Clustering-Based Materialized View Selection in Data Warehouses

Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited

A data warehouse view selection scheme to accommodate dimension hierarchies.

A data warehouse is a central data repository that supports efficient execution of complex business decision queries. Data warehouse views are aggregations or summary tables holding millions of records integrated from a variety of source data systems. An n-dimensional data cube is a multidimensional data model used to generate n different perspectives of the measure aggregates of interest, and has 2n subviews. Query response time can be significantly improved by pre-computing and storing needed warehouse views. Owing to disk space constraints and increasing maintenance cost of materialized views, pre-computation and storing of all of the required views may not be feasible. Thus, many algorithms have been proposed for selecting only a subset of views of the data cube most beneficial to materialize for better query response time. When taking the warehouse dimension hierarchies into consideration, the view selection problem in a data warehouse gets more complex. The objective of this thesis is to review and contribute a solution to the view-selection problem to accommodate warehouse dimension hierarchies. The proposed selection scheme recommends a set of warehouse views including the dimension subviews with maximum benefits, to materialize in order to improve the query response time in a data warehouse. Source: Masters Abstracts International, Volume: 39-02, page: 0529. Adviser: C. Ezeife. Thesis (M.Sc.)--University of Windsor (Canada), 1998

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

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

RDFViewS: A Storage Tuning Wizard for RDF Applications

In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a specific SPARQL query workload, while taking into account the view maintenance cost and storage space constraints. Our system employs practical algorithms and heuristics to navigate through the search space of potential view configurations, and exploits the possibly available semantic information - expressed via an RDF Schema - to ensure the completeness of the query evaluation