2010 | OriginalPaper | Buchkapitel
OLAP Data Cube Compression Techniques: A Ten-Year-Long History
verfasst von : Alfredo Cuzzocrea
Erschienen in: Future Generation Information Technology
Verlag: Springer Berlin Heidelberg
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OnLine Analytical Processing
(OLAP) is relevant for a plethora of
Intelligent Data Analysis and Mining Applications and Systems
, as it offers powerful tools for exploring, querying and mining massive amounts of data on the basis of fortunate and well-consolidated multidimensional and a multi-resolution metaphors over data. Applicative settings for which OLAP plays a critical role are manyfold, and span from
Business Intelligence
to
Complex Information Retrieval
and
Sensor and Stream Data Analysis
. Recently, the Database and Data Warehousing research community has experienced an explosion of OLAP-related methodologies and techniques aimed at improving the capabilities and the opportunities of complex mining processes over heterogeneous-in-nature, inter-related and massive data repositories. Despite this, open problems still arise, among which the so-called
curse of dimensionality problem
plays a major role. This problem refers to well-understood limitations of state-of-the-art OLAP data processing techniques in elaborating, querying and mining multidimensional data when data cubes grow in size and dimension number. This evidence has originated a large spectrum of research efforts in the context of
Approximate OLAP Query Answering
techniques, whose main idea consists in
compressing target data cubes in order to originate compressed data structures able of retrieving approximate answers to OLAP queries at a tolerable query error
. This research proposes an excerpt of a ten-year-long history of OLAP data cube compression techniques, by particularly focusing on three major results, namely Δ−
Syn
,
K
LSA
and
$\mathcal{LCS}-Hist$
.