International Journal of Cooperative Information Systems

Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

Metadata has been identified as a key success factor in data warehouse projects. It captures all kinds of information necessary to design, build, use and interpret the data warehouse contents. This paper gives an overview about the role metadata plays for data warehousing and reviews existing standards, commercial solutions and research actions relevant to metadata management. It turns out that an overall solution for managing all metadata in a central or federated repository is still missing regarding a global metadata schema as well as system aspects and interoperability among involved tools producing metadata. The divergence of proposed standards will probably prevent a breakthrough within the near future.

A Data Warehouse (DW) is a large collection of data integrated from multiple distributed autonomous databases and other information sources. A DW can be seen as a set of materialized views defined over the remote source data. Until now research work on DW design is restricted to quantitatively selecting view sets for materialization. However, quality issues in the DW design are neglected. In this paper we suggest a novel statement of the DW design problem that takes into account quality factors. We design a DW system architecture that supports performance and data consistency quality goals. In this framework we present a high level approach that allows to check whether a view selection guaranteeing a data completeness quality goal also satisfies a data currency quality goal. This approach is based on an AND/OR dag representation for multiple queries and views. It also allows determining the minimal change propagation frequencies that satisfy the data currency quality goal along with the optimal query evaluation and change propagation plans. Our results can be directly used for a quality driven design of a DW.

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored in the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks when designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views in such a way as to minimize the total query response time over all queries, given a limited amount of time for maintaining the views (maintenance-cost view selection problem).

In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in time complexity over existing search-based approaches using heuristics. Our analysis shows that the algorithm consistently yields a solution that lies within 10% of the optimal query benefit while at the same time exhibiting only a linear increase in execution time. We have implemented a prototype version of our algorithm which is used to simulate the measurements used in the analysis of our approach.

Data warehouses are used to store large amounts of data which is often used for On-Line Analytical Processing (OLAP). Short response times are essential for on-line decision support. Common approaches to reach this goal in read-mostly environments are the precomputation of materialized views and the use of index structures. This paper focuses on the use of index structures for supporting fast access to data. The performance of index structures depends on many different parameters. Here, we focus on a set of nine parameters. Two approaches are presented to support the decision making process which index structure should be applied. The first approach is based on classification trees. The second approach uses an aggregation and scatter diagram method. Both approaches are applied to four distinct index structures: a tree-based index structure without aggregated data, a tree-based index structure with aggregated data and two bitmap index structures. This paper presents results of the comparison with both approaches.

We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose , a simple data model for multidimensional databases, as the reference for the logical layer. provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that databases can be implemented in both relational and multidimensional storage systems. We also show that can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.