Leveraging Unstructured Data to Analyze Implicit Process Context View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-08-12

AUTHORS

Renuka Sindhgatta , Aditya Ghose , Hoa Khanh Dam

ABSTRACT

Adapting a business process to different context requires identifying various situations and evolving the process to support such situations. Previous work focused on modeling, observing and collecting contextual information. Furthermore, impact of context on process or resource performance has been studied. However, much of the work considers explicit contextual information that is defined by domain experts. There are several implicit contextual dimensions, that are difficult to model as all situations cannot be anticipated a priori. Context mining involves analysis of process logs to identify context and correlate with process performance indicators or outcomes. In this work, we leverage unstructured data available in user comments or mails to discover implicit context of the process. We automatically analyze textual data and group process instances by applying information extraction and text clustering techniques. Groups of process instances are correlated to their process outcomes to filter irrelevant information. We apply the approach on real-world process logs to identify contextual information. More... »

PAGES

143-158

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-98651-7_9

DOI

http://dx.doi.org/10.1007/978-3-319-98651-7_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106120934


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/2008/08", 
        "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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "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": "IBM Research, Bangalore, India", 
          "id": "http://www.grid.ac/institutes/grid.481550.d", 
          "name": [
            "IBM Research, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sindhgatta", 
        "givenName": "Renuka", 
        "id": "sg:person.015651720511.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015651720511.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Wollongong, Wollongong, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1007.6", 
          "name": [
            "University of Wollongong, Wollongong, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ghose", 
        "givenName": "Aditya", 
        "id": "sg:person.015573517335.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015573517335.70"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Wollongong, Wollongong, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1007.6", 
          "name": [
            "University of Wollongong, Wollongong, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Khanh Dam", 
        "givenName": "Hoa", 
        "type": "Person"
      }
    ], 
    "datePublished": "2018-08-12", 
    "datePublishedReg": "2018-08-12", 
    "description": "Adapting a business process to different context requires identifying various situations and evolving the process to support such situations. Previous work focused on modeling, observing and collecting contextual information. Furthermore, impact of context on process or resource performance has been studied. However, much of the work considers explicit contextual information that is defined by domain experts. There are several implicit contextual dimensions, that are difficult to model as all situations cannot be anticipated a priori. Context mining involves analysis of process logs to identify context and correlate with process performance indicators or outcomes. In this work, we leverage unstructured data available in user comments or mails to discover implicit context of the process. We automatically analyze textual data and group process instances by applying information extraction and text clustering techniques. Groups of process instances are correlated to their process outcomes to filter irrelevant information. We apply the approach on real-world process logs to identify contextual information.", 
    "editor": [
      {
        "familyName": "Weske", 
        "givenName": "Mathias", 
        "type": "Person"
      }, 
      {
        "familyName": "Montali", 
        "givenName": "Marco", 
        "type": "Person"
      }, 
      {
        "familyName": "Weber", 
        "givenName": "Ingo", 
        "type": "Person"
      }, 
      {
        "familyName": "vom Brocke", 
        "givenName": "Jan", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-98651-7_9", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-98650-0", 
        "978-3-319-98651-7"
      ], 
      "name": "Business Process Management Forum", 
      "type": "Book"
    }, 
    "keywords": [
      "contextual information", 
      "unstructured data", 
      "process instances", 
      "text clustering techniques", 
      "explicit contextual information", 
      "real-world processes", 
      "context mining", 
      "domain experts", 
      "business processes", 
      "information extraction", 
      "process performance indicators", 
      "process logs", 
      "textual data", 
      "filter irrelevant information", 
      "clustering techniques", 
      "process context", 
      "implicit context", 
      "user comments", 
      "resource performance", 
      "irrelevant information", 
      "impact of context", 
      "information", 
      "such situations", 
      "previous work", 
      "contextual dimensions", 
      "performance indicators", 
      "different contexts", 
      "mining", 
      "instances", 
      "context", 
      "work", 
      "process outcomes", 
      "situation", 
      "data", 
      "experts", 
      "logs", 
      "extraction", 
      "process", 
      "performance", 
      "mail", 
      "modeling", 
      "technique", 
      "comments", 
      "dimensions", 
      "analysis", 
      "impact", 
      "indicators", 
      "outcomes", 
      "group", 
      "approach"
    ], 
    "name": "Leveraging Unstructured Data to Analyze Implicit Process Context", 
    "pagination": "143-158", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106120934"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-98651-7_9"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-98651-7_9", 
      "https://app.dimensions.ai/details/publication/pub.1106120934"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-11-24T21:11", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/chapter/chapter_13.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-98651-7_9"
  }
]
 

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.10.1007/978-3-319-98651-7_9'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-98651-7_9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-98651-7_9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-98651-7_9'


 

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

144 TRIPLES      22 PREDICATES      75 URIs      67 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-98651-7_9 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:0806
4 schema:author Nbc2b08dc3e4a4645bc812c0294142c84
5 schema:datePublished 2018-08-12
6 schema:datePublishedReg 2018-08-12
7 schema:description Adapting a business process to different context requires identifying various situations and evolving the process to support such situations. Previous work focused on modeling, observing and collecting contextual information. Furthermore, impact of context on process or resource performance has been studied. However, much of the work considers explicit contextual information that is defined by domain experts. There are several implicit contextual dimensions, that are difficult to model as all situations cannot be anticipated a priori. Context mining involves analysis of process logs to identify context and correlate with process performance indicators or outcomes. In this work, we leverage unstructured data available in user comments or mails to discover implicit context of the process. We automatically analyze textual data and group process instances by applying information extraction and text clustering techniques. Groups of process instances are correlated to their process outcomes to filter irrelevant information. We apply the approach on real-world process logs to identify contextual information.
8 schema:editor Nb7c01032b37149418257021455cfff25
9 schema:genre chapter
10 schema:isAccessibleForFree false
11 schema:isPartOf N0228306bfb2341a491e1379b849bd943
12 schema:keywords analysis
13 approach
14 business processes
15 clustering techniques
16 comments
17 context
18 context mining
19 contextual dimensions
20 contextual information
21 data
22 different contexts
23 dimensions
24 domain experts
25 experts
26 explicit contextual information
27 extraction
28 filter irrelevant information
29 group
30 impact
31 impact of context
32 implicit context
33 indicators
34 information
35 information extraction
36 instances
37 irrelevant information
38 logs
39 mail
40 mining
41 modeling
42 outcomes
43 performance
44 performance indicators
45 previous work
46 process
47 process context
48 process instances
49 process logs
50 process outcomes
51 process performance indicators
52 real-world processes
53 resource performance
54 situation
55 such situations
56 technique
57 text clustering techniques
58 textual data
59 unstructured data
60 user comments
61 work
62 schema:name Leveraging Unstructured Data to Analyze Implicit Process Context
63 schema:pagination 143-158
64 schema:productId N61066378bc144b67a403e08ccd1e715f
65 Nf7353f48fe2d41d0a7cc48c55090f3f1
66 schema:publisher Nc437e754f1cb42aebd6b694c0e52a816
67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106120934
68 https://doi.org/10.1007/978-3-319-98651-7_9
69 schema:sdDatePublished 2022-11-24T21:11
70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
71 schema:sdPublisher N0cdf3d1a753f41b1aeb44f457383a422
72 schema:url https://doi.org/10.1007/978-3-319-98651-7_9
73 sgo:license sg:explorer/license/
74 sgo:sdDataset chapters
75 rdf:type schema:Chapter
76 N0228306bfb2341a491e1379b849bd943 schema:isbn 978-3-319-98650-0
77 978-3-319-98651-7
78 schema:name Business Process Management Forum
79 rdf:type schema:Book
80 N0cdf3d1a753f41b1aeb44f457383a422 schema:name Springer Nature - SN SciGraph project
81 rdf:type schema:Organization
82 N1799757ef89a4117bd2f64edd90cebb7 schema:familyName Weber
83 schema:givenName Ingo
84 rdf:type schema:Person
85 N1da0ee36990248459be5fa5985df1633 schema:familyName vom Brocke
86 schema:givenName Jan
87 rdf:type schema:Person
88 N4858f3df144e40d6ad4d0dc276df0a1c rdf:first N1799757ef89a4117bd2f64edd90cebb7
89 rdf:rest Ne8e6412cfc0641d58e5af04dbdda711e
90 N61066378bc144b67a403e08ccd1e715f schema:name dimensions_id
91 schema:value pub.1106120934
92 rdf:type schema:PropertyValue
93 N8f499a41e7084d4093c650483a6dd00a schema:affiliation grid-institutes:grid.1007.6
94 schema:familyName Khanh Dam
95 schema:givenName Hoa
96 rdf:type schema:Person
97 N96e9e7ee5ecb4db09f32808a3fa1763c rdf:first Na7737add84f54ad597555e4ebf6e000f
98 rdf:rest N4858f3df144e40d6ad4d0dc276df0a1c
99 N9c515ec9fd6b4e4aac8813bf096460ef schema:familyName Weske
100 schema:givenName Mathias
101 rdf:type schema:Person
102 Na7737add84f54ad597555e4ebf6e000f schema:familyName Montali
103 schema:givenName Marco
104 rdf:type schema:Person
105 Nb5583ba1872e4b9f92bde69cebbdd270 rdf:first N8f499a41e7084d4093c650483a6dd00a
106 rdf:rest rdf:nil
107 Nb7c01032b37149418257021455cfff25 rdf:first N9c515ec9fd6b4e4aac8813bf096460ef
108 rdf:rest N96e9e7ee5ecb4db09f32808a3fa1763c
109 Nbc2b08dc3e4a4645bc812c0294142c84 rdf:first sg:person.015651720511.55
110 rdf:rest Ndaec3e8197dd404fa23357f8d241642c
111 Nc437e754f1cb42aebd6b694c0e52a816 schema:name Springer Nature
112 rdf:type schema:Organisation
113 Ndaec3e8197dd404fa23357f8d241642c rdf:first sg:person.015573517335.70
114 rdf:rest Nb5583ba1872e4b9f92bde69cebbdd270
115 Ne8e6412cfc0641d58e5af04dbdda711e rdf:first N1da0ee36990248459be5fa5985df1633
116 rdf:rest rdf:nil
117 Nf7353f48fe2d41d0a7cc48c55090f3f1 schema:name doi
118 schema:value 10.1007/978-3-319-98651-7_9
119 rdf:type schema:PropertyValue
120 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
121 schema:name Information and Computing Sciences
122 rdf:type schema:DefinedTerm
123 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
124 schema:name Artificial Intelligence and Image Processing
125 rdf:type schema:DefinedTerm
126 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
127 schema:name Information Systems
128 rdf:type schema:DefinedTerm
129 sg:person.015573517335.70 schema:affiliation grid-institutes:grid.1007.6
130 schema:familyName Ghose
131 schema:givenName Aditya
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015573517335.70
133 rdf:type schema:Person
134 sg:person.015651720511.55 schema:affiliation grid-institutes:grid.481550.d
135 schema:familyName Sindhgatta
136 schema:givenName Renuka
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015651720511.55
138 rdf:type schema:Person
139 grid-institutes:grid.1007.6 schema:alternateName University of Wollongong, Wollongong, Australia
140 schema:name University of Wollongong, Wollongong, Australia
141 rdf:type schema:Organization
142 grid-institutes:grid.481550.d schema:alternateName IBM Research, Bangalore, India
143 schema:name IBM Research, Bangalore, India
144 rdf:type schema:Organization
 




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


...