Campus Event Calendar
Event Entry
What and Who
Cube-Lifecycle Management and Applications
Konstantinos Morfonios
University of Athens
Talk
AG 5, RG2
AG Audience
English
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Date, Time and Location
Thursday, 10 July 2008
11:15
30 Minutes
E1 4
433
Saarbrücken
Abstract
A common operation involved with the majority of algorithms relevant to
On-Line Analytical Processing (OLAP) is aggregation, which can be extremely
time-consuming if applied over large datasets. To overcome this drawback,
scientists have proposed the precomputation and materialization of a large
volume of aggregated data into a structure called data cube. Nevertheless,
the construction and usage of the data cube itself has been found very
demanding in terms of computational and storage resources. We study this
problem in depth, taking into account all the phases of a cube's lifecycle,
and propose comprehensive suites of scalable algorithms that perform
efficient cube construction, storage, query processing, incremental
updating, indexing, and caching. Our experimental results indicate that the
proposed solutions are viable even when applied over very large datasets
with arbitrary hierarchies. Finally, we briefly show how data cubes can be
used for various applications, including data mining and web search.
On-Line Analytical Processing (OLAP) is aggregation, which can be extremely
time-consuming if applied over large datasets. To overcome this drawback,
scientists have proposed the precomputation and materialization of a large
volume of aggregated data into a structure called data cube. Nevertheless,
the construction and usage of the data cube itself has been found very
demanding in terms of computational and storage resources. We study this
problem in depth, taking into account all the phases of a cube's lifecycle,
and propose comprehensive suites of scalable algorithms that perform
efficient cube construction, storage, query processing, incremental
updating, indexing, and caching. Our experimental results indicate that the
proposed solutions are viable even when applied over very large datasets
with arbitrary hierarchies. Finally, we briefly show how data cubes can be
used for various applications, including data mining and web search.
Contact
Gerhard Weikum
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Petra Schaaf, 07/02/2008 10:05
Petra Schaaf, 07/02/2008 10:02 -- Created document.
Petra Schaaf, 07/02/2008 10:02 -- Created document.