Integrity Constraints over Association Rules View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

Artur Bykowski , Thomas Daurel , Nicolas Méger , Christophe Rigotti

ABSTRACT

In this paper, we propose to investigate the notion of integrity constraints in inductive databases. We advocate that integrity constraints can be used in this context as an abstract concept to encompass common data mining tasks such as the detection of corrupted data or of patterns that contradict the expert beliefs. To illustrate this possibility we propose a form of constraints called association map constraints to specify authorized confidence variations among the association rules. These constraints are easy to read and thus can be used to write clear specifications. We also present experiments showing that their satisfaction can be tested in practice. More... »

PAGES

306-323

References to SciGraph publications

  • 1999-03. Discovery of frequent DATALOG patterns in DATA MINING AND KNOWLEDGE DISCOVERY
  • 1998. Querying inductive databases: A case study on the MINE RULE operator in PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY
  • 2003-01. Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2002-07-18. Approximation of Frequency Queries by Means of Free-Sets in PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY
  • Book

    TITLE

    Database Support for Data Mining Applications

    ISBN

    978-3-540-22479-2
    978-3-540-44497-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-44497-8_16

    DOI

    http://dx.doi.org/10.1007/978-3-540-44497-8_16

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1020818740


    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": "Institut National des Sciences Appliqu\u00e9es de Lyon", 
              "id": "https://www.grid.ac/institutes/grid.15399.37", 
              "name": [
                "Laboratoire d\u2019Informatique de Recherche en Image et Syst\u00e8mes d\u2019information (LIRIS), INSA Lyon, B\u00e2timent Blaise Pascal, 69621 Cedex, Villeurbanne, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bykowski", 
            "givenName": "Artur", 
            "id": "sg:person.016652325453.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016652325453.86"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Schlumberger (France)", 
              "id": "https://www.grid.ac/institutes/grid.410410.0", 
              "name": [
                "Laboratoire d\u2019Informatique de Recherche en Image et Syst\u00e8mes d\u2019information (LIRIS), INSA Lyon, B\u00e2timent Blaise Pascal, 69621 Cedex, Villeurbanne, France", 
                "Etudes et Productions Schlumberger, 1, rue Henri Becquerel, 92142 Cedex, Clamart, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Daurel", 
            "givenName": "Thomas", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institut National des Sciences Appliqu\u00e9es de Lyon", 
              "id": "https://www.grid.ac/institutes/grid.15399.37", 
              "name": [
                "Laboratoire d\u2019Informatique de Recherche en Image et Syst\u00e8mes d\u2019information (LIRIS), INSA Lyon, B\u00e2timent Blaise Pascal, 69621 Cedex, Villeurbanne, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "M\u00e9ger", 
            "givenName": "Nicolas", 
            "id": "sg:person.011033776133.74", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011033776133.74"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institut National des Sciences Appliqu\u00e9es de Lyon", 
              "id": "https://www.grid.ac/institutes/grid.15399.37", 
              "name": [
                "Laboratoire d\u2019Informatique de Recherche en Image et Syst\u00e8mes d\u2019information (LIRIS), INSA Lyon, B\u00e2timent Blaise Pascal, 69621 Cedex, Villeurbanne, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Rigotti", 
            "givenName": "Christophe", 
            "id": "sg:person.016577712467.20", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016577712467.20"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/s0306-4379(99)00003-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000463430"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-45372-5_8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002430884", 
              "https://doi.org/10.1007/3-540-45372-5_8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-45372-5_8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002430884", 
              "https://doi.org/10.1007/3-540-45372-5_8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/240455.240472", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006761471"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1009863704807", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011882918", 
              "https://doi.org/10.1023/a:1009863704807"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/312129.312216", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016125043"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/375551.375604", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019402812"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/233269.233311", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019413618"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/276305.276313", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028415526"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/170035.170072", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028726331"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bfb0094820", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035015916", 
              "https://doi.org/10.1007/bfb0094820"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/360402.360421", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044219887"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1021571501451", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052843726", 
              "https://doi.org/10.1023/a:1021571501451"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icde.1999.754924", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094800571"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/276304.276313", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098992874"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2004", 
        "datePublishedReg": "2004-01-01", 
        "description": "In this paper, we propose to investigate the notion of integrity constraints in inductive databases. We advocate that integrity constraints can be used in this context as an abstract concept to encompass common data mining tasks such as the detection of corrupted data or of patterns that contradict the expert beliefs. To illustrate this possibility we propose a form of constraints called association map constraints to specify authorized confidence variations among the association rules. These constraints are easy to read and thus can be used to write clear specifications. We also present experiments showing that their satisfaction can be tested in practice.", 
        "editor": [
          {
            "familyName": "Meo", 
            "givenName": "Rosa", 
            "type": "Person"
          }, 
          {
            "familyName": "Lanzi", 
            "givenName": "Pier Luca", 
            "type": "Person"
          }, 
          {
            "familyName": "Klemettinen", 
            "givenName": "Mika", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-540-44497-8_16", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-540-22479-2", 
            "978-3-540-44497-8"
          ], 
          "name": "Database Support for Data Mining Applications", 
          "type": "Book"
        }, 
        "name": "Integrity Constraints over Association Rules", 
        "pagination": "306-323", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1020818740"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-540-44497-8_16"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "6331c22f386dbde2a8316436d6ebcf1490e655ed95505a8fb35d6ccf87e1c3e6"
            ]
          }
        ], 
        "publisher": {
          "location": "Berlin, Heidelberg", 
          "name": "Springer Berlin Heidelberg", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-540-44497-8_16", 
          "https://app.dimensions.ai/details/publication/pub.1020818740"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T07:29", 
        "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/v90/0000000356_0000000356/records_57871_00000000.jsonl", 
        "type": "Chapter", 
        "url": "https://link.springer.com/10.1007%2F978-3-540-44497-8_16"
      }
    ]
     

    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.10.1007/978-3-540-44497-8_16'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-44497-8_16'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-44497-8_16'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-44497-8_16'


     

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

    145 TRIPLES      23 PREDICATES      41 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-540-44497-8_16 schema:about anzsrc-for:08
    2 anzsrc-for:0806
    3 schema:author Naed82f8f7771498a9a86ef89ca4a5251
    4 schema:citation sg:pub.10.1007/3-540-45372-5_8
    5 sg:pub.10.1007/bfb0094820
    6 sg:pub.10.1023/a:1009863704807
    7 sg:pub.10.1023/a:1021571501451
    8 https://doi.org/10.1016/s0306-4379(99)00003-4
    9 https://doi.org/10.1109/icde.1999.754924
    10 https://doi.org/10.1145/170035.170072
    11 https://doi.org/10.1145/233269.233311
    12 https://doi.org/10.1145/240455.240472
    13 https://doi.org/10.1145/276304.276313
    14 https://doi.org/10.1145/276305.276313
    15 https://doi.org/10.1145/312129.312216
    16 https://doi.org/10.1145/360402.360421
    17 https://doi.org/10.1145/375551.375604
    18 schema:datePublished 2004
    19 schema:datePublishedReg 2004-01-01
    20 schema:description In this paper, we propose to investigate the notion of integrity constraints in inductive databases. We advocate that integrity constraints can be used in this context as an abstract concept to encompass common data mining tasks such as the detection of corrupted data or of patterns that contradict the expert beliefs. To illustrate this possibility we propose a form of constraints called association map constraints to specify authorized confidence variations among the association rules. These constraints are easy to read and thus can be used to write clear specifications. We also present experiments showing that their satisfaction can be tested in practice.
    21 schema:editor N50d02eb6f1c7480cb08e00e6ed2502bb
    22 schema:genre chapter
    23 schema:inLanguage en
    24 schema:isAccessibleForFree false
    25 schema:isPartOf N2392a4c24ff449ad8353669aec894016
    26 schema:name Integrity Constraints over Association Rules
    27 schema:pagination 306-323
    28 schema:productId N07500c17bd5a4743a072231765f8f49e
    29 N203b4624c8504ffcae5ba567e06e6bfa
    30 N9868aff0632548868111497f3512359e
    31 schema:publisher Nb6bdf7d7520c4281823df5a9399bc08b
    32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020818740
    33 https://doi.org/10.1007/978-3-540-44497-8_16
    34 schema:sdDatePublished 2019-04-16T07:29
    35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    36 schema:sdPublisher N7950af49eb3b40ab88ca2f03667afbfb
    37 schema:url https://link.springer.com/10.1007%2F978-3-540-44497-8_16
    38 sgo:license sg:explorer/license/
    39 sgo:sdDataset chapters
    40 rdf:type schema:Chapter
    41 N0321c20f94524f6ea9ef128cc1503cf9 schema:affiliation https://www.grid.ac/institutes/grid.410410.0
    42 schema:familyName Daurel
    43 schema:givenName Thomas
    44 rdf:type schema:Person
    45 N07500c17bd5a4743a072231765f8f49e schema:name doi
    46 schema:value 10.1007/978-3-540-44497-8_16
    47 rdf:type schema:PropertyValue
    48 N16352ea7311942e5aaca0671e28f549f rdf:first N3b82adb12b9541829c7cc9c7fd5523c5
    49 rdf:rest rdf:nil
    50 N203b4624c8504ffcae5ba567e06e6bfa schema:name readcube_id
    51 schema:value 6331c22f386dbde2a8316436d6ebcf1490e655ed95505a8fb35d6ccf87e1c3e6
    52 rdf:type schema:PropertyValue
    53 N2392a4c24ff449ad8353669aec894016 schema:isbn 978-3-540-22479-2
    54 978-3-540-44497-8
    55 schema:name Database Support for Data Mining Applications
    56 rdf:type schema:Book
    57 N3b82adb12b9541829c7cc9c7fd5523c5 schema:familyName Klemettinen
    58 schema:givenName Mika
    59 rdf:type schema:Person
    60 N4f6cc84122eb4f84bacbbe76766d5d4e schema:familyName Meo
    61 schema:givenName Rosa
    62 rdf:type schema:Person
    63 N50d02eb6f1c7480cb08e00e6ed2502bb rdf:first N4f6cc84122eb4f84bacbbe76766d5d4e
    64 rdf:rest Ne08617fa4e5947b4ab18443208b36ce2
    65 N674d205ad16249e7ac5abf5bebcfdeda rdf:first sg:person.011033776133.74
    66 rdf:rest Nad652d36480c4a5892da3ed0d7d295f6
    67 N7950af49eb3b40ab88ca2f03667afbfb schema:name Springer Nature - SN SciGraph project
    68 rdf:type schema:Organization
    69 N9868aff0632548868111497f3512359e schema:name dimensions_id
    70 schema:value pub.1020818740
    71 rdf:type schema:PropertyValue
    72 Na7ab1c4959f241e794e516d86d81d08f schema:familyName Lanzi
    73 schema:givenName Pier Luca
    74 rdf:type schema:Person
    75 Nad652d36480c4a5892da3ed0d7d295f6 rdf:first sg:person.016577712467.20
    76 rdf:rest rdf:nil
    77 Naed82f8f7771498a9a86ef89ca4a5251 rdf:first sg:person.016652325453.86
    78 rdf:rest Ncc1f313573324599950e887193a05912
    79 Nb6bdf7d7520c4281823df5a9399bc08b schema:location Berlin, Heidelberg
    80 schema:name Springer Berlin Heidelberg
    81 rdf:type schema:Organisation
    82 Ncc1f313573324599950e887193a05912 rdf:first N0321c20f94524f6ea9ef128cc1503cf9
    83 rdf:rest N674d205ad16249e7ac5abf5bebcfdeda
    84 Ne08617fa4e5947b4ab18443208b36ce2 rdf:first Na7ab1c4959f241e794e516d86d81d08f
    85 rdf:rest N16352ea7311942e5aaca0671e28f549f
    86 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    87 schema:name Information and Computing Sciences
    88 rdf:type schema:DefinedTerm
    89 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    90 schema:name Information Systems
    91 rdf:type schema:DefinedTerm
    92 sg:person.011033776133.74 schema:affiliation https://www.grid.ac/institutes/grid.15399.37
    93 schema:familyName Méger
    94 schema:givenName Nicolas
    95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011033776133.74
    96 rdf:type schema:Person
    97 sg:person.016577712467.20 schema:affiliation https://www.grid.ac/institutes/grid.15399.37
    98 schema:familyName Rigotti
    99 schema:givenName Christophe
    100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016577712467.20
    101 rdf:type schema:Person
    102 sg:person.016652325453.86 schema:affiliation https://www.grid.ac/institutes/grid.15399.37
    103 schema:familyName Bykowski
    104 schema:givenName Artur
    105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016652325453.86
    106 rdf:type schema:Person
    107 sg:pub.10.1007/3-540-45372-5_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002430884
    108 https://doi.org/10.1007/3-540-45372-5_8
    109 rdf:type schema:CreativeWork
    110 sg:pub.10.1007/bfb0094820 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035015916
    111 https://doi.org/10.1007/bfb0094820
    112 rdf:type schema:CreativeWork
    113 sg:pub.10.1023/a:1009863704807 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011882918
    114 https://doi.org/10.1023/a:1009863704807
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1023/a:1021571501451 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052843726
    117 https://doi.org/10.1023/a:1021571501451
    118 rdf:type schema:CreativeWork
    119 https://doi.org/10.1016/s0306-4379(99)00003-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000463430
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1109/icde.1999.754924 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094800571
    122 rdf:type schema:CreativeWork
    123 https://doi.org/10.1145/170035.170072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028726331
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1145/233269.233311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019413618
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1145/240455.240472 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006761471
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1145/276304.276313 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098992874
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1145/276305.276313 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028415526
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1145/312129.312216 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016125043
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1145/360402.360421 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044219887
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1145/375551.375604 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019402812
    138 rdf:type schema:CreativeWork
    139 https://www.grid.ac/institutes/grid.15399.37 schema:alternateName Institut National des Sciences Appliquées de Lyon
    140 schema:name Laboratoire d’Informatique de Recherche en Image et Systèmes d’information (LIRIS), INSA Lyon, Bâtiment Blaise Pascal, 69621 Cedex, Villeurbanne, France
    141 rdf:type schema:Organization
    142 https://www.grid.ac/institutes/grid.410410.0 schema:alternateName Schlumberger (France)
    143 schema:name Etudes et Productions Schlumberger, 1, rue Henri Becquerel, 92142 Cedex, Clamart, France
    144 Laboratoire d’Informatique de Recherche en Image et Systèmes d’information (LIRIS), INSA Lyon, Bâtiment Blaise Pascal, 69621 Cedex, Villeurbanne, France
    145 rdf:type schema:Organization
     




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


    ...