Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Computational Intelligence - 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Proceedings |
| Pages | 580-593 |
| Number of pages | 14 |
| Edition | PART 2 |
| Publication status | Published - 2012 |
| Event | 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012 - Catania, Italy Duration: 9 Jul 2012 → 13 Jul 2012 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Number | PART 2 |
| Volume | 298 CCIS |
| ISSN (Print) | 1865-0929 |
Abstract
In this paper, we complement previous research results provided in [8], where the multiple-objective OLAP data cube compression paradigm has been introduced. This paradigm pursues the idea of compressing OLAP data cubes in the dependence of multiple requirements rather than only one, like in traditional approaches. Here, we provide a comprehensive description of algorithm computeMQHist, the main algorithm of the framework [8], which allows us to obtain compressed data cubes that adhere to the multiple-objective computational paradigm, and we prove that computeMQHist has a polynomial asymptotic complexity.
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- General Mathematics
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Advances in Computational Intelligence - 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Proceedings. PART 2. ed. 2012. p. 580-593 (Communications in Computer and Information Science; Vol. 298 CCIS, No. PART 2).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Polynomial asymptotic complexity of multiple-objective OLAP data cube compression
AU - Cuzzocrea, Alfredo
AU - Fisichella, Marco
PY - 2012
Y1 - 2012
N2 - In this paper, we complement previous research results provided in [8], where the multiple-objective OLAP data cube compression paradigm has been introduced. This paradigm pursues the idea of compressing OLAP data cubes in the dependence of multiple requirements rather than only one, like in traditional approaches. Here, we provide a comprehensive description of algorithm computeMQHist, the main algorithm of the framework [8], which allows us to obtain compressed data cubes that adhere to the multiple-objective computational paradigm, and we prove that computeMQHist has a polynomial asymptotic complexity.
AB - In this paper, we complement previous research results provided in [8], where the multiple-objective OLAP data cube compression paradigm has been introduced. This paradigm pursues the idea of compressing OLAP data cubes in the dependence of multiple requirements rather than only one, like in traditional approaches. Here, we provide a comprehensive description of algorithm computeMQHist, the main algorithm of the framework [8], which allows us to obtain compressed data cubes that adhere to the multiple-objective computational paradigm, and we prove that computeMQHist has a polynomial asymptotic complexity.
UR - http://www.scopus.com/inward/record.url?scp=84868270651&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31715-6_61
DO - 10.1007/978-3-642-31715-6_61
M3 - Conference contribution
AN - SCOPUS:84868270651
SN - 9783642317149
T3 - Communications in Computer and Information Science
SP - 580
EP - 593
BT - Advances in Computational Intelligence - 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Proceedings
T2 - 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012
Y2 - 9 July 2012 through 13 July 2012
ER -