Implementing data cubes efficiently

Harinarayan, Venky; Rajaraman, Anand; Ullman, Jeffrey D.

doi:10.1145/233269.233333

Cited by 805 publications

(618 citation statements)

References 6 publications

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“…Picking a reasonable value for α will balance the trade-off between reducing the wait time and increasing the tune and processing times. As in [14], we are assuming a linear cost model for aggregate query processing, where a table scan is required to compute the result. During processing, the processor accesses the downloaded summary table from memory and the memory transfer rate determines the time taken for processing.…”

Section: Discussionmentioning

confidence: 99%

“…The objective is to select the appropriate set of tables for storing (materialization), so that to speed up future query processing, while meeting the space constraints [12][13][14]. To facilitate the selection process, the search lattice was introduced in [14].…”

Section: Olap and Summary Tablesmentioning

confidence: 99%

“…We are assuming that all the lattice subcubes are pre-computed and stored at the server, which is a reasonable assumption, specially for relatively small size data marts. The Essbase system (according to [14]) is an example of commercial product that materialize all the possible summary tables.…”

Section: Mobile Olap Modelmentioning

confidence: 99%

“…Decision makers need to count on up-to-date, business-critical data being instantly available to their hand-held and wireless computers. Such data are maintained in data warehouses or data marts [14] and may be privately or publicly accessible. For example, a public data mart deployed within the stock market galleries or the campus of a trade fair may be responsible for gathering stock market and other financial information.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Olap and Summary Tablesmentioning

confidence: 99%

Section: Mobile Olap Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On-Demand Data Broadcasting for Mobile Decision Making

Sharaf

Chrysanthis

2004

Mobile Networks and Applications

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“…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. The introduction of the CUBE operator generated a significant level of interest in techniques for efficient computation and support of this operator [1,2,8,9,10,12,13]. These techniques do not concentrate on efficient range queries, but rather on which cuboids to pre-compute and how to efficiently access them (e.g., using index structures).…”

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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Multidimensional Data Model and Query Language for Informetrics

Niemi

Hirvonen

Järvelin

2003

J. Am. Soc. Inf. Sci.

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Multidimensional data analysis or On-line analytical processing (OLAP) offers a single subject-oriented source for analyzing summary data based on various dimensions. We demonstrate that the OLAP approach gives a promising starting point for advanced analysis and comparison among summary data in informetrics applications. At the moment there is no single precise, commonly accepted logical/conceptual model for multidimensional analysis. This is because the requirements of applications vary considerably. We develop a conceptual/logical multidimensional model for supporting the complex and unpredictable needs of informetrics. Summary data are considered with respect of some dimensions. By changing dimensions the user may construct other views on the same summary data. We develop a multidimensional query language whose basic idea is to support the definition of views in a way, which is natural and intuitive for lay users in the informetrics area. We show that this view-oriented query language has a great expressive power and its degree of declarativity is greater than in contemporary operation-oriented or SQL (Structured Query Language)-like OLAP query languages.

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Implementing data cubes efficiently

Cited by 805 publications

References 6 publications

On-Demand Data Broadcasting for Mobile Decision Making

On-Demand Data Broadcasting for Mobile Decision Making

Flexible Data Cubes for Online Aggregation

Multidimensional Data Model and Query Language for Informetrics

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