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Implementing data cubes efficiently
 In SIGMOD
, 1996
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
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Cited by 500 (1 self)
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Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube..A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query. 1
Querying Multidimensional Databases
 In Sixth Int. Workshop on Database Programming Languages
, 1997
"... . Multidimensional databases are large collections of data, often historical, used for sophisticated analysis oriented to decision making. This activity is supported by an emerging category of software technology, called OnLine Analytical Processing #OLAP#. In spite of a lot of commercial tools ..."
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Cited by 48 (3 self)
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. Multidimensional databases are large collections of data, often historical, used for sophisticated analysis oriented to decision making. This activity is supported by an emerging category of software technology, called OnLine Analytical Processing #OLAP#. In spite of a lot of commercial tools already available, a fundamental study for OLAP systems is still lacking. In this paper weintroduce a model and a query language to establish a theoretical basis for multidimensional data. The model is based on the notions of dimension and ftable. Dimensions are linguistic categories corresponding to di#erentways of looking at the information. Ftables are the constructs used to represent factual data, and are the logical counterpart of multidimensional arrays, the way in which current analytical tools store data. The query language is a calculus for ftables, and as such it o#ers a highlevel support to multidimensional data analysis. Scalar and aggregate functions can be embe...
Research issues in data warehousing
 In Datenbanksysteme in Buro, Technik und Wissenschaft
, 1997
"... Abstract. Data warehousing is a booming industry with many interesting research problems. The database research community has concentrated on only a few aspects. In this paper, We summarize the state of the art, suggest architectural extensions and identify research problems in the areas of warehous ..."
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Cited by 21 (0 self)
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Abstract. Data warehousing is a booming industry with many interesting research problems. The database research community has concentrated on only a few aspects. In this paper, We summarize the state of the art, suggest architectural extensions and identify research problems in the areas of warehouse modeling and design, data cleansing and loading, data refreshing and purging, metadata management, extensions to relational operators, alternative implementations of traditional relational operators, special index structures and query optimization with aggregates. 1
Abstract Implementing Data Cubes Efficiently*
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
Abstract
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Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube..A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query. 1
A Systematic Approach to Multidimensional Databases?
"... Abstract. Multidimensional databases are large collections of data, often historical, used for sophisticated analysis oriented to decision making. This activity is supported by an emerging category of software technology, called OnLine Analytical Processing (OLAP). In spite of a lot of commercial t ..."
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Abstract. Multidimensional databases are large collections of data, often historical, used for sophisticated analysis oriented to decision making. This activity is supported by an emerging category of software technology, called OnLine Analytical Processing (OLAP). In spite of a lot of commercial tools already available, a fundamental study for OLAP systems is still lacking. In this paper we introduce a model and a query language to establish a theoretical basis for multidimensional data. The model is based on the notions of dimension and ftable. Dimensions are linguistic categories corresponding to di erent ways of looking at the information. Ftables are the constructs used to represent factual data, and are the logical counterpart of multidimensional arrays, the way in which current analytical tools store data. The query language is a calculus for ftables, and as such it o ers a highlevel support to multidimensional data analysis. Scalar and aggregate functions can be embedded in calculus expressions in a natural way. We compare our model and language with other approaches, and discuss on several issues related to multidimensional databases. Finally, we identify important research topics that need to be investigated in this context. 1