View in EDS

Process-aware IIoT Knowledge Graph: A semantic model for Industrial IoT integration and analytics.

Saved in:
Bibliographic Details
Title: Process-aware IIoT Knowledge Graph: A semantic model for Industrial IoT integration and analytics.
Authors: Diamantini, Claudia1 (AUTHOR) c.diamantini@univpm.it, Mircoli, Alex1 (AUTHOR) a.mircoli@univpm.it, Potena, Domenico1 (AUTHOR) d.potena@univpm.it, Storti, Emanuele1 (AUTHOR) e.storti@univpm.it
Source: Future Generation Computer Systems. Feb2023, Vol. 139, p224-238. 15p.
Subjects: Knowledge graphs, Ontologies (Information retrieval), Internet of things, Set functions, Industry 4.0, Data extraction
Abstract: The integration of the huge data streams produced by the Industrial Internet of Things (IIoT) can provide invaluable knowledge in the context of Industry 4.0, and is also an open research issue. The present paper proposes a semantic approach to this issue, centred around the notion of process as the backbone. We build an ontology describing the fundamental elements involved in IIoT and their relations, and discuss the construction of the Process-aware IIoT Knowledge Graph, where raw sensor data are enriched with information about process activities and the physical production environment. We also propose a framework for querying the Knowledge Graph, and we demonstrate its capabilities by considering the production of metal accessories as case study. • A semantic approach for integration of Industrial Internet of Things data streams • An ontology to build a Process-aware IioT Knowledge Graph • A set of functions enabling data extraction and graph manipulations [ABSTRACT FROM AUTHOR]
Copyright of Future Generation Computer Systems is the property of Elsevier B.V. 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: 159954393
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Process-aware IIoT Knowledge Graph: A semantic model for Industrial IoT integration and analytics.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Diamantini%2C+Claudia%22">Diamantini, Claudia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> c.diamantini@univpm.it</i><br /><searchLink fieldCode="AR" term="%22Mircoli%2C+Alex%22">Mircoli, Alex</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> a.mircoli@univpm.it</i><br /><searchLink fieldCode="AR" term="%22Potena%2C+Domenico%22">Potena, Domenico</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> d.potena@univpm.it</i><br /><searchLink fieldCode="AR" term="%22Storti%2C+Emanuele%22">Storti, Emanuele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> e.storti@univpm.it</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Future+Generation+Computer+Systems%22">Future Generation Computer Systems</searchLink>. Feb2023, Vol. 139, p224-238. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Ontologies+%28Information+retrieval%29%22">Ontologies (Information retrieval)</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Set+functions%22">Set functions</searchLink><br /><searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Data+extraction%22">Data extraction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The integration of the huge data streams produced by the Industrial Internet of Things (IIoT) can provide invaluable knowledge in the context of Industry 4.0, and is also an open research issue. The present paper proposes a semantic approach to this issue, centred around the notion of process as the backbone. We build an ontology describing the fundamental elements involved in IIoT and their relations, and discuss the construction of the Process-aware IIoT Knowledge Graph, where raw sensor data are enriched with information about process activities and the physical production environment. We also propose a framework for querying the Knowledge Graph, and we demonstrate its capabilities by considering the production of metal accessories as case study. • A semantic approach for integration of Industrial Internet of Things data streams • An ontology to build a Process-aware IioT Knowledge Graph • A set of functions enabling data extraction and graph manipulations [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Future Generation Computer Systems is the property of Elsevier B.V. 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=159954393
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.future.2022.10.003
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 224
    Subjects:
      – SubjectFull: Knowledge graphs
        Type: general
      – SubjectFull: Ontologies (Information retrieval)
        Type: general
      – SubjectFull: Internet of things
        Type: general
      – SubjectFull: Set functions
        Type: general
      – SubjectFull: Industry 4.0
        Type: general
      – SubjectFull: Data extraction
        Type: general
    Titles:
      – TitleFull: Process-aware IIoT Knowledge Graph: A semantic model for Industrial IoT integration and analytics.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Diamantini, Claudia
      – PersonEntity:
          Name:
            NameFull: Mircoli, Alex
      – PersonEntity:
          Name:
            NameFull: Potena, Domenico
      – PersonEntity:
          Name:
            NameFull: Storti, Emanuele
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 0167739X
          Numbering:
            – Type: volume
              Value: 139
          Titles:
            – TitleFull: Future Generation Computer Systems
              Type: main
ResultId 1