View in EDS

All in One: Mining Multiple Movement Patterns.

Saved in:
Bibliographic Details
Title: All in One: Mining Multiple Movement Patterns.
Authors: Phan, Nhathai1, Poncelet, Pascal2, Teisseire, Maguelonne3
Source: International Journal of Information Technology & Decision Making. Sep2016, Vol. 15 Issue 5, p1115-1156. 42p.
Subjects: Algorithms, Data mining, Trajectory measurements, Databases, Telemetry
Abstract: Recent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. In common, these object sets are called object movement patterns. Due to the emergence of many different kinds of object movement patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of patterns. It is costly and time consuming to mine and manage various number of patterns, since we have to execute a large number of different pattern mining algorithms. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine movement patterns in the itemset context. Second, we propose a unifying approach, named GeT_Move, which uses a frequent closed itemset-based object movement pattern-mining algorithm to mine and manage different patterns. GeT_Move is developed in two versions which are GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the parameters setting, we further propose a Parameter Free Incremental GeT_Move algorithm. Comprehensive experiments are performed on real and large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Information Technology & Decision Making is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 117875248
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: All in One: Mining Multiple Movement Patterns.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Phan%2C+Nhathai%22">Phan, Nhathai</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Poncelet%2C+Pascal%22">Poncelet, Pascal</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Teisseire%2C+Maguelonne%22">Teisseire, Maguelonne</searchLink><relatesTo>3</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Information+Technology+%26+Decision+Making%22">International Journal of Information Technology & Decision Making</searchLink>. Sep2016, Vol. 15 Issue 5, p1115-1156. 42p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Trajectory+measurements%22">Trajectory measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Telemetry%22">Telemetry</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Recent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. In common, these object sets are called object movement patterns. Due to the emergence of many different kinds of object movement patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of patterns. It is costly and time consuming to mine and manage various number of patterns, since we have to execute a large number of different pattern mining algorithms. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine movement patterns in the itemset context. Second, we propose a unifying approach, named GeT_Move, which uses a frequent closed itemset-based object movement pattern-mining algorithm to mine and manage different patterns. GeT_Move is developed in two versions which are GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the parameters setting, we further propose a Parameter Free Incremental GeT_Move algorithm. Comprehensive experiments are performed on real and large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Information Technology & Decision Making is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=117875248
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1142/S0219622016500280
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 42
        StartPage: 1115
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Trajectory measurements
        Type: general
      – SubjectFull: Databases
        Type: general
      – SubjectFull: Telemetry
        Type: general
    Titles:
      – TitleFull: All in One: Mining Multiple Movement Patterns.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Phan, Nhathai
      – PersonEntity:
          Name:
            NameFull: Poncelet, Pascal
      – PersonEntity:
          Name:
            NameFull: Teisseire, Maguelonne
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2016
              Type: published
              Y: 2016
          Identifiers:
            – Type: issn-print
              Value: 02196220
          Numbering:
            – Type: volume
              Value: 15
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: International Journal of Information Technology & Decision Making
              Type: main
ResultId 1