Fast Streaming Behavioural Pattern Mining View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-10

AUTHORS

Tomas Chovanak, Ondrej Kassak, Michal Kompan, Maria Bielikova

ABSTRACT

Identification of typical user behaviour within a web application is a crucial assumption for revealing user characteristics, preferences and habits. Typical and repeating features of user behaviour during his/her interaction with web application can be generalized through behavioural patterns. In this paper, we propose HyBPMine—a novel method, for behavioural pattern mining over a data stream. Our method combines global patterns with patterns specific to dynamically identified groups of similar users. In this way, the method finds and combines the general global patterns (typical for high number of users) with the specific patterns (typical for user groups). We represent the patterns as frequent closed itemsets of items visited by users in their sessions. The behavioural patterns are often used for personalization, prediction or recommendation. In this paper, we evaluated the performance of our method indirectly, by applying discovered patterns in personalized recommendations. In other words, we recommended next user actions within the actual user session. We performed several experiments over data from e-learning and news domains. Our results clearly show that proposed method reaches higher precision than its components used individually as well as the state-of-the-art approaches. In addition, a inclusion of group patterns brings only low and constant computational load, which does not significantly increase resource requirements. More... »

PAGES

365-391

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00354-018-0044-4

DOI

http://dx.doi.org/10.1007/s00354-018-0044-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106097626


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": "Slovak University of Technology in Bratislava", 
          "id": "https://www.grid.ac/institutes/grid.440789.6", 
          "name": [
            "Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 841 04, Bratislava 4, Slovakia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chovanak", 
        "givenName": "Tomas", 
        "id": "sg:person.010737504720.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010737504720.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Slovak University of Technology in Bratislava", 
          "id": "https://www.grid.ac/institutes/grid.440789.6", 
          "name": [
            "Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 841 04, Bratislava 4, Slovakia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kassak", 
        "givenName": "Ondrej", 
        "id": "sg:person.013044212717.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013044212717.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Slovak University of Technology in Bratislava", 
          "id": "https://www.grid.ac/institutes/grid.440789.6", 
          "name": [
            "Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 841 04, Bratislava 4, Slovakia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kompan", 
        "givenName": "Michal", 
        "id": "sg:person.01307502001.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01307502001.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Slovak University of Technology in Bratislava", 
          "id": "https://www.grid.ac/institutes/grid.440789.6", 
          "name": [
            "Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 841 04, Bratislava 4, Slovakia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bielikova", 
        "givenName": "Maria", 
        "id": "sg:person.012464274743.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012464274743.55"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11280-015-0350-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020225725", 
          "https://doi.org/10.1007/s11280-015-0350-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10115-010-0342-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021695712", 
          "https://doi.org/10.1007/s10115-010-0342-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/342009.335372", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025244221"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/170035.170072", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028726331"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2014.03.074", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030763988"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.engappai.2016.01.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032485907"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.procs.2012.06.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040299101"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10844-007-0042-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047700188", 
          "https://doi.org/10.1007/s10844-007-0042-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2010.02.105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049462449"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmc.2015.2437327", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061794366"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmc.2015.2460691", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061794394"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.14445/22312803/ijctt-v19p103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067314049"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.19026/rjaset.8.1124", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068740798"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5120/15057-3517", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072597338"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5120/19368-1047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072601144"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5120/2980-3974", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072603872"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10115-017-1037-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084683959", 
          "https://doi.org/10.1007/s10115-017-1037-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10115-017-1037-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084683959", 
          "https://doi.org/10.1007/s10115-017-1037-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00354-017-0019-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085562227", 
          "https://doi.org/10.1007/s00354-017-0019-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00354-017-0019-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085562227", 
          "https://doi.org/10.1007/s00354-017-0019-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/1.9781611972764.29", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088800103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icde.2004.1319986", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093587884"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icde.1995.380415", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094007712"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/info9030066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101548130"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-10", 
    "datePublishedReg": "2018-10-01", 
    "description": "Identification of typical user behaviour within a web application is a crucial assumption for revealing user characteristics, preferences and habits. Typical and repeating features of user behaviour during his/her interaction with web application can be generalized through behavioural patterns. In this paper, we propose HyBPMine\u2014a novel method, for behavioural pattern mining over a data stream. Our method combines global patterns with patterns specific to dynamically identified groups of similar users. In this way, the method finds and combines the general global patterns (typical for high number of users) with the specific patterns (typical for user groups). We represent the patterns as frequent closed itemsets of items visited by users in their sessions. The behavioural patterns are often used for personalization, prediction or recommendation. In this paper, we evaluated the performance of our method indirectly, by applying discovered patterns in personalized recommendations. In other words, we recommended next user actions within the actual user session. We performed several experiments over data from e-learning and news domains. Our results clearly show that proposed method reaches higher precision than its components used individually as well as the state-of-the-art approaches. In addition, a inclusion of group patterns brings only low and constant computational load, which does not significantly increase resource requirements.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00354-018-0044-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4359291", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5521827", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1053619", 
        "issn": [
          "0288-3635", 
          "1882-7055"
        ], 
        "name": "New Generation Computing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "36"
      }
    ], 
    "name": "Fast Streaming Behavioural Pattern Mining", 
    "pagination": "365-391", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1e8bbd01680788f83254bd9a66a8e19023a13d52e56eb7bd34450957fdab6770"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00354-018-0044-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106097626"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00354-018-0044-4", 
      "https://app.dimensions.ai/details/publication/pub.1106097626"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T02:17", 
    "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_8700_00000541.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00354-018-0044-4"
  }
]
 

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/s00354-018-0044-4'

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/s00354-018-0044-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00354-018-0044-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00354-018-0044-4'


 

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

157 TRIPLES      21 PREDICATES      49 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00354-018-0044-4 schema:about anzsrc-for:08
2 anzsrc-for:0806
3 schema:author Nd9008125a8254443b1e1d752845bd8bb
4 schema:citation sg:pub.10.1007/s00354-017-0019-x
5 sg:pub.10.1007/s10115-010-0342-8
6 sg:pub.10.1007/s10115-017-1037-1
7 sg:pub.10.1007/s10844-007-0042-3
8 sg:pub.10.1007/s11280-015-0350-4
9 https://doi.org/10.1016/j.engappai.2016.01.018
10 https://doi.org/10.1016/j.eswa.2010.02.105
11 https://doi.org/10.1016/j.ins.2014.03.074
12 https://doi.org/10.1016/j.procs.2012.06.017
13 https://doi.org/10.1109/icde.1995.380415
14 https://doi.org/10.1109/icde.2004.1319986
15 https://doi.org/10.1109/tsmc.2015.2437327
16 https://doi.org/10.1109/tsmc.2015.2460691
17 https://doi.org/10.1137/1.9781611972764.29
18 https://doi.org/10.1145/170035.170072
19 https://doi.org/10.1145/342009.335372
20 https://doi.org/10.14445/22312803/ijctt-v19p103
21 https://doi.org/10.19026/rjaset.8.1124
22 https://doi.org/10.3390/info9030066
23 https://doi.org/10.5120/15057-3517
24 https://doi.org/10.5120/19368-1047
25 https://doi.org/10.5120/2980-3974
26 schema:datePublished 2018-10
27 schema:datePublishedReg 2018-10-01
28 schema:description Identification of typical user behaviour within a web application is a crucial assumption for revealing user characteristics, preferences and habits. Typical and repeating features of user behaviour during his/her interaction with web application can be generalized through behavioural patterns. In this paper, we propose HyBPMine—a novel method, for behavioural pattern mining over a data stream. Our method combines global patterns with patterns specific to dynamically identified groups of similar users. In this way, the method finds and combines the general global patterns (typical for high number of users) with the specific patterns (typical for user groups). We represent the patterns as frequent closed itemsets of items visited by users in their sessions. The behavioural patterns are often used for personalization, prediction or recommendation. In this paper, we evaluated the performance of our method indirectly, by applying discovered patterns in personalized recommendations. In other words, we recommended next user actions within the actual user session. We performed several experiments over data from e-learning and news domains. Our results clearly show that proposed method reaches higher precision than its components used individually as well as the state-of-the-art approaches. In addition, a inclusion of group patterns brings only low and constant computational load, which does not significantly increase resource requirements.
29 schema:genre research_article
30 schema:inLanguage en
31 schema:isAccessibleForFree false
32 schema:isPartOf N2bc51541bf6b48479acf7c25301d699a
33 N8eaa58220df4422681e4291f8dd52aff
34 sg:journal.1053619
35 schema:name Fast Streaming Behavioural Pattern Mining
36 schema:pagination 365-391
37 schema:productId N1aad5639088f4cc29de9688240ae9344
38 N2da12aa3714c420bb0a1870d35234715
39 Ne79da2aeba454d0181245ff022b83574
40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106097626
41 https://doi.org/10.1007/s00354-018-0044-4
42 schema:sdDatePublished 2019-04-11T02:17
43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
44 schema:sdPublisher Nec373c3002014f8ba3fe4e18a3fec00c
45 schema:url https://link.springer.com/10.1007%2Fs00354-018-0044-4
46 sgo:license sg:explorer/license/
47 sgo:sdDataset articles
48 rdf:type schema:ScholarlyArticle
49 N1aad5639088f4cc29de9688240ae9344 schema:name readcube_id
50 schema:value 1e8bbd01680788f83254bd9a66a8e19023a13d52e56eb7bd34450957fdab6770
51 rdf:type schema:PropertyValue
52 N2bc51541bf6b48479acf7c25301d699a schema:issueNumber 4
53 rdf:type schema:PublicationIssue
54 N2da12aa3714c420bb0a1870d35234715 schema:name doi
55 schema:value 10.1007/s00354-018-0044-4
56 rdf:type schema:PropertyValue
57 N8813ab68ac824955a854b4f960f948f5 rdf:first sg:person.013044212717.28
58 rdf:rest Nc207869fa0104b8fbea736043eae5ee3
59 N8eaa58220df4422681e4291f8dd52aff schema:volumeNumber 36
60 rdf:type schema:PublicationVolume
61 Nc207869fa0104b8fbea736043eae5ee3 rdf:first sg:person.01307502001.67
62 rdf:rest Ndd3d025a095b44f3946d58868531f557
63 Nd9008125a8254443b1e1d752845bd8bb rdf:first sg:person.010737504720.39
64 rdf:rest N8813ab68ac824955a854b4f960f948f5
65 Ndd3d025a095b44f3946d58868531f557 rdf:first sg:person.012464274743.55
66 rdf:rest rdf:nil
67 Ne79da2aeba454d0181245ff022b83574 schema:name dimensions_id
68 schema:value pub.1106097626
69 rdf:type schema:PropertyValue
70 Nec373c3002014f8ba3fe4e18a3fec00c schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
73 schema:name Information and Computing Sciences
74 rdf:type schema:DefinedTerm
75 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
76 schema:name Information Systems
77 rdf:type schema:DefinedTerm
78 sg:grant.4359291 http://pending.schema.org/fundedItem sg:pub.10.1007/s00354-018-0044-4
79 rdf:type schema:MonetaryGrant
80 sg:grant.5521827 http://pending.schema.org/fundedItem sg:pub.10.1007/s00354-018-0044-4
81 rdf:type schema:MonetaryGrant
82 sg:journal.1053619 schema:issn 0288-3635
83 1882-7055
84 schema:name New Generation Computing
85 rdf:type schema:Periodical
86 sg:person.010737504720.39 schema:affiliation https://www.grid.ac/institutes/grid.440789.6
87 schema:familyName Chovanak
88 schema:givenName Tomas
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010737504720.39
90 rdf:type schema:Person
91 sg:person.012464274743.55 schema:affiliation https://www.grid.ac/institutes/grid.440789.6
92 schema:familyName Bielikova
93 schema:givenName Maria
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012464274743.55
95 rdf:type schema:Person
96 sg:person.013044212717.28 schema:affiliation https://www.grid.ac/institutes/grid.440789.6
97 schema:familyName Kassak
98 schema:givenName Ondrej
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013044212717.28
100 rdf:type schema:Person
101 sg:person.01307502001.67 schema:affiliation https://www.grid.ac/institutes/grid.440789.6
102 schema:familyName Kompan
103 schema:givenName Michal
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01307502001.67
105 rdf:type schema:Person
106 sg:pub.10.1007/s00354-017-0019-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1085562227
107 https://doi.org/10.1007/s00354-017-0019-x
108 rdf:type schema:CreativeWork
109 sg:pub.10.1007/s10115-010-0342-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021695712
110 https://doi.org/10.1007/s10115-010-0342-8
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/s10115-017-1037-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084683959
113 https://doi.org/10.1007/s10115-017-1037-1
114 rdf:type schema:CreativeWork
115 sg:pub.10.1007/s10844-007-0042-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047700188
116 https://doi.org/10.1007/s10844-007-0042-3
117 rdf:type schema:CreativeWork
118 sg:pub.10.1007/s11280-015-0350-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020225725
119 https://doi.org/10.1007/s11280-015-0350-4
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.engappai.2016.01.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032485907
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.eswa.2010.02.105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049462449
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.ins.2014.03.074 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030763988
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.procs.2012.06.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040299101
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1109/icde.1995.380415 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094007712
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/icde.2004.1319986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093587884
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1109/tsmc.2015.2437327 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061794366
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/tsmc.2015.2460691 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061794394
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1137/1.9781611972764.29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088800103
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1145/170035.170072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028726331
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1145/342009.335372 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025244221
142 rdf:type schema:CreativeWork
143 https://doi.org/10.14445/22312803/ijctt-v19p103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067314049
144 rdf:type schema:CreativeWork
145 https://doi.org/10.19026/rjaset.8.1124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068740798
146 rdf:type schema:CreativeWork
147 https://doi.org/10.3390/info9030066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101548130
148 rdf:type schema:CreativeWork
149 https://doi.org/10.5120/15057-3517 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072597338
150 rdf:type schema:CreativeWork
151 https://doi.org/10.5120/19368-1047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072601144
152 rdf:type schema:CreativeWork
153 https://doi.org/10.5120/2980-3974 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072603872
154 rdf:type schema:CreativeWork
155 https://www.grid.ac/institutes/grid.440789.6 schema:alternateName Slovak University of Technology in Bratislava
156 schema:name Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 841 04, Bratislava 4, Slovakia
157 rdf:type schema:Organization
 




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


...