Artificial Intelligence Platform for Mobile Service Computing View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-06

AUTHORS

Haikuo Zhang, Zhonghua Lu, Ke Xu, Yuchen Pang, Fang Liu, Liandong Chen, Jue Wang, Yangang Wang, Rongqiang Cao

ABSTRACT

Since the birth of artificial intelligence, the theory and the technology have become more mature, and the application field is expanding. Mobile networks and applications have grown quickly in recent years, and mobile computing is the new computing paradigm for mobile networks. In this paper, we build an artificial intelligence platform for a mobile service, which supports deep learning frameworks such as TensorFlow and Caffe. We describe the overall architecture of the AI platform for a GPU cluster in mobile service computing. In the GPU cluster, based on the scheduling layer, we propose Yarn by the Slurm scheduler to not only improve the distributed TensorFlow plug-in for the Slurm scheduling layer but also to extend YARN to manage and schedule GPUs. The front-end of the high-performance AI platform has the attributes of availability, scalability and efficiency. Finally, we verify the convenience, scalability, and effectiveness of the AI platform by comparing the performance of single-chip and distributed versions for the TensorFlow, Caffe and YARN systems. More... »

PAGES

1-11

References to SciGraph publications

  • 2011-06. MVAPICH2-GPU: optimized GPU to GPU communication for InfiniBand clusters in COMPUTER SCIENCE - RESEARCH AND DEVELOPMENT
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11265-019-1438-3

    DOI

    http://dx.doi.org/10.1007/s11265-019-1438-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111949509


    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": "China Internet Network Information Center", 
              "id": "https://www.grid.ac/institutes/grid.496812.0", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China", 
                "University of Chinese Academy of Sciences, 100049, Beijing, China", 
                "China Internet Network Information Center, 100190, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Haikuo", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Chinese Academy of Sciences", 
              "id": "https://www.grid.ac/institutes/grid.410726.6", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China", 
                "University of Chinese Academy of Sciences, 100049, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lu", 
            "givenName": "Zhonghua", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Chinese Academy of Sciences", 
              "id": "https://www.grid.ac/institutes/grid.410726.6", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China", 
                "University of Chinese Academy of Sciences, 100049, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xu", 
            "givenName": "Ke", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Illinois at Urbana Champaign", 
              "id": "https://www.grid.ac/institutes/grid.35403.31", 
              "name": [
                "University of Illinois at Urbana-Champaign, 61820, Champaign, IL, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pang", 
            "givenName": "Yuchen", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Computer Network Information Center", 
              "id": "https://www.grid.ac/institutes/grid.433146.7", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Fang", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "State Grid Hebei Electric Power Company, 050022, Shijiazhuang, Hebei Province, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chen", 
            "givenName": "Liandong", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Computer Network Information Center", 
              "id": "https://www.grid.ac/institutes/grid.433146.7", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Jue", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Computer Network Information Center", 
              "id": "https://www.grid.ac/institutes/grid.433146.7", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Yangang", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Computer Network Information Center", 
              "id": "https://www.grid.ac/institutes/grid.433146.7", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cao", 
            "givenName": "Rongqiang", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.camwa.2009.08.052", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022599292"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.future.2015.02.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036006716"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.future.2016.01.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036804631"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00450-011-0171-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039789635", 
              "https://doi.org/10.1007/s00450-011-0171-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/004051759906900511", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039926050"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/004051759906900511", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039926050"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0167-8191(96)00024-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045623305"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1478-4408.2008.00134.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047806147"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1118/1.3578605", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049648597"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/jproc.2008.917757", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061296953"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/jsyst.2015.2460747", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061339651"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mcse.2011.83", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061398485"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mm.2010.41", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061408719"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tc.2015.2470247", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061536081"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1961295.1950408", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063159392"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/embc.2013.6611044", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1078797794"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icassp.2013.6639348", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094742597"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.comcom.2018.03.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101547762"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-02-06", 
        "datePublishedReg": "2019-02-06", 
        "description": "Since the birth of artificial intelligence, the theory and the technology have become more mature, and the application field is expanding. Mobile networks and applications have grown quickly in recent years, and mobile computing is the new computing paradigm for mobile networks. In this paper, we build an artificial intelligence platform for a mobile service, which supports deep learning frameworks such as TensorFlow and Caffe. We describe the overall architecture of the AI platform for a GPU cluster in mobile service computing. In the GPU cluster, based on the scheduling layer, we propose Yarn by the Slurm scheduler to not only improve the distributed TensorFlow plug-in for the Slurm scheduling layer but also to extend YARN to manage and schedule GPUs. The front-end of the high-performance AI platform has the attributes of availability, scalability and efficiency. Finally, we verify the convenience, scalability, and effectiveness of the AI platform by comparing the performance of single-chip and distributed versions for the TensorFlow, Caffe and YARN systems.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11265-019-1438-3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1297359", 
            "issn": [
              "0922-5773", 
              "1939-8115"
            ], 
            "name": "Journal of Signal Processing Systems", 
            "type": "Periodical"
          }
        ], 
        "name": "Artificial Intelligence Platform for Mobile Service Computing", 
        "pagination": "1-11", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "58889e144abf2bd5ddf31f4c9e9ff7e254a245c0893cf95188f3fceee8270fd3"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11265-019-1438-3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1111949509"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11265-019-1438-3", 
          "https://app.dimensions.ai/details/publication/pub.1111949509"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T09:02", 
        "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/v90/0000000331_0000000331/records_105440_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs11265-019-1438-3"
      }
    ]
     

    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/s11265-019-1438-3'

    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/s11265-019-1438-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11265-019-1438-3'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11265-019-1438-3'


     

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

    168 TRIPLES      21 PREDICATES      41 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11265-019-1438-3 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nb5d0d4033ead4cedac5040f825181d47
    4 schema:citation sg:pub.10.1007/s00450-011-0171-3
    5 https://doi.org/10.1016/0167-8191(96)00024-5
    6 https://doi.org/10.1016/j.camwa.2009.08.052
    7 https://doi.org/10.1016/j.comcom.2018.03.011
    8 https://doi.org/10.1016/j.future.2015.02.011
    9 https://doi.org/10.1016/j.future.2016.01.006
    10 https://doi.org/10.1109/embc.2013.6611044
    11 https://doi.org/10.1109/icassp.2013.6639348
    12 https://doi.org/10.1109/jproc.2008.917757
    13 https://doi.org/10.1109/jsyst.2015.2460747
    14 https://doi.org/10.1109/mcse.2011.83
    15 https://doi.org/10.1109/mm.2010.41
    16 https://doi.org/10.1109/tc.2015.2470247
    17 https://doi.org/10.1111/j.1478-4408.2008.00134.x
    18 https://doi.org/10.1118/1.3578605
    19 https://doi.org/10.1145/1961295.1950408
    20 https://doi.org/10.1177/004051759906900511
    21 schema:datePublished 2019-02-06
    22 schema:datePublishedReg 2019-02-06
    23 schema:description Since the birth of artificial intelligence, the theory and the technology have become more mature, and the application field is expanding. Mobile networks and applications have grown quickly in recent years, and mobile computing is the new computing paradigm for mobile networks. In this paper, we build an artificial intelligence platform for a mobile service, which supports deep learning frameworks such as TensorFlow and Caffe. We describe the overall architecture of the AI platform for a GPU cluster in mobile service computing. In the GPU cluster, based on the scheduling layer, we propose Yarn by the Slurm scheduler to not only improve the distributed TensorFlow plug-in for the Slurm scheduling layer but also to extend YARN to manage and schedule GPUs. The front-end of the high-performance AI platform has the attributes of availability, scalability and efficiency. Finally, we verify the convenience, scalability, and effectiveness of the AI platform by comparing the performance of single-chip and distributed versions for the TensorFlow, Caffe and YARN systems.
    24 schema:genre research_article
    25 schema:inLanguage en
    26 schema:isAccessibleForFree false
    27 schema:isPartOf sg:journal.1297359
    28 schema:name Artificial Intelligence Platform for Mobile Service Computing
    29 schema:pagination 1-11
    30 schema:productId N60ae418836f04a0083323db3efc8dabd
    31 Na5a82d526bc34501a270b65ea401824d
    32 Nf9e4ea90e2c04c22aba7d23e82e84550
    33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111949509
    34 https://doi.org/10.1007/s11265-019-1438-3
    35 schema:sdDatePublished 2019-04-11T09:02
    36 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    37 schema:sdPublisher N556ead40bf224fb3ab6fe8ceefaf20f1
    38 schema:url https://link.springer.com/10.1007%2Fs11265-019-1438-3
    39 sgo:license sg:explorer/license/
    40 sgo:sdDataset articles
    41 rdf:type schema:ScholarlyArticle
    42 N02a8ca9d23f84f6aa37255013990764c schema:affiliation https://www.grid.ac/institutes/grid.410726.6
    43 schema:familyName Xu
    44 schema:givenName Ke
    45 rdf:type schema:Person
    46 N0518de71eb4848e68cbcc6beb83c3ca5 rdf:first N49a13cbc4ca842d5b0088f928adbab12
    47 rdf:rest N92125760caa54af88e27d0ee786b7655
    48 N2485a2f745d0436d980c8ad5b71b1753 schema:affiliation https://www.grid.ac/institutes/grid.433146.7
    49 schema:familyName Wang
    50 schema:givenName Yangang
    51 rdf:type schema:Person
    52 N49a13cbc4ca842d5b0088f928adbab12 schema:affiliation Nd6a00824653543b4b92f4a0045e82069
    53 schema:familyName Chen
    54 schema:givenName Liandong
    55 rdf:type schema:Person
    56 N556ead40bf224fb3ab6fe8ceefaf20f1 schema:name Springer Nature - SN SciGraph project
    57 rdf:type schema:Organization
    58 N579ddb2bb26246828acd66c4c3ded383 schema:affiliation https://www.grid.ac/institutes/grid.410726.6
    59 schema:familyName Lu
    60 schema:givenName Zhonghua
    61 rdf:type schema:Person
    62 N60ae418836f04a0083323db3efc8dabd schema:name doi
    63 schema:value 10.1007/s11265-019-1438-3
    64 rdf:type schema:PropertyValue
    65 N616ca6001a4b473ba19580a6a0483345 rdf:first Nc94eef92564647319405d70e247a308c
    66 rdf:rest rdf:nil
    67 N6fd7ec3ea96c4d9b986bc66319bba9b1 rdf:first N02a8ca9d23f84f6aa37255013990764c
    68 rdf:rest Nbebdd530a3954f1b97029681922f185d
    69 N92125760caa54af88e27d0ee786b7655 rdf:first Nf9bc024e21014d93afc4146db99c3efc
    70 rdf:rest N9bf4f54fcfe64e0ebc436d28f442ba15
    71 N9bf4f54fcfe64e0ebc436d28f442ba15 rdf:first N2485a2f745d0436d980c8ad5b71b1753
    72 rdf:rest N616ca6001a4b473ba19580a6a0483345
    73 Na5a1d280763c45fe8f3ece0bdba70e9f rdf:first Nbd409c9da19641908eff54885ea73553
    74 rdf:rest N0518de71eb4848e68cbcc6beb83c3ca5
    75 Na5a82d526bc34501a270b65ea401824d schema:name readcube_id
    76 schema:value 58889e144abf2bd5ddf31f4c9e9ff7e254a245c0893cf95188f3fceee8270fd3
    77 rdf:type schema:PropertyValue
    78 Nb5d0d4033ead4cedac5040f825181d47 rdf:first Ndec4f5f32263436cb5e53dfddf21bf7d
    79 rdf:rest Neda04d698a574ae4ac2a7e6826ace621
    80 Nbd409c9da19641908eff54885ea73553 schema:affiliation https://www.grid.ac/institutes/grid.433146.7
    81 schema:familyName Liu
    82 schema:givenName Fang
    83 rdf:type schema:Person
    84 Nbebdd530a3954f1b97029681922f185d rdf:first Nc1488b57372c4b4e88c18c4ec2830391
    85 rdf:rest Na5a1d280763c45fe8f3ece0bdba70e9f
    86 Nc1488b57372c4b4e88c18c4ec2830391 schema:affiliation https://www.grid.ac/institutes/grid.35403.31
    87 schema:familyName Pang
    88 schema:givenName Yuchen
    89 rdf:type schema:Person
    90 Nc94eef92564647319405d70e247a308c schema:affiliation https://www.grid.ac/institutes/grid.433146.7
    91 schema:familyName Cao
    92 schema:givenName Rongqiang
    93 rdf:type schema:Person
    94 Nd6a00824653543b4b92f4a0045e82069 schema:name State Grid Hebei Electric Power Company, 050022, Shijiazhuang, Hebei Province, China
    95 rdf:type schema:Organization
    96 Ndec4f5f32263436cb5e53dfddf21bf7d schema:affiliation https://www.grid.ac/institutes/grid.496812.0
    97 schema:familyName Zhang
    98 schema:givenName Haikuo
    99 rdf:type schema:Person
    100 Neda04d698a574ae4ac2a7e6826ace621 rdf:first N579ddb2bb26246828acd66c4c3ded383
    101 rdf:rest N6fd7ec3ea96c4d9b986bc66319bba9b1
    102 Nf9bc024e21014d93afc4146db99c3efc schema:affiliation https://www.grid.ac/institutes/grid.433146.7
    103 schema:familyName Wang
    104 schema:givenName Jue
    105 rdf:type schema:Person
    106 Nf9e4ea90e2c04c22aba7d23e82e84550 schema:name dimensions_id
    107 schema:value pub.1111949509
    108 rdf:type schema:PropertyValue
    109 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    110 schema:name Information and Computing Sciences
    111 rdf:type schema:DefinedTerm
    112 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    113 schema:name Artificial Intelligence and Image Processing
    114 rdf:type schema:DefinedTerm
    115 sg:journal.1297359 schema:issn 0922-5773
    116 1939-8115
    117 schema:name Journal of Signal Processing Systems
    118 rdf:type schema:Periodical
    119 sg:pub.10.1007/s00450-011-0171-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039789635
    120 https://doi.org/10.1007/s00450-011-0171-3
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1016/0167-8191(96)00024-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045623305
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1016/j.camwa.2009.08.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022599292
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1016/j.comcom.2018.03.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101547762
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1016/j.future.2015.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036006716
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1016/j.future.2016.01.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036804631
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1109/embc.2013.6611044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078797794
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1109/icassp.2013.6639348 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094742597
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1109/jproc.2008.917757 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061296953
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1109/jsyst.2015.2460747 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061339651
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1109/mcse.2011.83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061398485
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1109/mm.2010.41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061408719
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1109/tc.2015.2470247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061536081
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1111/j.1478-4408.2008.00134.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1047806147
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1118/1.3578605 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049648597
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1145/1961295.1950408 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063159392
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1177/004051759906900511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039926050
    153 rdf:type schema:CreativeWork
    154 https://www.grid.ac/institutes/grid.35403.31 schema:alternateName University of Illinois at Urbana Champaign
    155 schema:name University of Illinois at Urbana-Champaign, 61820, Champaign, IL, USA
    156 rdf:type schema:Organization
    157 https://www.grid.ac/institutes/grid.410726.6 schema:alternateName University of Chinese Academy of Sciences
    158 schema:name Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China
    159 University of Chinese Academy of Sciences, 100049, Beijing, China
    160 rdf:type schema:Organization
    161 https://www.grid.ac/institutes/grid.433146.7 schema:alternateName Computer Network Information Center
    162 schema:name Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China
    163 rdf:type schema:Organization
    164 https://www.grid.ac/institutes/grid.496812.0 schema:alternateName China Internet Network Information Center
    165 schema:name China Internet Network Information Center, 100190, Beijing, China
    166 Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China
    167 University of Chinese Academy of Sciences, 100049, Beijing, China
    168 rdf:type schema:Organization
     




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


    ...