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187
Towards the Building of a DenseRegionBased OLAP System
, 2001
"... Online Analytical Processing (OLAP) has become a very useful tool in decision support systems built on data warehouses. ROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP) are two popular approaches for building OLAP systems. These two approaches have very different performance characteris ..."
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Cited by 5 (0 self)
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to build a high performance and space efficient denseregionbased data cube. In this pa...
Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets
"... Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, a ..."
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Cited by 198 (3 self)
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and spaceefficient representation of the underlying multidimensional array, based upon a multiresolution wavelet decomposition. In the online phase, each aggregation query can generally be answered using the compact data cube in one I/O or a small number of I/Os, depending upon the desired accuracy. We
An ArrayBased Algorithm for Simultaneous Multidimensional Aggregates
 In Proc. 1997 ACMSIGMOD Int. Conf. Management of Data
, 1997
"... Computing multiple related groupbys and aggregates is one of the core operations of OnLine Analytical Processing (OLAP) applications. Recently, Gray et al. [GBLP95] proposed the "Cube" operator, which computes groupby aggregations over all possible subsets of the specified dimensions. T ..."
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Cited by 189 (3 self)
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Computing multiple related groupbys and aggregates is one of the core operations of OnLine Analytical Processing (OLAP) applications. Recently, Gray et al. [GBLP95] proposed the "Cube" operator, which computes groupby aggregations over all possible subsets of the specified dimensions
Discoverydriven Exploration of OLAP Data Cubes
 In Proc. Int. Conf. of Extending Database Technology (EDBT'98
, 1998
"... . Analysts predominantly use OLAP data cubes to identify regions of anomalies that may represent problem areas or new opportunities. The current OLAP systems support hypothesisdriven exploration of data cubes through operations such as drilldown, rollup, and selection. Using these operations, an ..."
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Cited by 110 (2 self)
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the analyst to interesting regions of the cube during navigation. We present the statistical foundation underlying our approach. We then discuss the computational issue of finding exceptions in data and making the process efficient on large multidimensional data bases. 1 Introduction OnLine Analytical
Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions
, 1988
"... Efficiently answering decision support queries is an important problem. Most of the work in this direction has been in the context of the data cube. Queries are efficiently answered by precomputing large parts of the cube. Besides having large space requirements, such precomputation requires that ..."
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Cited by 63 (4 self)
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with the aggregation function of interest, is fixed for each cube. Queries over more than one target measure or using different aggregation functions, would require precomputing larger data cubes. In this paper, we propose a new compressed representation of the data cube that (a) drastically reduces storage
Data Cube Representation for Vehicle Insurance
"... OnLine Analytical Processing (OLAP) systems have a strong focus on the interactive analysis of data and typically provide extensive capabilities for visualizing the data and generating summary statistics. Most of the data sets can be represented as a table, where each row is an object and each colu ..."
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column is an attribute. Data cube represents the multidimensional data with all possible aggregates. The three dimensional data cubes represent the different attributes entirely controlled with the help of objects. In general, a data cube is generalization of statistical terminology as a cross
IceCube: Efficient Targeted Mining in Data Cubes
"... Abstract—We address the problem of mining targeted association rules over multidimensional marketbasket data. Here, each transaction has, in addition to the set of purchased items, ancillary dimension attributes associated with it. Based on these dimensions, transactions can be visualized as distr ..."
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as distributed over cells of an ndimensional cube. In this framework, a targeted association rule is of the form {X → Y}R, where R is a convex region in the cube and X → Y is a traditional association rule within region R. We first describe the TOARM algorithm, based on classical techniques, for identifying
pCube: UpdateEfficient Online Aggregation with Progressive Feedback and Error Bounds
 In Proceedings of the Twelfth International Conference on Scientific and Statistical Database Management
, 2000
"... Multidimensional data cubes are used in large data warehouses as a tool for online aggregation of information. As the number of dimensions increases, supporting efficient queries as well as updates to the data cube becomes difficult. Another problem that arises with increased dimensionality is the s ..."
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Cited by 5 (1 self)
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Multidimensional data cubes are used in large data warehouses as a tool for online aggregation of information. As the number of dimensions increases, supporting efficient queries as well as updates to the data cube becomes difficult. Another problem that arises with increased dimensionality
Range cube: Efficient cube computation by exploiting data correlation
 In ICDE 2004
, 2004
"... Data cube computation and representation are prohibitively expensive in terms of time and space. Prior work has focused on either reducing the computation time or condensing the representation of a data cube. In this paper, we introduce Range Cubing as an efficient way to compute and compress the da ..."
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Cited by 8 (1 self)
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Data cube computation and representation are prohibitively expensive in terms of time and space. Prior work has focused on either reducing the computation time or condensing the representation of a data cube. In this paper, we introduce Range Cubing as an efficient way to compute and compress
DROLAP  A DenseRegion Based Approach to Online Analytical Processing
, 1999
"... ROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP) are two opposing techniques for building Online Analytical Processing (OLAP) systems. MOLAP has good query performance but suffers when the data distribution in the multidimensional data cube is sparse. ROLAP can be built on mature RDBMS tec ..."
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technology but its performance is not as competitive. Many data warehouses contain sparse but clustered multidimensional data. We propose a denseregionbased OLAP (DROLAP) system which surpasses both ROLAP and MOLAP in space efficiency and query performance. DROLAP applies the MOLAP approach on the dense
Results 1  10
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187