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, that of 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 also describe an alternate utility of the proposed framework, that enables us to identify values that deviate from the underlying data distribution, suitable for data mining purposes. Finally, 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.

Entropy Based Approximate Querying and Exploration of Datacubes

Palpanas, Themistoklis;
2001-01-01

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, that of 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 also describe an alternate utility of the proposed framework, that enables us to identify values that deviate from the underlying data distribution, suitable for data mining purposes. Finally, 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.
2001
Thirteenth International Conference on Scientific and Statistical Database Management 2001
New York
IEEE
0769512186
Palpanas, Themistoklis; N., Koudas
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/73624
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact