With automated composition we mean generating an executable process that satisfies a given composition requirements by communicating with a set of existing Web services. Several approaches have been proposed to tackle this problem. However, most of them either omit or oversimplify important aspects of the Web service composition problem. The driving idea of the approach we’re presenting in this paper is to overcome these limitations, in order to deal with real world composition problems. The ASTRO approach is able to cope with complex control and data flows, i.e., with Web services exposing complex protocols and exchanging structured data, and with composition requirements expressing constraints not only on the service interactions but also on the exchanged data. The ASTRO approach has been implemented and evaluated on real world composition domains.

In the modern world of web service enabled enterprise applications,the ability to parse and process XML documents efficiently is of fundamental importance. The two premier development platforms of today, .NET and J2EE offer extensive capabilities for processing XML. In this paper, we make certain technical comparisons about the different XML processing techniques that are offered in these two frameworks. We discuss the basics of the different parsing alternatives available in the two platforms such as SAX, DOM, pull parsing, as well as the XML binding technologies JAXB and XmlSerializer. We then utilize a suite of open-source XML processing programs that explores various components of the parsing infrastructure and offer examples of which technique might be most suitable during the lifecycle of an XML document. At the end of it all, the reader will have a balanced view of how one can take advantage of the best of both worlds. This is only possible because the XML language is universal across both .NET and J2EE.

Memory is a particularly vital resource for decision support systems. This is because effective decision support frequently requires the analysis of large amounts of data and results in the production of equally large (and often larger) query results. In most situations, the amount of main memory available in the system is not enough to meet the response time goals of interactive analysis. This thesis deals with ways of managing memory more effectively so that we can better meet the demands imposed on decision support systems. The first part of the thesis concerns memory management for relational databases running complex decision support queries. We discuss several algorithms for distributing available memory among concurrently running operators of a query in ways that satisfy operator requirements and scheduling constraints. One of these algorithms, based on linear programming, is also optimal in that it is guaranteed to produce the best query response time. The second part of the thesis discusses memory management for data mining applications. Most current data mining algorithms have rather long response times that render them unsuitable for interactive use. This thesis incorporates the first proposal for using caching in the data mining context. We show how memory can be used to build a knowledge cache that can cut down the time required for association rule mining by several orders of magnitude. The final part of this thesis aims at broadening the scope of data mining queries and extending the benefits of caching to this more powerful query model. We view data mining as a special kind of aggregation operation that can be combined with selections and group-bys to yield a richer set of analysis tools. To make this system interactive, we describe the design of a chunked cache that employs a specialized semantic caching scheme to store the results of association mining queries in a uniform and scalable fashion. We believe that this idea will make a significant contribution towards bringing OLAP and various data mining techniques together under a common, unifying framework that can be used to build extensible and scalable decision support systems.

Association rule mining is a valuable decision support technique that can be used to analyze customer preferences, buying patterns, and product correlations. Current systems are however handicapped by the long processing times required by mining algorithms that make them unsuitable for interactive use. In this paper, we propose the use of a knowledge cache that can reduce the response time by several orders of magnitude. Most of the performance gain comes from the idea of guaranteed support that allows us to completely eliminate database accesses in a large number of cases. Using this cache, the time taken to answer a query is proportional to just the size of the result, rather than to the size of the database. Cache replacement is best done by a benefit-metric based strategy that can easily adapt to changing query patterns. We show that our caching scheme is quite robust, providing good performance on a wide variety of data distributions even for small cache sizes. We also compare algorithms that use precomputation to those that use caching and show that the best performance is obtained by combining both these techniques. Finally, we illustrate how the idea of caching can be readily extended to a broader class of problems such as the mining of generalized association rules.

Article Free Access Share on Memory allocation strategies for complex decision support queries Authors: Biswadeep Nag Computer Science Department, University of Wisconsin-Madison Computer Science Department, University of Wisconsin-MadisonView Profile , David J. DeWitt Computer Science Department, University of Wisconsin-Madison Computer Science Department, University of Wisconsin-MadisonView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998Pages 116–123https://doi.org/10.1145/288627.288647Published:01 November 1998Publication History 16citation375DownloadsMetricsTotal Citations16Total Downloads375Last 12 Months29Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper presents a number of new techniques for parallelizing geo-spatial database systems and discusses their implementation in the Paradise object-relational database system. The effectiveness of these techniques is demonstrated using a variety of complex geo-spatial queries over a 120 GB global geo-spatial data set.

The generalized transitive closure operator can be used to ask and answer a number of sophisticated queries on a database which is viewed as a directed graph. These queries cannot be expressed in SQL or relational algebra and neither can they be answered by commercially available relational or object-oriented database systems. In this report we describe the design and implementation of the generalized transitive closure operator in Paradise, an experimental geographical information system, a domain in which this operator is especially useful. We show how semi-naive evaluation, a basic technique for evaluating recursive queries, can be extended to compute aggregates and we also give a formal proof of the correctness of our method. We also discuss how selections can be used to optimize the evaluation of the query. The report concludes with a few examples of how these queries can now be expressed in the extended SQL like query language of Paradise and what the results look like.

This paper presentsa number of new techniques for parrdlelizing gee-spatial database systems and discusses their implementation in the Paradise object-relational database system. The effective- ness of these techniques is demonstrated using a variety of com- plex gee-spatial queries over a 120 GB global gee-spatial data set. 1. Introduction and Motivation