Optimized Translation of XPath into Algebraic Expressions Parameterized by Programs Containing Navigational Primitives

We propose a new approach for the efficient evaluation of XPath expressions. This is important, since XPath is not only used as a simple, stand-alone query language, but is also an essential ingredient of XQuery and XSLT. The main idea of our approach is to translate XPath into algebraic expressions parameterized with programs. These programs are mainly built from navigational primitives like accessing the first child or the next sibling. The goals of the approach are 1) to enable pipelined evaluation, 2) to avoid producing duplicate (intermediate) result nodes, 3) to visit as few document nodes as possible, and 4) to avoid visiting nodes more than once. This improves the existing approaches, because our method is highly efficient

Anatomy of a Native XML Base Management System

Several alternatives to manage large XML document collections exist, ranging from file systems over relational or other database systems to specifically tailored XML repositories. In this paper we give a tour of Natix, a database management system designed from scratch for storing and processing XML data. Contrary to the common belief that management of XML data is just another application for traditional databases like relational systems, we illustrate how almost every component in a database system is affected in terms of adequacy and performance. We show how to design and optimize areas such as storage, transaction management comprising recovery and multi-user synchronisation as well as query processing for XML

TIMBER: A native XML database

This paper describes the overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan. The system is based upon a bulk algebra for manipulating trees, and natively stores XML. New access methods have been developed to evaluate queries in the XML context, and new cost estimation and query optimization techniques have also been developed. We present performance numbers to support some of our design decisions. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42328/1/20110274.pd

Evaluating queries on structure with eXtended access support relations

There are three common design decisions taken by today’s search engines. First, they do not replicate the data found on the Web. Second, they rely on full-text indexes instead. Third, they do not support the querying of document structure. The main reason for the latter is that HTML’s ability to express semantics with syntactic structure is very limited. This is different for XML since it allows for self-describing data. Due to its flexibility by inventing arbitrary new element and attribute names, XML allows to encode semantics within syntax. The consequence is that search engines for XML should support the querying of structure. In our current work on search engines for XML data on the Web, we want to keep the first two design decisions of traditional search engines but modify the last one according to the new requirements implied by the necessity to query structure. Since our search engine accepts queries with structural information, a full-text index does not suffice any longer. What is needed is a scalable index structure that allows to answer queries over the structure of XML documents. One possible index structure called eXtended Access Support Relation (XASR) is introduced. Further, we report on a search engine for XML data called Mumpits. Due to its prototypical character, we intentionally kept the design and implementation of Mumpits very simple. Its design is centered around a single XASR and its implementation heavily builds on a commercial relational database management syste

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

Core Technologies for Native XML Database Management Systems

This work investigates the core technologies required to build Database Management Systems (DBMSs) for large collections of XML documents. We call such systems XML Base Management Systems (XBMSs). We identify requirements, and analyze how they can be met using a conventional DBMS. Our conclusion is that an XML support layer on top of an existing conventional DBMS does not address the requirements for XBMSs. Hence, we built a Native XBMS, called Natix. Natix has been developed completely from scratch, incorporating optimizations for high-performance XML processing in those places where they are most effective