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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "School of Software, Tsinghua University, 100084, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "School of Software, Tsinghua University, 100084, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Hedong", 
        "id": "sg:person.014342721753.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014342721753.90"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Software, Tsinghua University, 100084, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "School of Software, Tsinghua University, 100084, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wen", 
        "givenName": "Lijie", 
        "id": "sg:person.013640554311.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013640554311.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Software, Tsinghua University, 100084, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "School of Software, Tsinghua University, 100084, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Jianmin", 
        "id": "sg:person.012303351315.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012303351315.43"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2013", 
    "datePublishedReg": "2013-01-01", 
    "description": "By means of deriving knowledge from event logs, the application of process mining algorithms can provide valuable insight into the actual execution of business processes and help identify opportunities for their improvement. The event logs may be collected by people manually or generated by a variety of software applications, including business process management systems. However logging may not always be done in a reliable manner, resulting in events being missed or interchanged. Consequently, the results of the application of process mining algorithms to such \u201cpolluted\u201d logs may not be so reliable and it would be preferable if false traces, i.e. polluted traces which are not possibly valid as regards the process model to be discovered, could be identified first and removed before such algorithms are applied. In this paper an approach is proposed that assists with identifying false traces in event logs as well as the cause of their pollution. The approach is empirically validated.", 
    "editor": [
      {
        "familyName": "Pei", 
        "givenName": "Jian", 
        "type": "Person"
      }, 
      {
        "familyName": "Tseng", 
        "givenName": "Vincent S.", 
        "type": "Person"
      }, 
      {
        "familyName": "Cao", 
        "givenName": "Longbing", 
        "type": "Person"
      }, 
      {
        "familyName": "Motoda", 
        "givenName": "Hiroshi", 
        "type": "Person"
      }, 
      {
        "familyName": "Xu", 
        "givenName": "Guandong", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-37456-2_45", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-37455-5", 
        "978-3-642-37456-2"
      ], 
      "name": "Advances in Knowledge Discovery and Data Mining", 
      "type": "Book"
    }, 
    "keywords": [
      "process mining algorithms", 
      "event logs", 
      "mining algorithms", 
      "business process management system", 
      "process event logs", 
      "process management system", 
      "software applications", 
      "business processes", 
      "such algorithms", 
      "actual execution", 
      "management system", 
      "process model", 
      "algorithm", 
      "reliable manner", 
      "logs", 
      "applications", 
      "traces", 
      "execution", 
      "valuable insights", 
      "system", 
      "assists", 
      "knowledge", 
      "model", 
      "opportunities", 
      "improvement", 
      "manner", 
      "process", 
      "variety", 
      "people", 
      "means", 
      "results", 
      "insights", 
      "events", 
      "pollution", 
      "cause", 
      "approach", 
      "paper"
    ], 
    "name": "An Approach to Identifying False Traces in Process Event Logs", 
    "pagination": "533-545", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1013253540"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-37456-2_45"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-37456-2_45", 
      "https://app.dimensions.ai/details/publication/pub.1013253540"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-20T07:42", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/chapter/chapter_148.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-37456-2_45"
  }
]

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

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

131 TRIPLES      23 PREDICATES      63 URIs      56 LITERALS      7 BLANK NODES