Semantics-based event log aggregation for process mining and analytics View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-12

AUTHORS

Amit V. Deokar, Jie Tao

ABSTRACT

In highly complex and flexible environments, event logs tend to exhibit high levels of heterogeneity, and clustering-based methods are candidate techniques for simplifying the mined process models from the process observations. To compensate for the information loss occurring during clustering, semantic information from event logs may be extracted and organized in the form of knowledge structures such as process ontologies using methods of ontology learning. In this article, we propose an overall computational framework for event log pre-processing, and then focus on a specific component of the framework, namely event log aggregation. We develop a detailed system architecture for this component, along with an implemented and evaluated research prototype SemAgg. We use phrase-based semantic similarity between normalized event names to aggregate event logs in a hierarchical form. We discuss the practical implications of this work for learning lower level process ontology classes as well as performing further process mining and analytics. More... »

PAGES

1209-1226

References to SciGraph publications

  • 2012. Data Transformation and Semantic Log Purging for Process Mining in ACTIVE FLOW AND COMBUSTION CONTROL 2018
  • 2010-12. Ontology-driven web-based semantic similarity in JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
  • 2004. Process Mining for Ubiquitous Mobile Systems: An Overview and a Concrete Algorithm in UBIQUITOUS MOBILE INFORMATION AND COLLABORATION SYSTEMS
  • 2009. Trace Clustering in Process Mining in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2006. Process Mining by Measuring Process Block Similarity in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2007-04. Genetic process mining: an experimental evaluation in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2008. Process Mining Based on Clustering: A Quest for Precision in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2002. Managing Multiple Ontologies and Ontology Evolution in Ontologging in INTELLIGENT INFORMATION PROCESSING
  • 2011-06. Supporting process design for e-business via an integrated process repository in INFORMATION TECHNOLOGY AND MANAGEMENT
  • 2008. Application of Process Mining in Healthcare – A Case Study in a Dutch Hospital in BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES
  • 2010. Understanding Spaghetti Models with Sequence Clustering for ProM in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2000-04. Hierarchical Decision Lists for Word Sense Disambiguation in LANGUAGE RESOURCES AND EVALUATION
  • 2007. Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics in BUSINESS PROCESS MANAGEMENT
  • 2003-04-30. Using Measures of Semantic Relatedness for Word Sense Disambiguation in COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING
  • 2009. Abstractions in Process Mining: A Taxonomy of Patterns in BUSINESS PROCESS MANAGEMENT
  • 2008. Decision Support Based on Process Mining in HANDBOOK ON DECISION SUPPORT SYSTEMS 1
  • 2004. Working with Multiple Ontologies on the Semantic Web in THE SEMANTIC WEB – ISWC 2004
  • 2007. An Outlook on Semantic Business Process Mining and Monitoring in ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS 2007: OTM 2007 WORKSHOPS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10796-015-9563-4

    DOI

    http://dx.doi.org/10.1007/s10796-015-9563-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1005095575


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information Systems", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Pennsylvania State University", 
              "id": "https://www.grid.ac/institutes/grid.29857.31", 
              "name": [
                "Sam and Irene Black School of Business, Pennsylvania State University, 5101 Jordan Road, Burke Center 268, 16563, Erie, PA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Deokar", 
            "givenName": "Amit V.", 
            "id": "sg:person.016674505217.56", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016674505217.56"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Fairfield University", 
              "id": "https://www.grid.ac/institutes/grid.255794.8", 
              "name": [
                "Information Systems and Operations Management (IS&OM) Department, Dolan School of Business, Fairfield University, 1073N. Benson Road, 06824, Fairfield, CT, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Tao", 
            "givenName": "Jie", 
            "id": "sg:person.010151263357.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010151263357.51"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.3115/981732.981751", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000312622"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-92219-3_32", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000460235", 
              "https://doi.org/10.1007/978-3-540-92219-3_32"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-92219-3_32", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000460235", 
              "https://doi.org/10.1007/978-3-540-92219-3_32"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-36456-0_24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001746929", 
              "https://doi.org/10.1007/3-540-36456-0_24"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-36456-0_24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001746929", 
              "https://doi.org/10.1007/3-540-36456-0_24"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-75183-0_24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002709909", 
              "https://doi.org/10.1007/978-3-540-75183-0_24"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-75183-0_24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002709909", 
              "https://doi.org/10.1007/978-3-540-75183-0_24"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-03848-8_12", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005628088", 
              "https://doi.org/10.1007/978-3-642-03848-8_12"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1454008.1454048", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006414061"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.is.2012.05.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006448627"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1108/14637151311319905", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010141847"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10844-009-0103-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014242761", 
              "https://doi.org/10.1007/s10844-009-0103-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10844-009-0103-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014242761", 
              "https://doi.org/10.1007/s10844-009-0103-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.compind.2003.10.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016254915"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11837862_15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018860395", 
              "https://doi.org/10.1007/11837862_15"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11837862_15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018860395", 
              "https://doi.org/10.1007/11837862_15"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1002674829964", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020803675", 
              "https://doi.org/10.1023/a:1002674829964"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.dss.2008.07.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021486559"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/s0269888903000687", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021858997"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-12186-9_10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022829384", 
              "https://doi.org/10.1007/978-3-642-12186-9_10"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-12186-9_10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022829384", 
              "https://doi.org/10.1007/978-3-642-12186-9_10"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-78238-4_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025186835", 
              "https://doi.org/10.1007/978-3-540-78238-4_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-78238-4_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025186835", 
              "https://doi.org/10.1007/978-3-540-78238-4_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1108/14637150810849373", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030845762"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.datak.2011.07.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032158088"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-76890-6_52", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032887159", 
              "https://doi.org/10.1007/978-3-540-76890-6_52"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-76890-6_52", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032887159", 
              "https://doi.org/10.1007/978-3-540-76890-6_52"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.is.2006.05.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033060034"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10799-010-0076-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033634566", 
              "https://doi.org/10.1007/s10799-010-0076-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.dss.2010.02.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035321057"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1459352.1459355", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035414632"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-30188-2_12", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036621469", 
              "https://doi.org/10.1007/978-3-540-30188-2_12"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-30188-2_12", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036621469", 
              "https://doi.org/10.1007/978-3-540-30188-2_12"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-30475-3_43", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039198264", 
              "https://doi.org/10.1007/978-3-540-30475-3_43"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-30475-3_43", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039198264", 
              "https://doi.org/10.1007/978-3-540-30475-3_43"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-48713-5_29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039773032", 
              "https://doi.org/10.1007/978-3-540-48713-5_29"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-48713-5_29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039773032", 
              "https://doi.org/10.1007/978-3-540-48713-5_29"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-31095-9_16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045024240", 
              "https://doi.org/10.1007/978-3-642-31095-9_16"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-0-387-35602-0_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047834723", 
              "https://doi.org/10.1007/978-0-387-35602-0_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-00328-8_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051634441", 
              "https://doi.org/10.1007/978-3-642-00328-8_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10618-006-0061-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051962035", 
              "https://doi.org/10.1007/s10618-006-0061-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/01621459.1983.10478008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058302869"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2006.123", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061661511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2006.123", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061661511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2006.123", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061661511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2010.163", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061662161"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2011.17", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061662361"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tsmca.2012.2195169", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061795896"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1504/ijbpim.2012.047909", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067438584"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/1217208", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069398467"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3115/1614025.1614037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099150957"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015-12", 
        "datePublishedReg": "2015-12-01", 
        "description": "In highly complex and flexible environments, event logs tend to exhibit high levels of heterogeneity, and clustering-based methods are candidate techniques for simplifying the mined process models from the process observations. To compensate for the information loss occurring during clustering, semantic information from event logs may be extracted and organized in the form of knowledge structures such as process ontologies using methods of ontology learning. In this article, we propose an overall computational framework for event log pre-processing, and then focus on a specific component of the framework, namely event log aggregation. We develop a detailed system architecture for this component, along with an implemented and evaluated research prototype SemAgg. We use phrase-based semantic similarity between normalized event names to aggregate event logs in a hierarchical form. We discuss the practical implications of this work for learning lower level process ontology classes as well as performing further process mining and analytics.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10796-015-9563-4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1136609", 
            "issn": [
              "1387-3326", 
              "1572-9419"
            ], 
            "name": "Information Systems Frontiers", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "6", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "17"
          }
        ], 
        "name": "Semantics-based event log aggregation for process mining and analytics", 
        "pagination": "1209-1226", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "18472b8a750ad901b9673adaf455cd927ae7183bd280e9b195a7b2d0420e0f47"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10796-015-9563-4"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1005095575"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10796-015-9563-4", 
          "https://app.dimensions.ai/details/publication/pub.1005095575"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T19:08", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8678_00000510.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs10796-015-9563-4"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10796-015-9563-4'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10796-015-9563-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10796-015-9563-4'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10796-015-9563-4'


     

    This table displays all metadata directly associated to this object as RDF triples.

    203 TRIPLES      21 PREDICATES      65 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10796-015-9563-4 schema:about anzsrc-for:08
    2 anzsrc-for:0806
    3 schema:author Naaf50ebe8a62426fb303e2f09559b8ae
    4 schema:citation sg:pub.10.1007/11837862_15
    5 sg:pub.10.1007/3-540-36456-0_24
    6 sg:pub.10.1007/978-0-387-35602-0_6
    7 sg:pub.10.1007/978-3-540-30188-2_12
    8 sg:pub.10.1007/978-3-540-30475-3_43
    9 sg:pub.10.1007/978-3-540-48713-5_29
    10 sg:pub.10.1007/978-3-540-75183-0_24
    11 sg:pub.10.1007/978-3-540-76890-6_52
    12 sg:pub.10.1007/978-3-540-78238-4_4
    13 sg:pub.10.1007/978-3-540-92219-3_32
    14 sg:pub.10.1007/978-3-642-00328-8_11
    15 sg:pub.10.1007/978-3-642-03848-8_12
    16 sg:pub.10.1007/978-3-642-12186-9_10
    17 sg:pub.10.1007/978-3-642-31095-9_16
    18 sg:pub.10.1007/s10618-006-0061-7
    19 sg:pub.10.1007/s10799-010-0076-z
    20 sg:pub.10.1007/s10844-009-0103-x
    21 sg:pub.10.1023/a:1002674829964
    22 https://doi.org/10.1016/j.compind.2003.10.001
    23 https://doi.org/10.1016/j.datak.2011.07.002
    24 https://doi.org/10.1016/j.dss.2008.07.002
    25 https://doi.org/10.1016/j.dss.2010.02.003
    26 https://doi.org/10.1016/j.is.2006.05.003
    27 https://doi.org/10.1016/j.is.2012.05.007
    28 https://doi.org/10.1017/s0269888903000687
    29 https://doi.org/10.1080/01621459.1983.10478008
    30 https://doi.org/10.1108/14637150810849373
    31 https://doi.org/10.1108/14637151311319905
    32 https://doi.org/10.1109/tkde.2006.123
    33 https://doi.org/10.1109/tkde.2010.163
    34 https://doi.org/10.1109/tkde.2011.17
    35 https://doi.org/10.1109/tsmca.2012.2195169
    36 https://doi.org/10.1145/1454008.1454048
    37 https://doi.org/10.1145/1459352.1459355
    38 https://doi.org/10.1504/ijbpim.2012.047909
    39 https://doi.org/10.2307/1217208
    40 https://doi.org/10.3115/1614025.1614037
    41 https://doi.org/10.3115/981732.981751
    42 schema:datePublished 2015-12
    43 schema:datePublishedReg 2015-12-01
    44 schema:description In highly complex and flexible environments, event logs tend to exhibit high levels of heterogeneity, and clustering-based methods are candidate techniques for simplifying the mined process models from the process observations. To compensate for the information loss occurring during clustering, semantic information from event logs may be extracted and organized in the form of knowledge structures such as process ontologies using methods of ontology learning. In this article, we propose an overall computational framework for event log pre-processing, and then focus on a specific component of the framework, namely event log aggregation. We develop a detailed system architecture for this component, along with an implemented and evaluated research prototype SemAgg. We use phrase-based semantic similarity between normalized event names to aggregate event logs in a hierarchical form. We discuss the practical implications of this work for learning lower level process ontology classes as well as performing further process mining and analytics.
    45 schema:genre research_article
    46 schema:inLanguage en
    47 schema:isAccessibleForFree false
    48 schema:isPartOf N34957cab69f34c6f9ff31dc6a54281c6
    49 Na960f3bc40e94fc1a1feb327dea4c803
    50 sg:journal.1136609
    51 schema:name Semantics-based event log aggregation for process mining and analytics
    52 schema:pagination 1209-1226
    53 schema:productId N0a91b85174de4711a9b9b91a68a9e617
    54 N5e6852c484b84d4ba5d4c37688c52b57
    55 Naca4dacf7ace4a128de7252a779a2ea8
    56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005095575
    57 https://doi.org/10.1007/s10796-015-9563-4
    58 schema:sdDatePublished 2019-04-10T19:08
    59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    60 schema:sdPublisher N8685cd6ab551497cb309444ec8a34941
    61 schema:url http://link.springer.com/10.1007%2Fs10796-015-9563-4
    62 sgo:license sg:explorer/license/
    63 sgo:sdDataset articles
    64 rdf:type schema:ScholarlyArticle
    65 N0a91b85174de4711a9b9b91a68a9e617 schema:name readcube_id
    66 schema:value 18472b8a750ad901b9673adaf455cd927ae7183bd280e9b195a7b2d0420e0f47
    67 rdf:type schema:PropertyValue
    68 N34957cab69f34c6f9ff31dc6a54281c6 schema:volumeNumber 17
    69 rdf:type schema:PublicationVolume
    70 N5e6852c484b84d4ba5d4c37688c52b57 schema:name doi
    71 schema:value 10.1007/s10796-015-9563-4
    72 rdf:type schema:PropertyValue
    73 N81bd4fca4bf34fe2b4120a3110ca4408 rdf:first sg:person.010151263357.51
    74 rdf:rest rdf:nil
    75 N8685cd6ab551497cb309444ec8a34941 schema:name Springer Nature - SN SciGraph project
    76 rdf:type schema:Organization
    77 Na960f3bc40e94fc1a1feb327dea4c803 schema:issueNumber 6
    78 rdf:type schema:PublicationIssue
    79 Naaf50ebe8a62426fb303e2f09559b8ae rdf:first sg:person.016674505217.56
    80 rdf:rest N81bd4fca4bf34fe2b4120a3110ca4408
    81 Naca4dacf7ace4a128de7252a779a2ea8 schema:name dimensions_id
    82 schema:value pub.1005095575
    83 rdf:type schema:PropertyValue
    84 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    85 schema:name Information and Computing Sciences
    86 rdf:type schema:DefinedTerm
    87 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    88 schema:name Information Systems
    89 rdf:type schema:DefinedTerm
    90 sg:journal.1136609 schema:issn 1387-3326
    91 1572-9419
    92 schema:name Information Systems Frontiers
    93 rdf:type schema:Periodical
    94 sg:person.010151263357.51 schema:affiliation https://www.grid.ac/institutes/grid.255794.8
    95 schema:familyName Tao
    96 schema:givenName Jie
    97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010151263357.51
    98 rdf:type schema:Person
    99 sg:person.016674505217.56 schema:affiliation https://www.grid.ac/institutes/grid.29857.31
    100 schema:familyName Deokar
    101 schema:givenName Amit V.
    102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016674505217.56
    103 rdf:type schema:Person
    104 sg:pub.10.1007/11837862_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018860395
    105 https://doi.org/10.1007/11837862_15
    106 rdf:type schema:CreativeWork
    107 sg:pub.10.1007/3-540-36456-0_24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001746929
    108 https://doi.org/10.1007/3-540-36456-0_24
    109 rdf:type schema:CreativeWork
    110 sg:pub.10.1007/978-0-387-35602-0_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047834723
    111 https://doi.org/10.1007/978-0-387-35602-0_6
    112 rdf:type schema:CreativeWork
    113 sg:pub.10.1007/978-3-540-30188-2_12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036621469
    114 https://doi.org/10.1007/978-3-540-30188-2_12
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1007/978-3-540-30475-3_43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039198264
    117 https://doi.org/10.1007/978-3-540-30475-3_43
    118 rdf:type schema:CreativeWork
    119 sg:pub.10.1007/978-3-540-48713-5_29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039773032
    120 https://doi.org/10.1007/978-3-540-48713-5_29
    121 rdf:type schema:CreativeWork
    122 sg:pub.10.1007/978-3-540-75183-0_24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002709909
    123 https://doi.org/10.1007/978-3-540-75183-0_24
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/978-3-540-76890-6_52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032887159
    126 https://doi.org/10.1007/978-3-540-76890-6_52
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1007/978-3-540-78238-4_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025186835
    129 https://doi.org/10.1007/978-3-540-78238-4_4
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1007/978-3-540-92219-3_32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000460235
    132 https://doi.org/10.1007/978-3-540-92219-3_32
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1007/978-3-642-00328-8_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051634441
    135 https://doi.org/10.1007/978-3-642-00328-8_11
    136 rdf:type schema:CreativeWork
    137 sg:pub.10.1007/978-3-642-03848-8_12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005628088
    138 https://doi.org/10.1007/978-3-642-03848-8_12
    139 rdf:type schema:CreativeWork
    140 sg:pub.10.1007/978-3-642-12186-9_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022829384
    141 https://doi.org/10.1007/978-3-642-12186-9_10
    142 rdf:type schema:CreativeWork
    143 sg:pub.10.1007/978-3-642-31095-9_16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045024240
    144 https://doi.org/10.1007/978-3-642-31095-9_16
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1007/s10618-006-0061-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051962035
    147 https://doi.org/10.1007/s10618-006-0061-7
    148 rdf:type schema:CreativeWork
    149 sg:pub.10.1007/s10799-010-0076-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1033634566
    150 https://doi.org/10.1007/s10799-010-0076-z
    151 rdf:type schema:CreativeWork
    152 sg:pub.10.1007/s10844-009-0103-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014242761
    153 https://doi.org/10.1007/s10844-009-0103-x
    154 rdf:type schema:CreativeWork
    155 sg:pub.10.1023/a:1002674829964 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020803675
    156 https://doi.org/10.1023/a:1002674829964
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1016/j.compind.2003.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016254915
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1016/j.datak.2011.07.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032158088
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1016/j.dss.2008.07.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021486559
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1016/j.dss.2010.02.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035321057
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1016/j.is.2006.05.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033060034
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1016/j.is.2012.05.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006448627
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1017/s0269888903000687 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021858997
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1080/01621459.1983.10478008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058302869
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1108/14637150810849373 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030845762
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1108/14637151311319905 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010141847
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1109/tkde.2006.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061661511
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1109/tkde.2010.163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662161
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1109/tkde.2011.17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662361
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1109/tsmca.2012.2195169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061795896
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1145/1454008.1454048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006414061
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1145/1459352.1459355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035414632
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1504/ijbpim.2012.047909 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067438584
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.2307/1217208 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069398467
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.3115/1614025.1614037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099150957
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.3115/981732.981751 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000312622
    197 rdf:type schema:CreativeWork
    198 https://www.grid.ac/institutes/grid.255794.8 schema:alternateName Fairfield University
    199 schema:name Information Systems and Operations Management (IS&OM) Department, Dolan School of Business, Fairfield University, 1073N. Benson Road, 06824, Fairfield, CT, USA
    200 rdf:type schema:Organization
    201 https://www.grid.ac/institutes/grid.29857.31 schema:alternateName Pennsylvania State University
    202 schema:name Sam and Irene Black School of Business, Pennsylvania State University, 5101 Jordan Road, Burke Center 268, 16563, Erie, PA, USA
    203 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...