Construction and distribution of materialized views in Non-binary data space View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-05-31

AUTHORS

Santanu Roy, Bibekananda Shit, Soumya Sen, Agostino Cortesi

ABSTRACT

Materialized views are heavily used to speed up the query response time of any data centric application. In the literature, the construction and dynamic maintenance of materialized views are carried out in a Binary Data Space where all attributes are given the same weight. Considering different weights may be particularly significant when similar queries are fired from multiple sites in a distributed environment, as taking into account the number of accesses to the different attribute values may reflect into the ability of tuning the materialized views accordingly. The methodology to construct weighted materialized view introduced in this paper is based on the association mining techniques, by applying it in a Non-Binary Data Space in distributed environments. The allocation of the views in the operating sites is also considered to a suitable use in distributed databases. Experimental results prove the superiority of proposed methodology on three bench mark datasets in terms of query Hit-Miss ratio and regulation of the view size with varying requirement of practical applications. More... »

PAGES

205-217

References to SciGraph publications

  • 2013-08-14. Functional dependencies are helpful for partial materialization of data cubes in ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
  • 1999-04. Bump hunting in high-dimensional data in STATISTICS AND COMPUTING
  • 2016. Advanced Computing and Systems for Security, Volume 1 in NONE
  • 2014. Materialized View Construction Based on Clustering Technique in COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT
  • 2017-03-10. Association Based Multi-attribute Analysis to Construct Materialized View in ADVANCED COMPUTING AND SYSTEMS FOR SECURITY
  • 2011. Dynamic Materialized View Selection Approach for Improving Query Performance in COMPUTER NETWORKS AND INFORMATION TECHNOLOGIES
  • 2012. A New Scale for Attribute Dependency in Large Database Systems in COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT
  • 2012. Market Basket Analysis of Retail Data: Supervised Learning Approach in COMPUTER AIDED SYSTEMS THEORY – EUROCAST 2011
  • 2012. Materialized View Selection Using Genetic Algorithm in CONTEMPORARY COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11334-021-00404-8

    DOI

    http://dx.doi.org/10.1007/s11334-021-00404-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1138505546


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information Systems", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Future Institute of Engineering and Management, Kolkata, India", 
              "id": "http://www.grid.ac/institutes/grid.440742.1", 
              "name": [
                "Future Institute of Engineering and Management, Kolkata, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Roy", 
            "givenName": "Santanu", 
            "id": "sg:person.014025130017.23", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014025130017.23"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Future Institute of Engineering and Management, Kolkata, India", 
              "id": "http://www.grid.ac/institutes/grid.440742.1", 
              "name": [
                "Future Institute of Engineering and Management, Kolkata, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shit", 
            "givenName": "Bibekananda", 
            "id": "sg:person.011470350007.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011470350007.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Calcutta, Kolkata, India", 
              "id": "http://www.grid.ac/institutes/grid.59056.3f", 
              "name": [
                "University of Calcutta, Kolkata, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sen", 
            "givenName": "Soumya", 
            "id": "sg:person.012547155071.03", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012547155071.03"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "DAIS, Ca\u2019 Foscari University, Venice, Italy", 
              "id": "http://www.grid.ac/institutes/grid.7240.1", 
              "name": [
                "DAIS, Ca\u2019 Foscari University, Venice, Italy"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cortesi", 
            "givenName": "Agostino", 
            "id": "sg:person.016321602075.60", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016321602075.60"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-19542-6_33", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006409728", 
              "https://doi.org/10.1007/978-3-642-19542-6_33"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-27549-4_59", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016387135", 
              "https://doi.org/10.1007/978-3-642-27549-4_59"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-81-322-2650-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005655117", 
              "https://doi.org/10.1007/978-81-322-2650-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-981-10-3409-1_8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084733058", 
              "https://doi.org/10.1007/978-981-10-3409-1_8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-45237-0_25", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000292977", 
              "https://doi.org/10.1007/978-3-662-45237-0_25"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10472-013-9375-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018745223", 
              "https://doi.org/10.1007/s10472-013-9375-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-32129-0_26", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044801726", 
              "https://doi.org/10.1007/978-3-642-32129-0_26"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1008894516817", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018466498", 
              "https://doi.org/10.1023/a:1008894516817"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-33260-9_23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021814293", 
              "https://doi.org/10.1007/978-3-642-33260-9_23"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-05-31", 
        "datePublishedReg": "2021-05-31", 
        "description": "Materialized views are heavily used to speed up the query response time of any data centric application. In the literature, the construction and dynamic maintenance of materialized views are carried out in a Binary Data Space where all attributes are given the same weight. Considering different weights may be particularly significant when similar queries are fired from multiple sites in a distributed environment, as taking into account the number of accesses to the different attribute values may reflect into the ability of tuning the materialized views accordingly. The methodology to construct weighted materialized view introduced in this paper is based on the association mining techniques, by applying it in a Non-Binary Data Space in distributed environments. The allocation of the views in the operating sites is also considered to a suitable use in distributed databases. Experimental results prove the superiority of proposed methodology on three bench mark datasets in terms of query Hit-Miss ratio and regulation of the view size with varying requirement of practical applications.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11334-021-00404-8", 
        "inLanguage": "en", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1044675", 
            "issn": [
              "1614-5046", 
              "1614-5054"
            ], 
            "name": "Innovations in Systems and Software Engineering", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "17"
          }
        ], 
        "keywords": [
          "materialized views", 
          "data space", 
          "query response time", 
          "data-centric applications", 
          "association mining techniques", 
          "bench mark datasets", 
          "number of accesses", 
          "different attribute values", 
          "similar queries", 
          "centric applications", 
          "mining techniques", 
          "attribute values", 
          "dynamic maintenance", 
          "response time", 
          "experimental results", 
          "different weights", 
          "view size", 
          "queries", 
          "practical applications", 
          "environment", 
          "dataset", 
          "suitable use", 
          "applications", 
          "space", 
          "methodology", 
          "same weight", 
          "requirements", 
          "allocation", 
          "view", 
          "database", 
          "attributes", 
          "superiority", 
          "access", 
          "construction", 
          "technique", 
          "multiple sites", 
          "terms", 
          "number", 
          "maintenance", 
          "time", 
          "operating site", 
          "use", 
          "results", 
          "ability", 
          "account", 
          "literature", 
          "size", 
          "weight", 
          "values", 
          "distribution", 
          "sites", 
          "ratio", 
          "regulation", 
          "paper", 
          "Binary Data Space", 
          "mark datasets", 
          "query Hit-Miss ratio", 
          "Hit-Miss ratio", 
          "Non-binary data space"
        ], 
        "name": "Construction and distribution of materialized views in Non-binary data space", 
        "pagination": "205-217", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1138505546"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11334-021-00404-8"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11334-021-00404-8", 
          "https://app.dimensions.ai/details/publication/pub.1138505546"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-01-01T19:04", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_913.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11334-021-00404-8"
      }
    ]
     

    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/s11334-021-00404-8'

    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/s11334-021-00404-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11334-021-00404-8'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11334-021-00404-8'


     

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

    180 TRIPLES      22 PREDICATES      93 URIs      76 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11334-021-00404-8 schema:about anzsrc-for:08
    2 anzsrc-for:0806
    3 schema:author N240aed47bc7349a3bb39f126253b6d46
    4 schema:citation sg:pub.10.1007/978-3-642-19542-6_33
    5 sg:pub.10.1007/978-3-642-27549-4_59
    6 sg:pub.10.1007/978-3-642-32129-0_26
    7 sg:pub.10.1007/978-3-642-33260-9_23
    8 sg:pub.10.1007/978-3-662-45237-0_25
    9 sg:pub.10.1007/978-81-322-2650-5
    10 sg:pub.10.1007/978-981-10-3409-1_8
    11 sg:pub.10.1007/s10472-013-9375-5
    12 sg:pub.10.1023/a:1008894516817
    13 schema:datePublished 2021-05-31
    14 schema:datePublishedReg 2021-05-31
    15 schema:description Materialized views are heavily used to speed up the query response time of any data centric application. In the literature, the construction and dynamic maintenance of materialized views are carried out in a Binary Data Space where all attributes are given the same weight. Considering different weights may be particularly significant when similar queries are fired from multiple sites in a distributed environment, as taking into account the number of accesses to the different attribute values may reflect into the ability of tuning the materialized views accordingly. The methodology to construct weighted materialized view introduced in this paper is based on the association mining techniques, by applying it in a Non-Binary Data Space in distributed environments. The allocation of the views in the operating sites is also considered to a suitable use in distributed databases. Experimental results prove the superiority of proposed methodology on three bench mark datasets in terms of query Hit-Miss ratio and regulation of the view size with varying requirement of practical applications.
    16 schema:genre article
    17 schema:inLanguage en
    18 schema:isAccessibleForFree false
    19 schema:isPartOf N044668416f9e47a28893133bd1254bd5
    20 N40755f981f19480cbef4b2e186b971bf
    21 sg:journal.1044675
    22 schema:keywords Binary Data Space
    23 Hit-Miss ratio
    24 Non-binary data space
    25 ability
    26 access
    27 account
    28 allocation
    29 applications
    30 association mining techniques
    31 attribute values
    32 attributes
    33 bench mark datasets
    34 centric applications
    35 construction
    36 data space
    37 data-centric applications
    38 database
    39 dataset
    40 different attribute values
    41 different weights
    42 distribution
    43 dynamic maintenance
    44 environment
    45 experimental results
    46 literature
    47 maintenance
    48 mark datasets
    49 materialized views
    50 methodology
    51 mining techniques
    52 multiple sites
    53 number
    54 number of accesses
    55 operating site
    56 paper
    57 practical applications
    58 queries
    59 query Hit-Miss ratio
    60 query response time
    61 ratio
    62 regulation
    63 requirements
    64 response time
    65 results
    66 same weight
    67 similar queries
    68 sites
    69 size
    70 space
    71 suitable use
    72 superiority
    73 technique
    74 terms
    75 time
    76 use
    77 values
    78 view
    79 view size
    80 weight
    81 schema:name Construction and distribution of materialized views in Non-binary data space
    82 schema:pagination 205-217
    83 schema:productId N3565b0430ea84d4daad79e4d5f2fbcc1
    84 Neb41919d9fcb45f2bd0b2f75ebca2335
    85 schema:sameAs https://app.dimensions.ai/details/publication/pub.1138505546
    86 https://doi.org/10.1007/s11334-021-00404-8
    87 schema:sdDatePublished 2022-01-01T19:04
    88 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    89 schema:sdPublisher Na40bce8ed43c4ac1a2fd334baded2675
    90 schema:url https://doi.org/10.1007/s11334-021-00404-8
    91 sgo:license sg:explorer/license/
    92 sgo:sdDataset articles
    93 rdf:type schema:ScholarlyArticle
    94 N044668416f9e47a28893133bd1254bd5 schema:issueNumber 3
    95 rdf:type schema:PublicationIssue
    96 N240aed47bc7349a3bb39f126253b6d46 rdf:first sg:person.014025130017.23
    97 rdf:rest Nd6cf49f2404145c28f41fddb07b3bfa8
    98 N3565b0430ea84d4daad79e4d5f2fbcc1 schema:name doi
    99 schema:value 10.1007/s11334-021-00404-8
    100 rdf:type schema:PropertyValue
    101 N40755f981f19480cbef4b2e186b971bf schema:volumeNumber 17
    102 rdf:type schema:PublicationVolume
    103 N7c770775abdb45c59d34be6dec79c175 rdf:first sg:person.012547155071.03
    104 rdf:rest Nd8bb08fff2274ff087ff55038ef5d5d1
    105 Na40bce8ed43c4ac1a2fd334baded2675 schema:name Springer Nature - SN SciGraph project
    106 rdf:type schema:Organization
    107 Nd6cf49f2404145c28f41fddb07b3bfa8 rdf:first sg:person.011470350007.55
    108 rdf:rest N7c770775abdb45c59d34be6dec79c175
    109 Nd8bb08fff2274ff087ff55038ef5d5d1 rdf:first sg:person.016321602075.60
    110 rdf:rest rdf:nil
    111 Neb41919d9fcb45f2bd0b2f75ebca2335 schema:name dimensions_id
    112 schema:value pub.1138505546
    113 rdf:type schema:PropertyValue
    114 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    115 schema:name Information and Computing Sciences
    116 rdf:type schema:DefinedTerm
    117 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    118 schema:name Information Systems
    119 rdf:type schema:DefinedTerm
    120 sg:journal.1044675 schema:issn 1614-5046
    121 1614-5054
    122 schema:name Innovations in Systems and Software Engineering
    123 schema:publisher Springer Nature
    124 rdf:type schema:Periodical
    125 sg:person.011470350007.55 schema:affiliation grid-institutes:grid.440742.1
    126 schema:familyName Shit
    127 schema:givenName Bibekananda
    128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011470350007.55
    129 rdf:type schema:Person
    130 sg:person.012547155071.03 schema:affiliation grid-institutes:grid.59056.3f
    131 schema:familyName Sen
    132 schema:givenName Soumya
    133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012547155071.03
    134 rdf:type schema:Person
    135 sg:person.014025130017.23 schema:affiliation grid-institutes:grid.440742.1
    136 schema:familyName Roy
    137 schema:givenName Santanu
    138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014025130017.23
    139 rdf:type schema:Person
    140 sg:person.016321602075.60 schema:affiliation grid-institutes:grid.7240.1
    141 schema:familyName Cortesi
    142 schema:givenName Agostino
    143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016321602075.60
    144 rdf:type schema:Person
    145 sg:pub.10.1007/978-3-642-19542-6_33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006409728
    146 https://doi.org/10.1007/978-3-642-19542-6_33
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/978-3-642-27549-4_59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016387135
    149 https://doi.org/10.1007/978-3-642-27549-4_59
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1007/978-3-642-32129-0_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044801726
    152 https://doi.org/10.1007/978-3-642-32129-0_26
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1007/978-3-642-33260-9_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021814293
    155 https://doi.org/10.1007/978-3-642-33260-9_23
    156 rdf:type schema:CreativeWork
    157 sg:pub.10.1007/978-3-662-45237-0_25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000292977
    158 https://doi.org/10.1007/978-3-662-45237-0_25
    159 rdf:type schema:CreativeWork
    160 sg:pub.10.1007/978-81-322-2650-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005655117
    161 https://doi.org/10.1007/978-81-322-2650-5
    162 rdf:type schema:CreativeWork
    163 sg:pub.10.1007/978-981-10-3409-1_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084733058
    164 https://doi.org/10.1007/978-981-10-3409-1_8
    165 rdf:type schema:CreativeWork
    166 sg:pub.10.1007/s10472-013-9375-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018745223
    167 https://doi.org/10.1007/s10472-013-9375-5
    168 rdf:type schema:CreativeWork
    169 sg:pub.10.1023/a:1008894516817 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018466498
    170 https://doi.org/10.1023/a:1008894516817
    171 rdf:type schema:CreativeWork
    172 grid-institutes:grid.440742.1 schema:alternateName Future Institute of Engineering and Management, Kolkata, India
    173 schema:name Future Institute of Engineering and Management, Kolkata, India
    174 rdf:type schema:Organization
    175 grid-institutes:grid.59056.3f schema:alternateName University of Calcutta, Kolkata, India
    176 schema:name University of Calcutta, Kolkata, India
    177 rdf:type schema:Organization
    178 grid-institutes:grid.7240.1 schema:alternateName DAIS, Ca’ Foscari University, Venice, Italy
    179 schema:name DAIS, Ca’ Foscari University, Venice, Italy
    180 rdf:type schema:Organization
     




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


    ...