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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.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/7625
Date January 2013
CreatorsYang, Yuke
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation

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