Data cube: a relational aggregation operator generalizing GROUP-BY, CROSS-TAB, and SUB-TOTALS

Gray, Jim; Chaudhuri, Surajit; Bosworth, Adam; Layman, Andrew; Reichart, Don; Venkatrao, Murali; Pellow, Frank; Pirahesh, Hamid

doi:10.1109/icde.1996.492099

Cited by 879 publications

(1,045 citation statements)

References 2 publications

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“…Users, for analysis purposes, commonly inquire about the values of aggregates, like the total sales of jeans in the state of New York. Such aggregates can be computed using the datacube operator [17], and queries of this kind can be efficiently supported.…”

Section: Introductionmentioning

confidence: 99%

Using datacube aggregates for approximate querying and deviation detection

Palpanas

Koudas

Mendelzon

2005

IEEE Trans. Knowl. Data Eng.

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Abstract-Much research has been devoted to the efficient computation of relational aggregations and, specifically, the efficient execution of the datacube operation. In this paper, we consider the inverse problem, that of deriving (approximately) the original data from the aggregates. We motivate this problem in the context of two specific application areas, approximate query answering and data analysis. We propose a framework based on the notion of information entropy that enables us to estimate the original values in a data set, given only aggregated information about it. We then show how approximate queries on the data from which the aggregates were derived can be performed using our framework. We also describe an alternate use of the proposed framework that enables us to identify values that deviate from the underlying data distribution, suitable for data mining purposes. We present a detailed performance study of the algorithms using both real and synthetic data, highlighting the benefits of our approach as well as the efficiency of the proposed solutions. Finally, we evaluate our techniques with a case study on a real data set, which illustrates the applicability of our approach.

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Section: Introductionmentioning

confidence: 99%

Using datacube aggregates for approximate querying and deviation detection

Palpanas

Koudas

Mendelzon

2005

IEEE Trans. Knowl. Data Eng.

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“…Our notion of a data cube is slightly different from the terminology in [7]. More precisely, the cuboids generated by CUBE (i.e, the results of grouping the data by subsets of the dimensions) are data cubes as defined in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…In [7] a new SQL operator, CUBE or "data cube", was proposed to support online aggregation by pre-computing query results for queries that involve grouping operations (GROUP BY). Our notion of a data cube is slightly different from the terminology in [7].…”

Section: Related Workmentioning

confidence: 99%

Flexible Data Cubes for Online Aggregation

Riedewald

Agrawal

Abbadi

2001

Database Theory — ICDT 2001

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Abstract. Applications like Online Analytical Processing depend heavily on the ability to quickly summarize large amounts of information. Techniques were proposed recently that speed up aggregate range queries on MOLAP data cubes by storing pre-computed aggregates. These approaches try to handle data cubes of any dimensionality by dealing with all dimensions at the same time and treat the different dimensions uniformly. The algorithms are typically complex, and it is difficult to prove their correctness and to analyze their performance. We present a new technique to generate Iterative Data Cubes (IDC) that addresses these problems. The proposed approach provides a modular framework for combining one-dimensional aggregation techniques to create spaceoptimal high-dimensional data cubes. A large variety of cost tradeoffs for high-dimensional IDC can be generated, making it easy to find the right configuration based on the application requirements.

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“…The needed consolidated data by an OLAP query can be derived using the data cube operator [11]. The data cube operator is basically the union of all possible Group-By operators applied on the fact table.…”

Section: Olap and Summary Tablesmentioning

confidence: 99%

On-Demand Data Broadcasting for Mobile Decision Making

Sharaf

Chrysanthis

2004

Mobile Networks and Applications

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Abstract. The wide spread of mobile computing devices is transforming the newly emerged e-business world into a mobile e-business one, a world in which hand-held computers are the user's front-ends to access enterprise data. For good mobile decision making, users need to count on up-to-date, business-critical data. Such data are typically in the form of summarized information tailored to suit the user's analysis interests. In this paper, we are addressing the issue of time and energy efficient delivery of summary tables to mobile users with hand-held computers equipped with OLAP (On-Line Analytical Processing) front-end tools. Towards this, we propose a new on-demand scheduling algorithm, called STOBS, that exploits the derivation semantics among OLAP summary tables. It maximizes the aggregated data sharing between mobile users and reduces the broadcast length for satisfying a set of requests compared to the already existing techniques. The algorithm effectiveness with respect to access time and energy consumption is evaluated using simulation.

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Data cube: a relational aggregation operator generalizing GROUP-BY, CROSS-TAB, and SUB-TOTALS

Cited by 879 publications

References 2 publications

Using datacube aggregates for approximate querying and deviation detection

Using datacube aggregates for approximate querying and deviation detection

Flexible Data Cubes for Online Aggregation

On-Demand Data Broadcasting for Mobile Decision Making

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