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Materialized aggregate views represent a set of redundant entities in a data warehouse that are frequently used to accelerate On-Line Analytical Processing (OLAP). Due to the complex structure of the data warehouse and the different profiles of the users who submit queries, there is need for tools that will automate and ease the view selection and management processes. In this article we present DynaMat, a system that manages dynamic collections of materialized aggregate views in a data warehouse. At query time, DynaMat utilizes a dedicated disk space for storing computed aggregates that are further engaged for answering new queries. Queries are executed independently or can be bundled within a multiquery expression. In the latter case, we present an execution mechanism that exploits dependencies among the queries and the materialized set to further optimize their execution. During updates, DynaMat reconciles the current materialized view selection and refreshes the most beneficial subset of it within a given maintenance window. We show how to derive an efficient update plan with respect to the available maintenance window, the different update policies for the views and the dependencies that exist among them. Categories and Subject Descriptors: H.2.7 [Database Management]: Database Administration--data warehouse and repository; H.4.m [Information Systems Applications]: Miscellaneous General Terms: Management Additional Key Words and Phrases: Data cube, data warehousing, materialized views, OLAP

The Client-Server computing paradigm has significantly influenced the way modern Database Management Systems are designed and built. In such systems, clients maintain data pages in their main-memory caches, originating from the server's database. The Enhanced Client-Server architecture takes advantage of all the available client resources, including their long-term memory. Clients can cache server data into their own disk units if these data are part of their operational spaces. However, when updates occur at the server, a number of clients may need to not only be notified about these changes, but also obtain portions of the updates as well. In this paper, we examine the problem of managing server imposed updates that affect data cached on client disk managers. We propose a number of server update propagation techniques in the context of the Enhanced Client-Server DBMS architecture, and examine the performance of these strategies through detailed simulation experiments. In addition, we study how the various settings of the network affect the performance of these policies. Index Terms - Client-server DBMSs, update propagation policies, cluster database computing, push servers, modeling of client-server DBMSs, simulation and performance analysis.

ADMS is an advanced database management system developed to experiment with incremental access methods for large and distributed databases. It has been developed over the past eight years at the University of Maryland. The purpose of this paper is to provide an overview of ADMS, and to describe its capabilities and the performance attained by its incremental access methods. This paper also describes an enhanced client-server architecture that allows an incremental gateway access to multiple heterogeneous commercial database management systems.

Several studies have demonstrated the effectiveness of the Haar wavelet decomposition as a tool for reducing large amounts of data down to compact wavelet synopses that can be used to obtain fast, accurate approximate answers to user queries. Although originally designed for minimizing the overall mean-squared (i.e., L2-norm) error in the data approximation, recently proposed methods also enable the use of Haar wavelets in minimizing other error metrics, such as the relative error in data value reconstruction, which is arguably the most important for approximate query answers. Relatively little attention, however, has been paid to the problem of using wavelet synopses as an approximate query answering tool over complex tabular datasets containing multiple measures, such as those typically found in real-life OLAP applications. Existing decomposition approaches will either operate on each measure individually, or treat all measures as a vector of values and process them simultaneously. As we demonstrate in this article, these existing individual or combined storage approaches for the wavelet coefficients of different measures can easily lead to suboptimal storage utilization, resulting in drastically reduced accuracy for approximate query answers. To address this problem, in this work, we introduce the notion of an extended wavelet coefficient as a flexible, efficient storage method for wavelet coefficients over multimeasure data. We also propose novel algorithms for constructing effective (optimal or near-optimal) extended wavelet-coefficient synopses under a given storage constraint, for both sum-squared error and relative-error norms. Experimental results with both real-life and synthetic datasets validate our approach, demonstrating that our techniques consistently obtain significant gains in approximation accuracy compared to existing solutions. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems--Query processing General Terms: Algorithms, Experimentation, Performance Additional Key Words and Phrases: Wavelets, data synopses, approximate query processing ACM Reference Format: Deligiannakis, A., Garofalakis, M., and Roussopoulos, N. 2007. Extended wavelets for multiple measures. ACM Trans. Datab. Syst. 32, 2, Article 10 (June 2007), 52 pages. DOI = 10.1145/ 1242524.1242527 http://doi.acm.org/10.1145/1242524.1242527