Tools and approaches for topic detection from Twitter streams: survey View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-03

AUTHORS

Rania Ibrahim, Ahmed Elbagoury, Mohamed S. Kamel, Fakhri Karray

ABSTRACT

Detecting topics from Twitter streams has become an important task as it is used in various fields including natural disaster warning, users opinion assessment, and traffic prediction. In this article, we outline different types of topic detection techniques and evaluate their performance. We categorize the topic detection techniques into five categories which are clustering, frequent pattern mining, Exemplar-based, matrix factorization, and probabilistic models. For clustering techniques, we discuss and evaluate nine different techniques which are sequential k-means, spherical k-means, Kernel k-means, scalable Kernel k-means, incremental batch k-means, DBSCAN, spectral clustering, document pivot clustering, and Bngram. Moreover, for matrix factorization techniques, we analyze five different techniques which are sequential Latent Semantic Indexing (LSI), stochastic LSI, Alternating Least Squares (ALS), Rank-one Downdate (R1D), and Column Subset Selection (CSS). Additionally, we evaluate several other techniques in the frequent pattern mining, Exemplar-based, and probabilistic model categories. Results on three Twitter datasets show that Soft Frequent Pattern Mining (SFM) and Bngram achieve the best term precision, while CSS achieves the best term recall and topic recall in most of the cases. Moreover, Exemplar-based topic detection obtains a good balance between the term recall and term precision, while achieving a good topic recall and running time. More... »

PAGES

511-539

References to SciGraph publications

  • 2017-01. Prediction of places of visit using tweets in KNOWLEDGE AND INFORMATION SYSTEMS
  • 1970-04. Singular value decomposition and least squares solutions in NUMERISCHE MATHEMATIK
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10115-017-1081-x

    DOI

    http://dx.doi.org/10.1007/s10115-017-1081-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1090852518


    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/2008/0801", 
            "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/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "University of Waterloo, N2L 3G1, Waterloo, ON, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ibrahim", 
            "givenName": "Rania", 
            "id": "sg:person.012273114467.13", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012273114467.13"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "University of Waterloo, N2L 3G1, Waterloo, ON, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Elbagoury", 
            "givenName": "Ahmed", 
            "id": "sg:person.013070475067.20", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013070475067.20"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "University of Waterloo, N2L 3G1, Waterloo, ON, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kamel", 
            "givenName": "Mohamed S.", 
            "id": "sg:person.01133760566.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133760566.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Waterloo", 
              "id": "https://www.grid.ac/institutes/grid.46078.3d", 
              "name": [
                "University of Waterloo, N2L 3G1, Waterloo, ON, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Karray", 
            "givenName": "Fakhri", 
            "id": "sg:person.010544641574.33", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010544641574.33"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/1390156.1390165", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000986240"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2015.06.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001852941"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2484028.2484166", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014502103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0307752101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026144033"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/331499.331504", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026347712"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10115-016-0936-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027781802", 
              "https://doi.org/10.1007/s10115-016-0936-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/01638539809545028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029248962"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cosrev.2013.03.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033216016"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1459352.1459355", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035414632"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.csda.2006.11.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046812635"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02163027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049192175", 
              "https://doi.org/10.1007/bf02163027"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02163027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049192175", 
              "https://doi.org/10.1007/bf02163027"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/t-affc.2013.22", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061447042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2012.51", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061662644"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2016.2525768", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061663225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmm.2013.2265080", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061698144"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/0702016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062850551"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/090771806", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062856710"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/0911052", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062857337"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.14778/2212351.2212354", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067367966"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9781611973440.49", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1088801992"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdm.2011.22", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094186276"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/cbo9780511809682", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098667572"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2018-03", 
        "datePublishedReg": "2018-03-01", 
        "description": "Detecting topics from Twitter streams has become an important task as it is used in various fields including natural disaster warning, users opinion assessment, and traffic prediction. In this article, we outline different types of topic detection techniques and evaluate their performance. We categorize the topic detection techniques into five categories which are clustering, frequent pattern mining, Exemplar-based, matrix factorization, and probabilistic models. For clustering techniques, we discuss and evaluate nine different techniques which are sequential k-means, spherical k-means, Kernel k-means, scalable Kernel k-means, incremental batch k-means, DBSCAN, spectral clustering, document pivot clustering, and Bngram. Moreover, for matrix factorization techniques, we analyze five different techniques which are sequential Latent Semantic Indexing (LSI), stochastic LSI, Alternating Least Squares (ALS), Rank-one Downdate (R1D), and Column Subset Selection (CSS). Additionally, we evaluate several other techniques in the frequent pattern mining, Exemplar-based, and probabilistic model categories. Results on three Twitter datasets show that Soft Frequent Pattern Mining (SFM) and Bngram achieve the best term precision, while CSS achieves the best term recall and topic recall in most of the cases. Moreover, Exemplar-based topic detection obtains a good balance between the term recall and term precision, while achieving a good topic recall and running time.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10115-017-1081-x", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1041769", 
            "issn": [
              "0219-1377", 
              "0219-3116"
            ], 
            "name": "Knowledge and Information Systems", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "54"
          }
        ], 
        "name": "Tools and approaches for topic detection from Twitter streams: survey", 
        "pagination": "511-539", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "b796239f2725ddb3338bbdd4e8e5e35fc48009fa6c050acb44d79a36fdde84cc"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10115-017-1081-x"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1090852518"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10115-017-1081-x", 
          "https://app.dimensions.ai/details/publication/pub.1090852518"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T10:27", 
        "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/v90/0000000349_0000000349/records_113639_00000004.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs10115-017-1081-x"
      }
    ]
     

    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/s10115-017-1081-x'

    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/s10115-017-1081-x'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10115-017-1081-x'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10115-017-1081-x'


     

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

    150 TRIPLES      21 PREDICATES      49 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10115-017-1081-x schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Ned4c05a2c80d447e92fe548f378407b4
    4 schema:citation sg:pub.10.1007/bf02163027
    5 sg:pub.10.1007/s10115-016-0936-x
    6 https://doi.org/10.1016/j.cosrev.2013.03.001
    7 https://doi.org/10.1016/j.csda.2006.11.006
    8 https://doi.org/10.1016/j.knosys.2015.06.010
    9 https://doi.org/10.1017/cbo9780511809682
    10 https://doi.org/10.1073/pnas.0307752101
    11 https://doi.org/10.1080/01638539809545028
    12 https://doi.org/10.1109/icdm.2011.22
    13 https://doi.org/10.1109/t-affc.2013.22
    14 https://doi.org/10.1109/tkde.2012.51
    15 https://doi.org/10.1109/tkde.2016.2525768
    16 https://doi.org/10.1109/tmm.2013.2265080
    17 https://doi.org/10.1137/0702016
    18 https://doi.org/10.1137/090771806
    19 https://doi.org/10.1137/0911052
    20 https://doi.org/10.1137/1.9781611973440.49
    21 https://doi.org/10.1145/1390156.1390165
    22 https://doi.org/10.1145/1459352.1459355
    23 https://doi.org/10.1145/2484028.2484166
    24 https://doi.org/10.1145/331499.331504
    25 https://doi.org/10.14778/2212351.2212354
    26 schema:datePublished 2018-03
    27 schema:datePublishedReg 2018-03-01
    28 schema:description Detecting topics from Twitter streams has become an important task as it is used in various fields including natural disaster warning, users opinion assessment, and traffic prediction. In this article, we outline different types of topic detection techniques and evaluate their performance. We categorize the topic detection techniques into five categories which are clustering, frequent pattern mining, Exemplar-based, matrix factorization, and probabilistic models. For clustering techniques, we discuss and evaluate nine different techniques which are sequential k-means, spherical k-means, Kernel k-means, scalable Kernel k-means, incremental batch k-means, DBSCAN, spectral clustering, document pivot clustering, and Bngram. Moreover, for matrix factorization techniques, we analyze five different techniques which are sequential Latent Semantic Indexing (LSI), stochastic LSI, Alternating Least Squares (ALS), Rank-one Downdate (R1D), and Column Subset Selection (CSS). Additionally, we evaluate several other techniques in the frequent pattern mining, Exemplar-based, and probabilistic model categories. Results on three Twitter datasets show that Soft Frequent Pattern Mining (SFM) and Bngram achieve the best term precision, while CSS achieves the best term recall and topic recall in most of the cases. Moreover, Exemplar-based topic detection obtains a good balance between the term recall and term precision, while achieving a good topic recall and running time.
    29 schema:genre research_article
    30 schema:inLanguage en
    31 schema:isAccessibleForFree false
    32 schema:isPartOf N4864e55416c54c269822abdf99de4bf6
    33 Na53177096a764e9cb817efd9b74c3418
    34 sg:journal.1041769
    35 schema:name Tools and approaches for topic detection from Twitter streams: survey
    36 schema:pagination 511-539
    37 schema:productId N43ee5a3cc3f042b9b61a6f37b24a1271
    38 Nd487a830a27a45aa8d2c6d3736990434
    39 Ne0ebe07e76764c0d8aa15db98ae69b97
    40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090852518
    41 https://doi.org/10.1007/s10115-017-1081-x
    42 schema:sdDatePublished 2019-04-11T10:27
    43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    44 schema:sdPublisher N6d3645fac69b4ae7b36388da96b93fd7
    45 schema:url https://link.springer.com/10.1007%2Fs10115-017-1081-x
    46 sgo:license sg:explorer/license/
    47 sgo:sdDataset articles
    48 rdf:type schema:ScholarlyArticle
    49 N1e611e8554524e6abd9928868617b845 rdf:first sg:person.01133760566.26
    50 rdf:rest Nf977c63803944c248b557c2cd81ae68b
    51 N43ee5a3cc3f042b9b61a6f37b24a1271 schema:name doi
    52 schema:value 10.1007/s10115-017-1081-x
    53 rdf:type schema:PropertyValue
    54 N4864e55416c54c269822abdf99de4bf6 schema:volumeNumber 54
    55 rdf:type schema:PublicationVolume
    56 N555aefcb34fc47c4840790848d3ab23a rdf:first sg:person.013070475067.20
    57 rdf:rest N1e611e8554524e6abd9928868617b845
    58 N6d3645fac69b4ae7b36388da96b93fd7 schema:name Springer Nature - SN SciGraph project
    59 rdf:type schema:Organization
    60 Na53177096a764e9cb817efd9b74c3418 schema:issueNumber 3
    61 rdf:type schema:PublicationIssue
    62 Nd487a830a27a45aa8d2c6d3736990434 schema:name dimensions_id
    63 schema:value pub.1090852518
    64 rdf:type schema:PropertyValue
    65 Ne0ebe07e76764c0d8aa15db98ae69b97 schema:name readcube_id
    66 schema:value b796239f2725ddb3338bbdd4e8e5e35fc48009fa6c050acb44d79a36fdde84cc
    67 rdf:type schema:PropertyValue
    68 Ned4c05a2c80d447e92fe548f378407b4 rdf:first sg:person.012273114467.13
    69 rdf:rest N555aefcb34fc47c4840790848d3ab23a
    70 Nf977c63803944c248b557c2cd81ae68b rdf:first sg:person.010544641574.33
    71 rdf:rest rdf:nil
    72 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    73 schema:name Information and Computing Sciences
    74 rdf:type schema:DefinedTerm
    75 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    76 schema:name Artificial Intelligence and Image Processing
    77 rdf:type schema:DefinedTerm
    78 sg:journal.1041769 schema:issn 0219-1377
    79 0219-3116
    80 schema:name Knowledge and Information Systems
    81 rdf:type schema:Periodical
    82 sg:person.010544641574.33 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    83 schema:familyName Karray
    84 schema:givenName Fakhri
    85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010544641574.33
    86 rdf:type schema:Person
    87 sg:person.01133760566.26 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    88 schema:familyName Kamel
    89 schema:givenName Mohamed S.
    90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133760566.26
    91 rdf:type schema:Person
    92 sg:person.012273114467.13 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    93 schema:familyName Ibrahim
    94 schema:givenName Rania
    95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012273114467.13
    96 rdf:type schema:Person
    97 sg:person.013070475067.20 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
    98 schema:familyName Elbagoury
    99 schema:givenName Ahmed
    100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013070475067.20
    101 rdf:type schema:Person
    102 sg:pub.10.1007/bf02163027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049192175
    103 https://doi.org/10.1007/bf02163027
    104 rdf:type schema:CreativeWork
    105 sg:pub.10.1007/s10115-016-0936-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1027781802
    106 https://doi.org/10.1007/s10115-016-0936-x
    107 rdf:type schema:CreativeWork
    108 https://doi.org/10.1016/j.cosrev.2013.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033216016
    109 rdf:type schema:CreativeWork
    110 https://doi.org/10.1016/j.csda.2006.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046812635
    111 rdf:type schema:CreativeWork
    112 https://doi.org/10.1016/j.knosys.2015.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001852941
    113 rdf:type schema:CreativeWork
    114 https://doi.org/10.1017/cbo9780511809682 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098667572
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1073/pnas.0307752101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026144033
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.1080/01638539809545028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029248962
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.1109/icdm.2011.22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094186276
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1109/t-affc.2013.22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061447042
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1109/tkde.2012.51 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662644
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1109/tkde.2016.2525768 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061663225
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1109/tmm.2013.2265080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061698144
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1137/0702016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062850551
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1137/090771806 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062856710
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1137/0911052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062857337
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1137/1.9781611973440.49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088801992
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1145/1390156.1390165 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000986240
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1145/1459352.1459355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035414632
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1145/2484028.2484166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014502103
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1145/331499.331504 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026347712
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.14778/2212351.2212354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067367966
    147 rdf:type schema:CreativeWork
    148 https://www.grid.ac/institutes/grid.46078.3d schema:alternateName University of Waterloo
    149 schema:name University of Waterloo, N2L 3G1, Waterloo, ON, Canada
    150 rdf:type schema:Organization
     




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


    ...