TIDES—a new descriptor for time series oscillation behavior View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-01

AUTHORS

Leonardo E. Mariote, Claudia Bauzer Medeiros, Ricardo da Silva Torres, Lucas M. Bueno

ABSTRACT

Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. There is a wide range of techniques to describe and compare time series, but they focus on series’ values. This paper concentrates on a new aspect—that of describing oscillation patterns. It presents a technique for time series similarity search, and multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. This technique is generalized to handle co-evolution, in which several phenomena vary at the same time. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of single time series, for multiple time scales, and is able to compute the similarity among sets of co-evolving series. More... »

PAGES

75-109

References to SciGraph publications

  • 2008-03. Techniques for Computing Fitness of Use (FoU) for Time Series Datasets with Applications in the Geospatial Domain in GEOINFORMATICA
  • 2000. A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases in KNOWLEDGE DISCOVERY AND DATA MINING. CURRENT ISSUES AND NEW APPLICATIONS
  • 2005-03. Exact indexing of dynamic time warping in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2007-10. Data mining with Temporal Abstractions: learning rules from time series in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2007-10. Experiencing SAX: a novel symbolic representation of time series in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2004. Lessons from a Sensor Network Expedition in WIRELESS SENSOR NETWORKS
  • 2007. APCAS: An Approximate Approach to Adaptively Segment Time Series Stream in ADVANCES IN DATA AND WEB MANAGEMENT
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10707-010-0112-5

    DOI

    http://dx.doi.org/10.1007/s10707-010-0112-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1021668466


    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": "State University of Campinas", 
              "id": "https://www.grid.ac/institutes/grid.411087.b", 
              "name": [
                "Institute of Computing, University of Campinas\u2014CP6176, 13084-851, Campinas, S\u00e3o Paulo, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Mariote", 
            "givenName": "Leonardo E.", 
            "id": "sg:person.015617032073.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617032073.51"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State University of Campinas", 
              "id": "https://www.grid.ac/institutes/grid.411087.b", 
              "name": [
                "Institute of Computing, University of Campinas\u2014CP6176, 13084-851, Campinas, S\u00e3o Paulo, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Medeiros", 
            "givenName": "Claudia Bauzer", 
            "id": "sg:person.011537123441.73", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011537123441.73"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State University of Campinas", 
              "id": "https://www.grid.ac/institutes/grid.411087.b", 
              "name": [
                "Institute of Computing, University of Campinas\u2014CP6176, 13084-851, Campinas, S\u00e3o Paulo, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Torres", 
            "givenName": "Ricardo da Silva", 
            "id": "sg:person.0703703457.58", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703703457.58"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State University of Campinas", 
              "id": "https://www.grid.ac/institutes/grid.411087.b", 
              "name": [
                "Institute of Computing, University of Campinas\u2014CP6176, 13084-851, Campinas, S\u00e3o Paulo, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bueno", 
            "givenName": "Lucas M.", 
            "id": "sg:person.015677437531.56", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015677437531.56"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/882082.882086", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003687047"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-72524-4_57", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004619692", 
              "https://doi.org/10.1007/978-3-540-72524-4_57"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10618-007-0064-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006340982", 
              "https://doi.org/10.1007/s10618-007-0064-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10618-007-0064-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006340982", 
              "https://doi.org/10.1007/s10618-007-0064-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1066157.1066235", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011279287"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-012088469-8.50053-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011902476"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/253260.253264", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013628981"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/543613.543615", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018126042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/570738.570751", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018579220"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10707-007-0025-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023012031", 
              "https://doi.org/10.1007/s10707-007-0025-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/776985.776986", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031364351"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10618-007-0077-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034457471", 
              "https://doi.org/10.1007/s10618-007-0077-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/347090.347167", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035246359"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1142473.1142483", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035342231"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-45571-x_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036055360", 
              "https://doi.org/10.1007/3-540-45571-x_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/191839.191925", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037554782"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/376284.375680", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039316236"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10115-004-0154-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047997676", 
              "https://doi.org/10.1007/s10115-004-0154-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/375360.375365", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048171690"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tcs.2006.10.032", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048649681"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1389-1286(01)00302-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048798332"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-24606-0_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052491299", 
              "https://doi.org/10.1007/978-3-540-24606-0_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-24606-0_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052491299", 
              "https://doi.org/10.1007/978-3-540-24606-0_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/34.895972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061157192"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.14778/1454159.1454226", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067367464"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icde.2000.839383", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094001407"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/isda.2007.54", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094074396"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/kdex.1999.836610", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094151199"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdm.2001.989531", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094205033"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdmw.2007.28", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094432137"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdsp.2002.1028280", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094857978"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdmw.2007.76", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095045811"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/re.2005.37", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095133103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/re.2005.37", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095133103"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2011-01", 
        "datePublishedReg": "2011-01-01", 
        "description": "Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. There is a wide range of techniques to describe and compare time series, but they focus on series\u2019 values. This paper concentrates on a new aspect\u2014that of describing oscillation patterns. It presents a technique for time series similarity search, and multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. This technique is generalized to handle co-evolution, in which several phenomena vary at the same time. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of single time series, for multiple time scales, and is able to compute the similarity among sets of co-evolving series.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10707-010-0112-5", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1043850", 
            "issn": [
              "1384-6175", 
              "1573-7624"
            ], 
            "name": "GeoInformatica", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "15"
          }
        ], 
        "name": "TIDES\u2014a new descriptor for time series oscillation behavior", 
        "pagination": "75-109", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "673d1e8d15368cb74f39d1b7c52945ac2041293f82e9e4e48cf330014970be6e"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10707-010-0112-5"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1021668466"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10707-010-0112-5", 
          "https://app.dimensions.ai/details/publication/pub.1021668466"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T17:31", 
        "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_8672_00000512.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs10707-010-0112-5"
      }
    ]
     

    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/s10707-010-0112-5'

    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/s10707-010-0112-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10707-010-0112-5'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10707-010-0112-5'


     

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

    182 TRIPLES      21 PREDICATES      58 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10707-010-0112-5 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nabdd64ff81cc47a4928d44e9ccd775c4
    4 schema:citation sg:pub.10.1007/3-540-45571-x_14
    5 sg:pub.10.1007/978-3-540-24606-0_21
    6 sg:pub.10.1007/978-3-540-72524-4_57
    7 sg:pub.10.1007/s10115-004-0154-9
    8 sg:pub.10.1007/s10618-007-0064-z
    9 sg:pub.10.1007/s10618-007-0077-7
    10 sg:pub.10.1007/s10707-007-0025-0
    11 https://doi.org/10.1016/b978-012088469-8.50053-x
    12 https://doi.org/10.1016/j.tcs.2006.10.032
    13 https://doi.org/10.1016/s1389-1286(01)00302-4
    14 https://doi.org/10.1109/34.895972
    15 https://doi.org/10.1109/icde.2000.839383
    16 https://doi.org/10.1109/icdm.2001.989531
    17 https://doi.org/10.1109/icdmw.2007.28
    18 https://doi.org/10.1109/icdmw.2007.76
    19 https://doi.org/10.1109/icdsp.2002.1028280
    20 https://doi.org/10.1109/isda.2007.54
    21 https://doi.org/10.1109/kdex.1999.836610
    22 https://doi.org/10.1109/re.2005.37
    23 https://doi.org/10.1145/1066157.1066235
    24 https://doi.org/10.1145/1142473.1142483
    25 https://doi.org/10.1145/191839.191925
    26 https://doi.org/10.1145/253260.253264
    27 https://doi.org/10.1145/347090.347167
    28 https://doi.org/10.1145/375360.375365
    29 https://doi.org/10.1145/376284.375680
    30 https://doi.org/10.1145/543613.543615
    31 https://doi.org/10.1145/570738.570751
    32 https://doi.org/10.1145/776985.776986
    33 https://doi.org/10.1145/882082.882086
    34 https://doi.org/10.14778/1454159.1454226
    35 schema:datePublished 2011-01
    36 schema:datePublishedReg 2011-01-01
    37 schema:description Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. There is a wide range of techniques to describe and compare time series, but they focus on series’ values. This paper concentrates on a new aspect—that of describing oscillation patterns. It presents a technique for time series similarity search, and multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. This technique is generalized to handle co-evolution, in which several phenomena vary at the same time. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of single time series, for multiple time scales, and is able to compute the similarity among sets of co-evolving series.
    38 schema:genre research_article
    39 schema:inLanguage en
    40 schema:isAccessibleForFree false
    41 schema:isPartOf N8287dde0d3d146afadebb6f76724eaff
    42 Nb256686301484316aaad0061d101f7fc
    43 sg:journal.1043850
    44 schema:name TIDES—a new descriptor for time series oscillation behavior
    45 schema:pagination 75-109
    46 schema:productId N32fe303c97fb4e0190cfce2163a8f4a7
    47 Nced155f1ab5d4b83b1a9da97663f3989
    48 Ncf7b299b98ed493b87527423ec9dd39f
    49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021668466
    50 https://doi.org/10.1007/s10707-010-0112-5
    51 schema:sdDatePublished 2019-04-10T17:31
    52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    53 schema:sdPublisher Nda237120b6d24e499a6aedec67753154
    54 schema:url http://link.springer.com/10.1007%2Fs10707-010-0112-5
    55 sgo:license sg:explorer/license/
    56 sgo:sdDataset articles
    57 rdf:type schema:ScholarlyArticle
    58 N32fe303c97fb4e0190cfce2163a8f4a7 schema:name dimensions_id
    59 schema:value pub.1021668466
    60 rdf:type schema:PropertyValue
    61 N4b9e1f27274b4454acd2049863e4ec0d rdf:first sg:person.015677437531.56
    62 rdf:rest rdf:nil
    63 N60a3eb7ba19c4862a6ea2140e4f59884 rdf:first sg:person.011537123441.73
    64 rdf:rest Nef380c3c8a0a48009620a5b718f8cb40
    65 N8287dde0d3d146afadebb6f76724eaff schema:volumeNumber 15
    66 rdf:type schema:PublicationVolume
    67 Nabdd64ff81cc47a4928d44e9ccd775c4 rdf:first sg:person.015617032073.51
    68 rdf:rest N60a3eb7ba19c4862a6ea2140e4f59884
    69 Nb256686301484316aaad0061d101f7fc schema:issueNumber 1
    70 rdf:type schema:PublicationIssue
    71 Nced155f1ab5d4b83b1a9da97663f3989 schema:name doi
    72 schema:value 10.1007/s10707-010-0112-5
    73 rdf:type schema:PropertyValue
    74 Ncf7b299b98ed493b87527423ec9dd39f schema:name readcube_id
    75 schema:value 673d1e8d15368cb74f39d1b7c52945ac2041293f82e9e4e48cf330014970be6e
    76 rdf:type schema:PropertyValue
    77 Nda237120b6d24e499a6aedec67753154 schema:name Springer Nature - SN SciGraph project
    78 rdf:type schema:Organization
    79 Nef380c3c8a0a48009620a5b718f8cb40 rdf:first sg:person.0703703457.58
    80 rdf:rest N4b9e1f27274b4454acd2049863e4ec0d
    81 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    82 schema:name Information and Computing Sciences
    83 rdf:type schema:DefinedTerm
    84 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    85 schema:name Artificial Intelligence and Image Processing
    86 rdf:type schema:DefinedTerm
    87 sg:journal.1043850 schema:issn 1384-6175
    88 1573-7624
    89 schema:name GeoInformatica
    90 rdf:type schema:Periodical
    91 sg:person.011537123441.73 schema:affiliation https://www.grid.ac/institutes/grid.411087.b
    92 schema:familyName Medeiros
    93 schema:givenName Claudia Bauzer
    94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011537123441.73
    95 rdf:type schema:Person
    96 sg:person.015617032073.51 schema:affiliation https://www.grid.ac/institutes/grid.411087.b
    97 schema:familyName Mariote
    98 schema:givenName Leonardo E.
    99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617032073.51
    100 rdf:type schema:Person
    101 sg:person.015677437531.56 schema:affiliation https://www.grid.ac/institutes/grid.411087.b
    102 schema:familyName Bueno
    103 schema:givenName Lucas M.
    104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015677437531.56
    105 rdf:type schema:Person
    106 sg:person.0703703457.58 schema:affiliation https://www.grid.ac/institutes/grid.411087.b
    107 schema:familyName Torres
    108 schema:givenName Ricardo da Silva
    109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703703457.58
    110 rdf:type schema:Person
    111 sg:pub.10.1007/3-540-45571-x_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036055360
    112 https://doi.org/10.1007/3-540-45571-x_14
    113 rdf:type schema:CreativeWork
    114 sg:pub.10.1007/978-3-540-24606-0_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052491299
    115 https://doi.org/10.1007/978-3-540-24606-0_21
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/978-3-540-72524-4_57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004619692
    118 https://doi.org/10.1007/978-3-540-72524-4_57
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/s10115-004-0154-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047997676
    121 https://doi.org/10.1007/s10115-004-0154-9
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/s10618-007-0064-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1006340982
    124 https://doi.org/10.1007/s10618-007-0064-z
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1007/s10618-007-0077-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034457471
    127 https://doi.org/10.1007/s10618-007-0077-7
    128 rdf:type schema:CreativeWork
    129 sg:pub.10.1007/s10707-007-0025-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023012031
    130 https://doi.org/10.1007/s10707-007-0025-0
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1016/b978-012088469-8.50053-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1011902476
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1016/j.tcs.2006.10.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048649681
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1016/s1389-1286(01)00302-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048798332
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1109/34.895972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157192
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1109/icde.2000.839383 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094001407
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1109/icdm.2001.989531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094205033
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1109/icdmw.2007.28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094432137
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1109/icdmw.2007.76 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095045811
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1109/icdsp.2002.1028280 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094857978
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1109/isda.2007.54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094074396
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1109/kdex.1999.836610 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094151199
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1109/re.2005.37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095133103
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1145/1066157.1066235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011279287
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1145/1142473.1142483 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035342231
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1145/191839.191925 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037554782
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1145/253260.253264 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013628981
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1145/347090.347167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035246359
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1145/375360.375365 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048171690
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1145/376284.375680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039316236
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1145/543613.543615 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018126042
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1145/570738.570751 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018579220
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1145/776985.776986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031364351
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1145/882082.882086 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003687047
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.14778/1454159.1454226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067367464
    179 rdf:type schema:CreativeWork
    180 https://www.grid.ac/institutes/grid.411087.b schema:alternateName State University of Campinas
    181 schema:name Institute of Computing, University of Campinas—CP6176, 13084-851, Campinas, São Paulo, Brazil
    182 rdf:type schema:Organization
     




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


    ...