Semantics and efficient evaluation of partial tree-pattern queries on XML

Current applications export and exchange XML data on the web. Usually, XML data are queried using keyword queries or using the standard structured query language XQuery the core of which consists of the navigational query language XPath. In this context, one major challenge is the querying of the data when the structure of the data sources is complex or not fully known to the user. Another challenge is the integration of multiple data sources that export data with structural differences and irregularities. In this dissertation, a query language for XML called Partial Tree-Pattern Query (PTPQ) language is considered. PTPQs generalize and strictly contain Tree-Pattern Queries (TPQs) and can express a broad structural fragment of XPath. Because of their expressive power and flexibility, they are useful for querying XML documents the structure of which is complex or not fully known to the user, and for integrating XML data sources with different structures. The dissertation focuses on three issues. The first one is the design of efficient non-main-memory evaluation methods for PTPQs. The second one is the assignment of semantics to PTPQs so that they return meaningful answers. The third one is the development of techniques for answering TPQs using materialized views. Non-main-memory XML query evaluation can be done in two modes (which also define two evaluation models). In the first mode, data is preprocessed and indexes, called inverted lists, are built for it. In the second mode, data are unindexed and arrives continuously in the form of a stream. Existing algorithms cannot be used directly or indirectly to efficiently compute PTPQs in either mode. Initially, the problem of efficiently evaluating partial path queries in the inverted lists model has been addressed. Partial path queries form a subclass of PTPQs which is not contained in the class of TPQs. Three novel algorithms for evaluating partial path queries including a holistic one have been designed. The analytical and experimental results show that the holistic algorithm outperforms the other two. These results have been extended into holistic and non-holistic approaches for PTPQs in the inverted lists model. The experiments show again the superiority of the holistic approach. The dissertation has also addressed the problem of evaluating PTPQs in the streaming model, and two original efficient streaming algorithms for PTPQs have been designed. Compared to the only known streaming algorithm that supports an extension of TPQs, the experimental results show that the proposed algorithms perform better by orders of magnitude while consuming a much smaller fraction of memory space. An original approach for assigning semantics to PTPQs has also been devised. The novel semantics seamlessly applies to keyword queries and to queries with structural restrictions. In contrast to previous approaches that operate locally on data, the proposed approach operates globally on structural summaries of data to extract tree patterns. Compared to previous approaches, an experimental evaluation shows that our approach has a perfect recall both for XML documents with complete and with incomplete data. It also shows better precision compared to approaches with similar recall. Finally, the dissertation has addressed the problem of answering XML queries using exclusively materialized views. An original approach for materializing views in the context of the inverted lists model has been suggested. Necessary and sufficient conditions have been provided for tree-pattern query answerability in terms of view-to-query homomorphisms. A time and space efficient algorithm was designed for deciding query answerability and a technique for computing queries over view materializations using stack- based holistic algorithms was developed. Further, optimizations were developed which (a) minimize the storage space and avoid redundancy by materializing views as bitmaps, and (b) optimize the evaluation of the queries over the views by applying bitwise operations on view materializations. The experimental results show that the proposed approach obtains largely higher hit rates than previous approaches, speeds up significantly the evaluation of queries without using views, and scales very smoothly in terms of storage space and computational overhead

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Automaton Meet Algebra: A Hybrid Paradigm for Efficiently Processing XQuery over XML Stream

XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data streams. The automaton paradigm is naturally suited for pattern retrieval on tokenized XML streams, but requires patches for implementing the filtering or restructuring functionalities common for the XML query languages. In contrast, the algebraic paradigm is well-established for processing self-contained tuples. However, it does not traditionally support token inputs. This dissertation proposes a framework called Raindrop, which accommodates both the automaton and algebra paradigms to take advantage of both. First, we propose an architecture for Raindrop. Raindrop is an algebra framework that models queries at different abstraction levels. We represent the token-based automaton computations as an algebraic subplan at the high level while exposing the automaton details at the low level. The algebraic subplan modeling automaton computations can thus be integrated with the algebraic subplan modeling the non-automaton computations. Second, we explore a novel optimization opportunity. Other XML stream processing systems always retrieve all the patterns in a query in the automaton. In contrast, Raindrop allows a plan to retrieve some of the pattern retrieval in the automaton and some out of the automaton. This opens up an automaton-in-or-out optimization opportunity. We study this optimization in two types of run-time environments, one with stable data characteristics and one with fluctuating data characteristics. We provide search strategies catering to each environment. We also describe how to migrate from a currently running plan to a new plan at run-time. Third, we optimize the automaton computations using the schema knowledge. A set of criteria are established to decide what schema constraints are useful to a given query. Optimization rules utilizing different types of schema constraints are proposed based on the criteria. We design a rule application algorithm which ensures both completeness (i.e., no optimization is missed) and minimality (i.e., no redundant optimization is introduced). The experimentations on both real and synthetic data illustrate that these techniques bring significant performance improvement with little overhead

Metadata-Aware Query Processing over Data Streams

Many modern applications need to process queries over potentially infinite data streams to provide answers in real-time. This dissertation proposes novel techniques to optimize CPU and memory utilization in stream processing by exploiting metadata on streaming data or queries. It focuses on four topics: 1) exploiting stream metadata to optimize SPJ query operators via operator configuration, 2) exploiting stream metadata to optimize SPJ query plans via query-rewriting, 3) exploiting workload metadata to optimize parameterized queries via indexing, and 4) exploiting event constraints to optimize event stream processing via run-time early termination. The first part of this dissertation proposes algorithms for one of the most common and expensive query operators, namely join, to at runtime identify and purge no-longer-needed data from the state based on punctuations. Exploitations of the combination of punctuation and commonly-used window constraints are also studied. Extensive experimental evaluations demonstrate both reduction on memory usage and improvements on execution time due to the proposed strategies. The second part proposes herald-driven runtime query plan optimization techniques. We identify four query optimization techniques, design a lightweight algorithm to efficiently detect the optimization opportunities at runtime upon receiving heralds. We propose a novel execution paradigm to support multiple concurrent logical plans by maintaining one physical plan. Extensive experimental study confirms that our techniques significantly reduce query execution times. The third part deals with the shared execution of parameterized queries instantiated from a query template. We design a lightweight index mechanism to provide multiple access paths to data to facilitate a wide range of parameterized queries. To withstand workload fluctuations, we propose an index tuning framework to tune the index configurations in a timely manner. Extensive experimental evaluations demonstrate the effectiveness of the proposed strategies. The last part proposes event query optimization techniques by exploiting event constraints such as exclusiveness or ordering relationships among events extracted from workflows. Significant performance gains are shown to be achieved by our proposed constraint-aware event processing techniques

Efficient main memory-based XML stream processing

Applications that process XML documents as files or streams are naturally main-memory based. This makes main memory the bottleneck for scalability. This doctoral thesis addresses this problem and presents a toolkit for effective buffer management in main memory-based XML stream processors. XML document projection is an established technique for reducing the buffer requirements of main memory-based XML processors, where only data relevant to query evaluation is loaded into main memory buffers. We present a novel implementation of this task, where we use string matching algorithms designed for efficient keyword search in flat strings to navigate in tree-structured data. We then introduce an extension of the XQuery language, called FluX, that supports event-based query processing. Purely event-based queries of this language can be executed on streaming XML data in a very direct way. We develop an algorithm to efficiently rewrite XQueries into FluX. This algorithm is capable of exploiting order constraints derived from schemata to reduce the amount of buffering in query evaluation. During streaming query evaluation, we continuously purge buffers from data that is no longer relevant. By combining static query analysis with a dynamic analysis of the buffer contents, we effectively reduce the size of memory buffers. We have confirmed the efficacy of these techniques by extensive experiments and by publication at international venues. To compare our contributions to related work in a systematic manner, we contribute an abstract framework for XML stream processing. This framework allows us to gain a greater-picture view over the factors influencing the main memory consumption.Anwendungen, die XML-Dokumente als Dateien oder Ströme verarbeiten, sind natürlicherweise hauptspeicherbasiert. Für die Skalierbarkeit wird der Hauptspeicher damit zu einem Engpass. Diese Doktorarbeit widmet sich diesem Problem, zu dessen Lösung sie Werkzeuge für eine effektive Pufferverwaltung in hauptspeicherbasierten Prozessoren für XML-Datenströme vorstellt. Die Projektion von XML-Dokumenten ist eine etablierte Methode, um den Pufferverbrauch von hauptspeicherbasierten XML-Prozessoren zu reduzieren. Dabei werden nur jene Daten in den Hauptspeicherpuffer geladen, die für die Anfrageauswertung auch relevant sind. Wir präsentieren eine neue Implementierung dieser Aufgabe, wobei wir Algorithmen zur effizienten Suche in flachen Zeichenketten einsetzen, um in baumartig strukturierten Daten zu navigieren. Danach stellen wir eine Erweiterung der XQuery-Sprache vor, genannt FluX, welche eine ereignisbasierte Anfragebearbeitung erlaubt. Anfragen, die nur ereignisbasierte Konstrukte benutzen, können direkt über XML-Datenströmen ausgewertet werden. Dazu entwickeln wir einen Algorithmus, mit dessen Hilfe sich XQuery-Anfragen effizient in FluX übersetzen lassen. Dieser benutzt Ordnungsinformationen aus Datenschemata, womit das Puffern in der Anfragebearbeitung reduziert werden kann. Während der Verarbeitung des Datenstroms bereinigen wir laufend den Hauptspeicherpuffer von solchen Daten, die nicht länger relevant sind. Eine nachhaltige Reduzierung der Größe von Hauptspeicherpuffern gelingt durch die Kombination der statischen Anfrageanalyse mit einer dynamischen Analyse der Pufferinhalte. Die Effektivität dieser Puffermanagement-Techniken erfährt ihre Bestätigung in umfangreichen Experimenten und internationalen Publikationen. Für einen systematischen Vergleich unserer Beiträge mit der aktuellen Literatur entwickeln wir ein abstraktes System zur Modellierung von Prozessoren zur XML-Stromverarbeitung. So können wir die spezifischen Faktoren herausgreifen, die den Hauptspeicherverbrauch beeinflussen

Semantics and result disambiguation for keyword search on tree data

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them. Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced

Algorithms for XML stream processing : massive data, external memory and scalable performance

Many modern applications require processing of massive streams of XML data, creating difficult technical challenges. Among these, there is the design and implementation of applications to optimize the processing of XPath queries and to provide an accurate cost estimation for these queries processed on a massive steam of XML data. In this thesis, we propose a novel performance prediction model which a priori estimates the cost (in terms of space used and time spent) for any structural query belonging to Forward XPath. In doing so, we perform an experimental study to confirm the linear relationship between stream-processing and data-access resources. Therefore, we introduce a mathematical model (linear regression functions) to predict the cost for a given XPath query. Moreover, we introduce a new selectivity estimation technique. It consists of two elements. The first one is the path tree structure synopsis: a concise, accurate, and convenient summary of the structure of an XML document. The second one is the selectivity estimation algorithm: an efficient streamquerying algorithm to traverse the path tree synopsis for estimating the values of cost-parameters. Those parameters are used by the mathematical model to determine the cost of a given XPath query. We compare the performance of our model with existing approaches. Furthermore, we present a use case for an online stream-querying system. The system uses our performance predicate model to estimate the cost for a given XPath query in terms of time/memory. Moreover, it provides an accurate answer for the query's sender. This use case illustrates the practical advantages of performance management with our techniques.Plusieurs applications modernes nécessitent un traitement de flux massifs de données XML, cela crée de défis techniques. Parmi ces derniers, il y a la conception et la mise en ouvre d'outils pour optimiser le traitement des requêtes XPath et fournir une estimation précise des coûts de ces requêtes traitées sur un flux massif de données XML. Dans cette thèse, nous proposons un nouveau modèle de prédiction de performance qui estime a priori le coût (en termes d'espace utilisé et de temps écoulé) pour les requêtes structurelles de Forward XPath. Ce faisant, nous réalisons une étude expérimentale pour confirmer la relation linéaire entre le traitement de flux, et les ressources d'accès aux données. Par conséquent, nous présentons un modèle mathématique (fonctions de régression linéaire) pour prévoir le coût d'une requête XPath donnée. En outre, nous présentons une technique nouvelle d'estimation de sélectivité. Elle se compose de deux éléments. Le premier est le résumé path tree: une présentation concise et précise de la structure d'un document XML. Le second est l'algorithme d'estimation de sélectivité: un algorithme efficace de flux pour traverser le synopsis path tree pour estimer les valeurs des paramètres de coût. Ces paramètres sont utilisés par le modèle mathématique pour déterminer le coût d'une requête XPath donnée. Nous comparons les performances de notre modèle avec les approches existantes. De plus, nous présentons un cas d'utilisation d'un système en ligne appelé "online stream-querying system". Le système utilise notre modèle de prédiction de performance pour estimer le coût (en termes de temps / mémoire) d'une requête XPath donnée. En outre, il fournit une réponse précise à l'auteur de la requête. Ce cas d'utilisation illustre les avantages pratiques de gestion de performance avec nos technique