Knowledge and Metadata Integration for Warehousing Complex Data

With the ever-growing availability of so-called complex data, especially on the Web, decision-support systems such as data warehouses must store and process data that are not only numerical or symbolic. Warehousing and analyzing such data requires the joint exploitation of metadata and domain-related knowledge, which must thereby be integrated. In this paper, we survey the types of knowledge and metadata that are needed for managing complex data, discuss the issue of knowledge and metadata integration, and propose a CWM-compliant integration solution that we incorporate into an XML complex data warehousing framework we previously designed.Comment: 6th International Conference on Information Systems Technology and its Applications (ISTA 07), Kharkiv : Ukraine (2007

Data warehouse stream view update with multiple streaming.

The main objective of data warehousing is to store information representing an integration of base data from single or multiple data sources over an extended period of time. To provide fast access to the data, regardless of the availability of the data source, data warehouses often use materialized views. Materialized views are able to provide aggregation on some attributes to help Decision Support Systems. Updating materialized views in response to modifications in the base data is called materialized view maintenance. In some applications, for example, the stock market and banking systems, the source data is updated so frequently that we can consider them as a continuous stream of data. To keep the materialized view updated with respect to changes in the base tables in a traditional way will cause query response times to increase. This thesis proposes a new view maintenance algorithm for multiple streaming which improves semi-join methods and hash filter methods. Our proposed algorithm is able to update a view which joins two base tables where both of the base tables are in the form of data streams (always changing). By using a timestamp, building updategrams in parallel and by optimizing the joining cost between two data sources it can reduce the query response time or execution time significantly.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .A336. Source: Masters Abstracts International, Volume: 44-03, page: 1391. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Data warehouse stream view update with hash filter.

A data warehouse usually contains large amounts of information representing an integration of base data from one or more external data sources over a long period of time to provide fast-query response time. It stores materialized views which provide aggregation (SUM, MIX, MIN, COUNT and AVG) on some measure attributes of interest for data warehouse users. The process of updating materialized views in response to the modification of the base data is called materialized view maintenance. Some data warehouse application domains, like stock markets, credit cards, automated banking and web log domains depend on data sources updated as continuous streams of data. In particular, electronic stock trading markets such as the NASDAQ, generate large volumes of data, in bursts that are up to 4,200 messages per second. This thesis proposes a new view maintenance algorithm (StreamVup), which improves on semi join methods by using hash filters. The new algorithm first, reduce the amount of bytes transported through the network for streams tuples, and secondly reduces the cost of join operations during view update by eliminating the recompution of view updates caused by newly arriving duplicate tuples. (Abstract shortened by UMI.)Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .I85. Source: Masters Abstracts International, Volume: 42-05, page: 1753. Adviser: C. I. Ezeife. Thesis (M.Sc.)--University of Windsor (Canada), 2003