Spatiotemporal aggregate computation: A survey

Inés Fernando Vega López, Richard Thomas Snodgrass, Bongki Moon

Research output: Contribution to journalArticle

99 Citations (Scopus)

Abstract

Spatiotemporal databases are becoming increasingly more common. Typically, applications modeling spatiotemporal objects need to process vast amounts of data. In such cases, generating aggregate information from the data set is more useful than individually analyzing every entry. In this paper, we study the most relevant techniques for the evaluation of aggregate queries on spatial, temporal, and spatiotemporal data. We also present a model that reduces the evaluation of aggregate queries to the problem of selecting qualifying tuples and the grouping of these tuples into collections on which an aggregate function is to be applied. This model give us a framework that allows us to analyze and compare the different existing techniques for the evaluation of aggregate queries. At the same time, it allows us to identify opportunities for research on types of aggregate queries that have not been studied.

Original languageEnglish (US)
Pages (from-to)271-286
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume17
Issue number2
DOIs
StatePublished - Feb 2005

Keywords

  • Aggregate function
  • Aggregation queries
  • Spatiotemporal databases

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Information Systems

Cite this

Spatiotemporal aggregate computation : A survey. / López, Inés Fernando Vega; Snodgrass, Richard Thomas; Moon, Bongki.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 2, 02.2005, p. 271-286.

Research output: Contribution to journalArticle

López, Inés Fernando Vega ; Snodgrass, Richard Thomas ; Moon, Bongki. / Spatiotemporal aggregate computation : A survey. In: IEEE Transactions on Knowledge and Data Engineering. 2005 ; Vol. 17, No. 2. pp. 271-286.
@article{8eeb3cb9bda746718287f2d02c9dfe4b,
title = "Spatiotemporal aggregate computation: A survey",
abstract = "Spatiotemporal databases are becoming increasingly more common. Typically, applications modeling spatiotemporal objects need to process vast amounts of data. In such cases, generating aggregate information from the data set is more useful than individually analyzing every entry. In this paper, we study the most relevant techniques for the evaluation of aggregate queries on spatial, temporal, and spatiotemporal data. We also present a model that reduces the evaluation of aggregate queries to the problem of selecting qualifying tuples and the grouping of these tuples into collections on which an aggregate function is to be applied. This model give us a framework that allows us to analyze and compare the different existing techniques for the evaluation of aggregate queries. At the same time, it allows us to identify opportunities for research on types of aggregate queries that have not been studied.",
keywords = "Aggregate function, Aggregation queries, Spatiotemporal databases",
author = "L{\'o}pez, {In{\'e}s Fernando Vega} and Snodgrass, {Richard Thomas} and Bongki Moon",
year = "2005",
month = "2",
doi = "10.1109/TKDE.2005.34",
language = "English (US)",
volume = "17",
pages = "271--286",
journal = "IEEE Transactions on Knowledge and Data Engineering",
issn = "1041-4347",
publisher = "IEEE Computer Society",
number = "2",

}

TY - JOUR

T1 - Spatiotemporal aggregate computation

T2 - A survey

AU - López, Inés Fernando Vega

AU - Snodgrass, Richard Thomas

AU - Moon, Bongki

PY - 2005/2

Y1 - 2005/2

N2 - Spatiotemporal databases are becoming increasingly more common. Typically, applications modeling spatiotemporal objects need to process vast amounts of data. In such cases, generating aggregate information from the data set is more useful than individually analyzing every entry. In this paper, we study the most relevant techniques for the evaluation of aggregate queries on spatial, temporal, and spatiotemporal data. We also present a model that reduces the evaluation of aggregate queries to the problem of selecting qualifying tuples and the grouping of these tuples into collections on which an aggregate function is to be applied. This model give us a framework that allows us to analyze and compare the different existing techniques for the evaluation of aggregate queries. At the same time, it allows us to identify opportunities for research on types of aggregate queries that have not been studied.

AB - Spatiotemporal databases are becoming increasingly more common. Typically, applications modeling spatiotemporal objects need to process vast amounts of data. In such cases, generating aggregate information from the data set is more useful than individually analyzing every entry. In this paper, we study the most relevant techniques for the evaluation of aggregate queries on spatial, temporal, and spatiotemporal data. We also present a model that reduces the evaluation of aggregate queries to the problem of selecting qualifying tuples and the grouping of these tuples into collections on which an aggregate function is to be applied. This model give us a framework that allows us to analyze and compare the different existing techniques for the evaluation of aggregate queries. At the same time, it allows us to identify opportunities for research on types of aggregate queries that have not been studied.

KW - Aggregate function

KW - Aggregation queries

KW - Spatiotemporal databases

UR - http://www.scopus.com/inward/record.url?scp=14644390243&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=14644390243&partnerID=8YFLogxK

U2 - 10.1109/TKDE.2005.34

DO - 10.1109/TKDE.2005.34

M3 - Article

AN - SCOPUS:14644390243

VL - 17

SP - 271

EP - 286

JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

SN - 1041-4347

IS - 2

ER -