Large Scale Reasoning Using Allen's Interval Algebra

Matthew Mantle; Sotirios Batsakis; Grigoris Antoniou

doi:10.1007/978-3-319-62428-0_3

Large Scale Reasoning Using Allen's Interval Algebra

Matthew Mantle, Sotirios Batsakis, Grigoris Antoniou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

This paper proposes and evaluates a distributed, parallel approach for reasoning over large scale datasets using Allen's Interval Algebra (IA). We have developed and implemented algorithms that reason over IA networks using the Spark distributed processing framework. Experiments have been conducted by deploying the algorithms on computer clusters using synthetic datasets with various characteristics. We show that reasoning over datasets consisting of millions of interval relations is feasible and that our implementation scales effectively. The size of the IA networks we are able to reason over is far greater than those found in previously published works.

Original language	English
Title of host publication	Advances in Soft Computing
Subtitle of host publication	15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part II
Editors	Obdulia Pichardo-Lagunas, Sabino Miranda-Jiménez
Publisher	Springer, Cham
Pages	29-41
Number of pages	13
Volume	LNCS 10062
ISBN (Electronic)	9783319624280
ISBN (Print)	9783319624273
DOIs	https://doi.org/10.1007/978-3-319-62428-0_3
Publication status	Published - 2 Aug 2017
Event	Mexican International Conference on Artificial Intelligence - Cancun, Mexico Duration: 23 Oct 2016 → 29 Oct 2016 Conference number: 15 http://www.micai.org/2016/ (Link to Conference Website)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	LNCS 10062
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Mexican International Conference on Artificial Intelligence
Abbreviated title	MCIAI
Country/Territory	Mexico
City	Cancun
Period	23/10/16 → 29/10/16
Internet address	http://www.micai.org/2016/ (Link to Conference Website)

Access to Document

10.1007/978-3-319-62428-0_3Licence: Unspecified

http://eprints.hud.ac.uk/id/eprint/29804/

Matthew Mantle
- M.e.Mantlehud.acuk
- Department of Computer Science - Senior Lecturer
- Centre for Planning, Autonomy and Representation of Knowledge
- Centre for Autonomous and Intelligent Systems - Member
Person: Academic

Cite this

Mantle, M., Batsakis, S., & Antoniou, G. (2017). Large Scale Reasoning Using Allen's Interval Algebra. In O. Pichardo-Lagunas, & S. Miranda-Jiménez (Eds.), Advances in Soft Computing: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part II (Vol. LNCS 10062, pp. 29-41). (Lecture Notes in Computer Science; Vol. LNCS 10062). Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_3

Mantle, Matthew ; Batsakis, Sotirios ; Antoniou, Grigoris. / Large Scale Reasoning Using Allen's Interval Algebra. Advances in Soft Computing: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part II. editor / Obdulia Pichardo-Lagunas ; Sabino Miranda-Jiménez. Vol. LNCS 10062 Springer, Cham, 2017. pp. 29-41 (Lecture Notes in Computer Science).

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title = "Large Scale Reasoning Using Allen's Interval Algebra",

abstract = "This paper proposes and evaluates a distributed, parallel approach for reasoning over large scale datasets using Allen's Interval Algebra (IA). We have developed and implemented algorithms that reason over IA networks using the Spark distributed processing framework. Experiments have been conducted by deploying the algorithms on computer clusters using synthetic datasets with various characteristics. We show that reasoning over datasets consisting of millions of interval relations is feasible and that our implementation scales effectively. The size of the IA networks we are able to reason over is far greater than those found in previously published works.",

keywords = "Distributed computing, MapReduce, Qualitative temporal reasoning",

author = "Matthew Mantle and Sotirios Batsakis and Grigoris Antoniou",

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pages = "29--41",

editor = "Obdulia Pichardo-Lagunas and Sabino Miranda-Jim{\'e}nez",

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Mantle, M , Batsakis, S & Antoniou, G 2017, Large Scale Reasoning Using Allen's Interval Algebra. in O Pichardo-Lagunas & S Miranda-Jiménez (eds), Advances in Soft Computing: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part II. vol. LNCS 10062, Lecture Notes in Computer Science, vol. LNCS 10062, Springer, Cham, pp. 29-41, Mexican International Conference on Artificial Intelligence, Cancun, Mexico, 23/10/16. https://doi.org/10.1007/978-3-319-62428-0_3

Large Scale Reasoning Using Allen's Interval Algebra. / Mantle, Matthew ; Batsakis, Sotirios; Antoniou, Grigoris.

Advances in Soft Computing: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part II. ed. / Obdulia Pichardo-Lagunas; Sabino Miranda-Jiménez. Vol. LNCS 10062 Springer, Cham, 2017. p. 29-41 (Lecture Notes in Computer Science; Vol. LNCS 10062).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Batsakis, Sotirios

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N1 - Conference code: 15

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N2 - This paper proposes and evaluates a distributed, parallel approach for reasoning over large scale datasets using Allen's Interval Algebra (IA). We have developed and implemented algorithms that reason over IA networks using the Spark distributed processing framework. Experiments have been conducted by deploying the algorithms on computer clusters using synthetic datasets with various characteristics. We show that reasoning over datasets consisting of millions of interval relations is feasible and that our implementation scales effectively. The size of the IA networks we are able to reason over is far greater than those found in previously published works.

AB - This paper proposes and evaluates a distributed, parallel approach for reasoning over large scale datasets using Allen's Interval Algebra (IA). We have developed and implemented algorithms that reason over IA networks using the Spark distributed processing framework. Experiments have been conducted by deploying the algorithms on computer clusters using synthetic datasets with various characteristics. We show that reasoning over datasets consisting of millions of interval relations is feasible and that our implementation scales effectively. The size of the IA networks we are able to reason over is far greater than those found in previously published works.

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KW - MapReduce

KW - Qualitative temporal reasoning

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DO - 10.1007/978-3-319-62428-0_3

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SN - 9783319624273

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Mantle M , Batsakis S, Antoniou G. Large Scale Reasoning Using Allen's Interval Algebra. In Pichardo-Lagunas O, Miranda-Jiménez S, editors, Advances in Soft Computing: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part II. Vol. LNCS 10062. Springer, Cham. 2017. p. 29-41. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-62428-0_3

Large Scale Reasoning Using Allen's Interval Algebra

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