A B S T R A C T
OLAP cubes enable aggregation-centric analysis of transactional data by shaping data
records into measurable facts with dimensional characteristics. A multidimensional view
is obtained from the available data fields and explicit relationships between them. This
classical modeling approach is not feasible for scenarios dealing with semi-structured or
poorly structured data. We propose to the data warehouse design methodology with a
content-driven discovery of measures and dimensions in the original dataset. Our
approach is based on introducing a data enrichment layer responsible for detecting new
structural elements in the data using data mining and other techniques. Discovered
elements can be of type measure, dimension, or hierarchy level and may represent static
or even dynamic properties of the data. This paper focuses on the challenge of generating,
maintaining, and querying discovered elements in OLAP cubes.
We demonstrate the power of our approach by providing OLAP to the public stream of
user-generated content on the Twitter platform. We have been able to enrich the original
set with dynamic characteristics, such as user activity, popularity, messaging behavior, as
well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge
discovery techniques coupled with human expertise enable structural enrichment of the
original data beyond the scope of the existing methods for obtaining multidimensional
models from relational or semi-structured data.
& 2013 Elsevier Ltd. All rights reserved.
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کد محصول : شماره 50
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