On Region Algebras, XML Databases, and Information Retrieval

This paper describes some new ideas on developing a logical algebra for databases that manage textual data and support information retrieval functionality. We describe a first prototype of such a system

REALIZATION OF A SYSTEM OF EFFICIENT QUERYING OF HIERARCHICAL DATA TRANSFORMED INTO A QUASI-RELATIONAL MODEL

Extensible Markup Language was mainly designed to easily represent documents; however, it has evolved and is now widely used for the representation of arbitrary data structures. There are many Application Programming Interfaces (APIs) to aid software developers with processing XML data. There are also many languages for querying and transforming XML, such as XPath or XQuery, which are widely used in this field. However, because of the great flexibility of XML documents, there are no unified data storing and processing standards, tools, or systems.On the other hand, a relational model is still the most-commonly and widely used standard for storing and querying data. Many Database Management Systems consist of components for loading and transforming hierarchical data. DB2 pureXML or Oracle SQLX are some of the most-recognized examples. Unfortunately, all of them require knowledge of additional tools, standards, and languages dedicated to accessing hierarchical data (for example, XPath or XQuery). Transforming XML documents into a (quasi)relational model and then querying (transformed) documents with SQL or SQL–like queries would significantly simplify the development of data-oriented systems and applications.In this paper, an implementation of the SQLxD query system is proposed. The XML documents are converted into a quasi-relational model (preserving their hierarchical structure), and the SQL–like language based on SQL-92 allows for efficient data querying

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

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

Hyperset Approach to Semi-structured Databases and the Experimental Implementation of the Query Language Delta

This thesis presents practical suggestions towards the implementation of the hyperset approach to semi-structured databases and the associated query language Delta. This work can be characterised as part of a top-down approach to semi-structured databases, from theory to practice. The main original part of this work consisted in implementation of the hyperset Delta query language to semi-structured databases, including worked example queries. In fact, the goal was to demonstrate the practical details of this approach and language. The required development of an extended, practical version of the language based on the existing theoretical version, and the corresponding operational semantics. Here we present detailed description of the most essential steps of the implementation. Another crucial problem for this approach was to demonstrate how to deal in reality with the concept of the equality relation between (hyper)sets, which is computationally realised by the bisimulation relation. In fact, this expensive procedure, especially in the case of distributed semi-structured data, required some additional theoretical considerations and practical suggestions for efficient implementation. To this end the 'local/global' strategy for computing the bisimulation relation over distributed semi-structured data was developed and its efficiency was experimentally confirmed.Comment: Technical Report (PhD thesis), University of Liverpool, Englan

Implementation of Web Query Languages Reconsidered

Visions of the next generation Web such as the "Semantic Web" or the "Web 2.0" have triggered the emergence of a multitude of data formats. These formats have different characteristics as far as the shape of data is concerned (for example tree- vs. graph-shaped). They are accompanied by a puzzlingly large number of query languages each limited to one data format. Thus, a key feature of the Web, namely to make it possible to access anything published by anyone, is compromised. This thesis is devoted to versatile query languages capable of accessing data in a variety of Web formats. The issue is addressed from three angles: language design, common, yet uniform semantics, and common, yet uniform evaluation. % Thus it is divided in three parts: First, we consider the query language Xcerpt as an example of the advocated class of versatile Web query languages. Using this concrete exemplar allows us to clarify and discuss the vision of versatility in detail. Second, a number of query languages, XPath, XQuery, SPARQL, and Xcerpt, are translated into a common intermediary language, CIQLog. This language has a purely logical semantics, which makes it easily amenable to optimizations. As a side effect, this provides the, to the best of our knowledge, first logical semantics for XQuery and SPARQL. It is a very useful tool for understanding the commonalities and differences of the considered languages. Third, the intermediate logical language is translated into a query algebra, CIQCAG. The core feature of CIQCAG is that it scales from tree- to graph-shaped data and queries without efficiency losses when tree-data and -queries are considered: it is shown that, in these cases, optimal complexities are achieved. CIQCAG is also shown to evaluate each of the aforementioned query languages with a complexity at least as good as the best known evaluation methods so far. For example, navigational XPath is evaluated with space complexity O(q d) and time complexity O(q n) where q is the query size, n the data size, and d the depth of the (tree-shaped) data. CIQCAG is further shown to provide linear time and space evaluation of tree-shaped queries for a larger class of graph-shaped data than any method previously proposed. This larger class of graph-shaped data, called continuous-image graphs, short CIGs, is introduced for the first time in this thesis. A (directed) graph is a CIG if its nodes can be totally ordered in such a manner that, for this order, the children of any node form a continuous interval. CIQCAG achieves these properties by employing a novel data structure, called sequence map, that allows an efficient evaluation of tree-shaped queries, or of tree-shaped cores of graph-shaped queries on any graph-shaped data. While being ideally suited to trees and CIGs, the data structure gracefully degrades to unrestricted graphs. It yields a remarkably efficient evaluation on graph-shaped data that only a few edges prevent from being trees or CIGs

From Relations to XML: Cleaning, Integrating and Securing Data

While relational databases are still the preferred approach for storing data, XML is emerging as the primary standard for representing and exchanging data. Consequently, it has been increasingly important to provide a uniform XML interface to various data sources— integration; and critical to protect sensitive and confidential information in XML data — access control. Moreover, it is preferable to first detect and repair the inconsistencies in the data to avoid the propagation of errors to other data processing steps. In response to these challenges, this thesis presents an integrated framework for cleaning, integrating and securing data. The framework contains three parts. First, the data cleaning sub-framework makes use of a new class of constraints specially designed for improving data quality, referred to as conditional functional dependencies (CFDs), to detect and remove inconsistencies in relational data. Both batch and incremental techniques are developed for detecting CFD violations by SQL efficiently and repairing them based on a cost model. The cleaned relational data, together with other non-XML data, is then converted to XML format by using widely deployed XML publishing facilities. Second, the data integration sub-framework uses a novel formalism, XML integration grammars (XIGs), to integrate multi-source XML data which is either native or published from traditional databases. XIGs automatically support conformance to a target DTD, and allow one to build a large, complex integration via composition of component XIGs. To efficiently materialize the integrated data, algorithms are developed for merging XML queries in XIGs and for scheduling them. Third, to protect sensitive information in the integrated XML data, the data security sub-framework allows users to access the data only through authorized views. User queries posed on these views need to be rewritten into equivalent queries on the underlying document to avoid the prohibitive cost of materializing and maintaining large number of views. Two algorithms are proposed to support virtual XML views: a rewriting algorithm that characterizes the rewritten queries as a new form of automata and an evaluation algorithm to execute the automata-represented queries. They allow the security sub-framework to answer queries on views in linear time. Using both relational and XML technologies, this framework provides a uniform approach to clean, integrate and secure data. The algorithms and techniques in the framework have been implemented and the experimental study verifies their effectiveness and efficiency

Cost-Based Optimization of Integration Flows

Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, and high requirements for data consistency and up-to-dateness of query results, many instances of integration flows are executed over time. Due to this high load and blocking synchronous source systems, the performance of the central integration platform is crucial for an IT infrastructure. To tackle these high performance requirements, we introduce the concept of cost-based optimization of imperative integration flows that relies on incremental statistics maintenance and inter-instance plan re-optimization. As a foundation, we introduce the concept of periodical re-optimization including novel cost-based optimization techniques that are tailor-made for integration flows. Furthermore, we refine the periodical re-optimization to on-demand re-optimization in order to overcome the problems of many unnecessary re-optimization steps and adaptation delays, where we miss optimization opportunities. This approach ensures low optimization overhead and fast workload adaptation