Anfrageoptimierung in Data Warehouses durch Verwendung voraggregierter Views

In der vorliegenden Diplomarbeit wird ein Verfahren vorgestellt, mit dem Aggregatanfragen an ein relational verwaltetes Data Warehouse optimiert werden. Mittels voraggregierter Relationen, welche das Ergebnis von Aggregatanfragen sind und materialisierte Views genannt werden, werden Anfragen an das Data Warehouse analysiert und umgeschrieben. Dafür müssen Metadaten über die Views vorliegen, die in einem umgesetzten Relationenschema verwaltet werden. Darüber hinaus wird eine Kostenfunktion definiert, mit der entschieden wird, welche materialisierte View zur Optimierung verwendet werden soll. Weiterhin wird ein Verfahren erläutert, mit dem materialisierte Views bei Änderungen der Daten des Data Warehouses aktualisiert werden. The diploma thesis presents a technique for optimizing aggregation-queries to relational data warehouses. With the use of preaggregated relations, which are the results of former aggregation-queries and named as materialized views, queries to data warehouses are analyzed and reformulated. Therefore metadata are needed, which are stored in an implemented relation-schema. Furthermore a cost function is defined, that determines the materialized view used for optimization. A maintenance technique for updating materialized views after changing data of the data warehouse is also presented

A role-based access control schema for materialized views

This thesis research presents a framework that enhances security at the level of materialized views. Materialized views can be used for performance reasons in very large systems such as data warehouses or distributed systems, or for providing a filtered selection of data from a more general database. Existing proposed techniques provide rule-based access control for materialized views, however, the administration of such systems is time consuming and cumbersome in a large environment. This thesis presents a role-based access control schema for materialized views in which data authorization rules are associated with roles and defined in Datalog syntax in plain text files, a column level restriction is imposed on a materialized view based on a user assigned role, and a role conflict strategy is defined in which priority is given to each conflicting role in order to resolve role conflicts if a user is gaining authorization for permissions associated with conflicting roles at the same time. KEYWORDS Materialized Views, Authorization Views, Session Roles, Role Conflict

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

ViewDF: a Flexible Framework for Incremental View Maintenance in Stream Data Warehouses

Because of the increasing data sizes and demands for low latency in modern data analysis, the traditional data warehousing technologies are greatly pushed beyond their limits. Several stream data warehouse (SDW) systems, which are warehouses that ingest append-only data feeds and support frequent refresh cycles, have been proposed including different methods to improve the responsiveness of the systems. Materialized views are critical in large-scale data warehouses due to their ability to speed up queries. Thus an SDW maintains layers of materialized views. Materialized view maintenance in SDW systems introduces new challenges. However, some of the existing SDW systems do not address the maintenance of views while others employ view maintenance techniques that are not efficient. This thesis presents ViewDF, a flexible framework for incremental maintenance of materialized views in SDW systems that generalizes existing techniques and enables new optimizations for views defined with operators that are common in stream analytics. We give a special view definition (ViewDF) to enhance the traditional way of creating views in SQL by being able to reference any partition of any table. We describe a prototype system based on this idea, which allows users to write ViewDFs directly and can automatically translate a broad class of queries into ViewDFs. Several optimizations are proposed and experiments show that our proposed system can improve view maintenance time by a factor of two or more in practical settings.1 yea

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