Large Scale Reasoning Using Allen's Interval Algebra

Matthew Mantle, Sotirios Batsakis, Grigoris Antoniou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


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 languageEnglish
Title of host publicationAdvances in Soft Computing
Subtitle of host publication15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part II
EditorsObdulia Pichardo-Lagunas, Sabino Miranda-Jiménez
PublisherSpringer, Cham
Number of pages13
VolumeLNCS 10062
ISBN (Electronic)9783319624280
ISBN (Print)9783319624273
Publication statusPublished - 2 Aug 2017
EventMexican International Conference on Artificial Intelligence - Cancun, Mexico
Duration: 23 Oct 201629 Oct 2016
Conference number: 15 (Link to Conference Website)

Publication series

NameLecture Notes in Computer Science
VolumeLNCS 10062
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMexican International Conference on Artificial Intelligence
Abbreviated titleMCIAI
Internet address


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