Pre-Aggregation with Probability Distributions

Igor Timko; Curtis E. Dyreson; Torben Bach Pedersen

Pre-Aggregation with Probability Distributions

Igor Timko, Curtis E. Dyreson, Torben Bach Pedersen

Department of Computer Science

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

8 Citations (Scopus)

Abstract

Motivated by the increasing need to analyze complex, uncertain multidimensional data this paper proposes probabilistic OLAP queries that are computed using probability distributions rather than atomic values. The paper describes how to create probability distributions from base data, and how the distributions can be subsequently used in pre-aggregation.
Since the probability distributions can become large, we show how to achieve good time and space efficiency by approximating the distributions. We present the results of
several experiments that demonstrate the effectiveness of our methods. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services. This paper is the first to consider the approximate processing of probabilistic OLAP queries over probability distributions.

Original language	English
Title of host publication	Data Warehousing and OLAP : Proceedings of the 9th ACM international workshop on Data warehousing and OLAP 2006, Arlington, Virginia, USA
Number of pages	8
Publisher	Association for Computing Machinery
Publication date	2006
Pages	35-42
ISBN (Print)	1595935304
Publication status	Published - 2006
Event	ACM Ninth International Workshop on Data Warehousing and OLAP - Arlington, Va, United States Duration: 10 Nov 2006 → 10 Nov 2006 Conference number: 9

Conference

Conference	ACM Ninth International Workshop on Data Warehousing and OLAP
Number	9
Country/Territory	United States
City	Arlington, Va
Period	10/11/2006 → 10/11/2006

Keywords

OLAP
Pre-aggregation
probability

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Timko, I., Dyreson, C. E., & Pedersen, T. B. (2006). Pre-Aggregation with Probability Distributions. In Data Warehousing and OLAP: Proceedings of the 9th ACM international workshop on Data warehousing and OLAP 2006, Arlington, Virginia, USA (pp. 35-42). Association for Computing Machinery. http://portal.acm.org/citation.cfm?id=1183520&jmp=cit&coll=portal&dl=ACM&CFID=11021540&CFTOKEN=11212676#CIT

@inproceedings{1cf156e0a08311db8ed6000ea68e967b,

title = "Pre-Aggregation with Probability Distributions",

abstract = "Motivated by the increasing need to analyze complex, uncertain multidimensional data this paper proposes probabilistic OLAP queries that are computed using probability distributions rather than atomic values. The paper describes how to create probability distributions from base data, and how the distributions can be subsequently used in pre-aggregation.Since the probability distributions can become large, we show how to achieve good time and space efficiency by approximating the distributions. We present the results ofseveral experiments that demonstrate the effectiveness of our methods. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services. This paper is the first to consider the approximate processing of probabilistic OLAP queries over probability distributions.",

keywords = "OLAP, OLAP, Pre-aggregation, probability",

author = "Igor Timko and Dyreson, {Curtis E.} and Pedersen, {Torben Bach}",

year = "2006",

language = "English",

isbn = "1595935304",

pages = "35--42",

booktitle = "Data Warehousing and OLAP",

publisher = "Association for Computing Machinery",

address = "United States",

note = "ACM Ninth International Workshop on Data Warehousing and OLAP ; Conference date: 10-11-2006 Through 10-11-2006",

}

Timko, I, Dyreson, CE & Pedersen, TB 2006, Pre-Aggregation with Probability Distributions. in Data Warehousing and OLAP: Proceedings of the 9th ACM international workshop on Data warehousing and OLAP 2006, Arlington, Virginia, USA. Association for Computing Machinery, pp. 35-42, ACM Ninth International Workshop on Data Warehousing and OLAP, Arlington, Va, United States, 10/11/2006. <http://portal.acm.org/citation.cfm?id=1183520&jmp=cit&coll=portal&dl=ACM&CFID=11021540&CFTOKEN=11212676#CIT>

Pre-Aggregation with Probability Distributions. / Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach.
Data Warehousing and OLAP: Proceedings of the 9th ACM international workshop on Data warehousing and OLAP 2006, Arlington, Virginia, USA. Association for Computing Machinery, 2006. p. 35-42.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Pre-Aggregation with Probability Distributions

AU - Timko, Igor

AU - Dyreson, Curtis E.

AU - Pedersen, Torben Bach

N1 - Conference code: 9

PY - 2006

Y1 - 2006

N2 - Motivated by the increasing need to analyze complex, uncertain multidimensional data this paper proposes probabilistic OLAP queries that are computed using probability distributions rather than atomic values. The paper describes how to create probability distributions from base data, and how the distributions can be subsequently used in pre-aggregation.Since the probability distributions can become large, we show how to achieve good time and space efficiency by approximating the distributions. We present the results ofseveral experiments that demonstrate the effectiveness of our methods. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services. This paper is the first to consider the approximate processing of probabilistic OLAP queries over probability distributions.

AB - Motivated by the increasing need to analyze complex, uncertain multidimensional data this paper proposes probabilistic OLAP queries that are computed using probability distributions rather than atomic values. The paper describes how to create probability distributions from base data, and how the distributions can be subsequently used in pre-aggregation.Since the probability distributions can become large, we show how to achieve good time and space efficiency by approximating the distributions. We present the results ofseveral experiments that demonstrate the effectiveness of our methods. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services. This paper is the first to consider the approximate processing of probabilistic OLAP queries over probability distributions.

KW - OLAP

KW - Pre-aggregation

KW - probability

M3 - Article in proceeding

SN - 1595935304

SP - 35

EP - 42

BT - Data Warehousing and OLAP

PB - Association for Computing Machinery

T2 - ACM Ninth International Workshop on Data Warehousing and OLAP

Y2 - 10 November 2006 through 10 November 2006

ER -

Pre-Aggregation with Probability Distributions

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Keywords

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