Continuous query processing in spatio-temporal databases

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this paper, we aim to develop a framework for continuous query processing in spatio-temporal databases. The proposed framework distinguishes itself from other query processors by employing two main paradigms: (1) Sealability in terms of the number of concurrent continuous spatio-temporal queries. (2) Incremental evaluation of continuous spatio-temporal queries. Scalability is achieved thorough employing a shared execution paradigm. Incremental evaluation is achieved through computing only the updates to the previously reported answer. We distinguish between two types of updates; positive updates and negative updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. The proposed framework is applicable to a wide variety of continuous spatio-temporal queries where we do not have any constraints about the mutability of objects and queries (i.e., both objects and queries can be either stationary or moving) or the movement representation (i.e., movement can be represented either by sampling or trajectory).

Original languageEnglish
Pages (from-to)100-111
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3268
Publication statusPublished - 1 Dec 2004
Externally publishedYes

Fingerprint

Spatio-temporal Databases
Continuous Queries
Query processing
Query Processing
Scalability
Trajectories
Query
Sampling
Update
Paradigm
Evaluation
Concurrent
Trajectory
Computing
Object
Framework

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

@article{f55496ef246646efbd43515b7ff3d00b,
title = "Continuous query processing in spatio-temporal databases",
abstract = "In this paper, we aim to develop a framework for continuous query processing in spatio-temporal databases. The proposed framework distinguishes itself from other query processors by employing two main paradigms: (1) Sealability in terms of the number of concurrent continuous spatio-temporal queries. (2) Incremental evaluation of continuous spatio-temporal queries. Scalability is achieved thorough employing a shared execution paradigm. Incremental evaluation is achieved through computing only the updates to the previously reported answer. We distinguish between two types of updates; positive updates and negative updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. The proposed framework is applicable to a wide variety of continuous spatio-temporal queries where we do not have any constraints about the mutability of objects and queries (i.e., both objects and queries can be either stationary or moving) or the movement representation (i.e., movement can be represented either by sampling or trajectory).",
author = "Mohamed Mokbel",
year = "2004",
month = "12",
day = "1",
language = "English",
volume = "3268",
pages = "100--111",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Continuous query processing in spatio-temporal databases

AU - Mokbel, Mohamed

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In this paper, we aim to develop a framework for continuous query processing in spatio-temporal databases. The proposed framework distinguishes itself from other query processors by employing two main paradigms: (1) Sealability in terms of the number of concurrent continuous spatio-temporal queries. (2) Incremental evaluation of continuous spatio-temporal queries. Scalability is achieved thorough employing a shared execution paradigm. Incremental evaluation is achieved through computing only the updates to the previously reported answer. We distinguish between two types of updates; positive updates and negative updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. The proposed framework is applicable to a wide variety of continuous spatio-temporal queries where we do not have any constraints about the mutability of objects and queries (i.e., both objects and queries can be either stationary or moving) or the movement representation (i.e., movement can be represented either by sampling or trajectory).

AB - In this paper, we aim to develop a framework for continuous query processing in spatio-temporal databases. The proposed framework distinguishes itself from other query processors by employing two main paradigms: (1) Sealability in terms of the number of concurrent continuous spatio-temporal queries. (2) Incremental evaluation of continuous spatio-temporal queries. Scalability is achieved thorough employing a shared execution paradigm. Incremental evaluation is achieved through computing only the updates to the previously reported answer. We distinguish between two types of updates; positive updates and negative updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. The proposed framework is applicable to a wide variety of continuous spatio-temporal queries where we do not have any constraints about the mutability of objects and queries (i.e., both objects and queries can be either stationary or moving) or the movement representation (i.e., movement can be represented either by sampling or trajectory).

UR - http://www.scopus.com/inward/record.url?scp=35048871235&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048871235&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35048871235

VL - 3268

SP - 100

EP - 111

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -