph cache consistency, putting forth a systematic solution GraphCache+