Till Westmann

Anatomy of a native XML base management system

Abstract. Several alternatives to manage large XML document collections exist, ranging from file systems over relational or other database systems to specifically tailored XML base management systems. 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 synchronization - as well as query processing for XML.

The BEA/XQRL streaming XQuery processor

In this paper, we describe the design, implementation, and performance characteristics of a complete, industrial-strength XQuery engine, the BEA streaming XQuery processor. The engine was designed to provide very high performance for message processing applications, i.e., for transforming XML data streams, and it is a central component of the 8.1 release of BEAs WebLogic Integration (WLI) product. This XQuery engine is fully compliant with the August 2002 draft of the W3C XML Query Language specification. A goal of this paper is to describe how an efficient, fully compliant XQuery engine can be built from a few relatively simple components and well-understood technologies.

The implementation and performance of compressed databases

In this paper, we show how compression can be integrated into a relational database system. Specifically, we describe how the storage manager, the query execution engine, and the query optimizer of a database system can be extended to deal with compressed data. Our main result is that compression can significantly improve the response time of queries if very light-weight compression techniques are used. We will present such light-weight compression techniques and give the results of running the TPC-D benchmark on a so compressed database and a non-compressed database using the AODB database system, an experimental database system that was developed at the Universities of Mannheim and Passau. Our benchmark results demonstrate that compression indeed offers high performance gains (up to 50%) for IO-intensive queries and moderate gains for CPU-intensive queries. Compression can, however, also increase the running time of certain update operations. In all, we recommend to extend todays database systems with light-weight compression techniques and to make extensive use of this feature.

The BEA streaming XQuery processor

Abstract.This paper describes the design, implementation, and performance characteristics of a commercial XQuery processing engine, the BEA streaming XQuery processor. This XQuery engine was designed to provide high performance for message-processing applications, i.e., for transforming XML data streams. The engine is a central component of the 8.1 release of BEA’s WebLogic Integration (WLI) product. The BEA XQuery engine is fully compliant with the August 2002 draft of the W3C XML Query Language specification and we are currently porting it to the latest version of the XQuery language (July 2004). A goal of this paper is to describe how a fully compliant yet efficient XQuery engine has been built from a few relatively simple components and well-understood technologies.

Natix: A Technology Overview

Several alternatives to manage large XML document collections exist, ranging from file systems over relational or other database systems to specifically tailored XML base management systems. In this paper we review 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 indicate how almost every component in a database system is affected in terms of adequacy and performance. We show what kind of problems have to be tackled when designing and optimizing areas such as storage, transaction management comprising recovery and multi-user synchronization as well as query processing for XML.

XQSE: An XQuery Scripting Extension for the AquaLogic Data Services Platform

The AquaLogic Data Services Platform (ALDSP) is a BEA middleware platform for creating services that access and manipulate information drawn from multiple heterogeneous sources of data. The integration logic for read services is specified declaratively using the XQuery language. ALDSP 3.0, available in December 2007, includes a new XQuery-based Scripting Extension - XQSE - that enables developers to write procedural as well as declarative logic without leaving the XQuery world. In this paper, we describe the XQSE extensions to XQuery and show how they help to support important new classes of data services in ALDSP 3.0.

Updates in the AquaLogic Data Services Platform

The BEA AquaLogic Data Services Platform (ALDSP) is a middleware platform for creating services that integrate and manipulate information from disparate enterprise data sources. This paper provides a technical overview of the all-new update support in ALDSP 3.0, released in January 2008. It describes the update side of data services, our unique model for making update automation transparent and flexible, and the use of the XQuery Scripting Extension (XQSE) for further customizing the systems default handling of updates. It also gives an overview of the ALDSP update processing machinery, including the automatic generation of update maps from read functions, translation of update maps into Update Virtual Machine (UVM) programs, the UVM instruction interpreter, and SQL generation for updates to data drawn from relational data sources.

The BEA AquaLogic data services platform (Demo)

We showcase the BEA AquaLogic Data Services Platform (ALDSP), a middleware infrastructure product that enables the declarative development of data services for service-oriented architectures (SOA). ALDSP includes support for modeling networks of interrelated data services, for realizing data services using either graphical or source-based XQuery editors, for testing data services as they are developed, and for identifying and incorporating changes in the structure of the underlying sources of data. Physical data sources supported include relational tables and views, Web services, packaged applications, stored procedures, XML files, delimited files, and custom Java applications. Data service definitions can be layered; as with relational views, such layering is virtual, and is rewritten away at query compilation time. ALDSP supports both read and update data service functions, and the ALDSP XML query runtime includes a number of interesting query operators and distributed query optimizations. In addition, ALDSP supports function caching, fine-grained security, and SQL-based data access as well as providing service-based and XQuery access to SOA data. We plan to demonstrate as much of this as time permits.

BigFUN: A performance study of big data management system functionality

In this paper, we report on an evaluation of four representative Big Data management systems (BDMSs): Mon-goDB, Hive, AsterixDB, and a commercial parallel shared-nothing relational database system. In terms of features, all offer to store and manage large volumes of data, and all provide some degree of query processing capabilities on top of such data. Our evaluation is based on a micro-benchmark that utilizes a synthetic application that has a social network flavor. We analyze the performance results and discuss the lessons learned from this effort. We hope that this study will inspire future domain-centric evaluations of BDMSs with a focus on their features.

Xyleme, a dynamic warehouse for XML data of the Web

The current development of the Web and the generalization of XML technology provide a major opportunity which can radically change the face of the Web. Xyleme intends to be a leader of this revolution by providing database services over the XML data of the Web. Originally, Xyleme was a research project functioning as an open, loosely coupled network of researchers. At the end of 2000, a prototype had been implemented. A start-up company, also called Xyleme, is now turning into a product. The authors summarize the main research efforts of the Xyleme team. They concern: a scalable architecture; the efficient storage of huge quantities of XML data (hundreds of millions of pages); XML query processing with full-text and structural indexing; data acquisition strategies to build the repository and keep it up-to-date; change control with services such as query subscription; and semantic data integration to free users from having to deal with many specific DTDs when expressing queries.