A Topology-Based Approach to Visualize the Thematic Composition of Document Collections View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Patrick Oesterling , Christian Heine , Gunther H. Weber , Gerik Scheuermann

ABSTRACT

The thematic composition of document collections is commonly conceptualized by clusters of high-dimensional point clouds. However, illustrating these clusters is challenging: typical visualizations such as colored projections or parallel coordinate plots suffer from feature occlusion and noise covering the whole visualization. We propose a method that avoids structural occlusion by using topology-based visualizations to preserve primary clustering features and neglect geometric properties that cannot be preserved in low-dimensional representations. Abstracting the input points as nested dense regions with individual properties, we provide the user with intuitive landscape visualizations that illustrate the high-dimensional clustering structure occlusion-free. More... »

PAGES

63-85

References to SciGraph publications

  • 2010. Towards Automatic Detection and Tracking of Topic Change in COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING
  • 2002-11. Topological Persistence and Simplification in DISCRETE & COMPUTATIONAL GEOMETRY
  • 2010. Visualization of Text Streams: A Survey in KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS
  • 1998-11. Knowledge Mining With VxInsight: Discovery Through Interaction in JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
  • 2001. Self-Organizing Maps in NONE
  • 2004. The Challenges of Clustering High Dimensional Data in NEW DIRECTIONS IN STATISTICAL PHYSICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-12655-5_4

    DOI

    http://dx.doi.org/10.1007/978-3-319-12655-5_4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1030290889


    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": "Leipzig University", 
              "id": "https://www.grid.ac/institutes/grid.9647.c", 
              "name": [
                "Image and Signal Processing Group, Institute of Computer Science, Leipzig University, Leipzig, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Oesterling", 
            "givenName": "Patrick", 
            "id": "sg:person.012502263423.09", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012502263423.09"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Swiss Federal Institute of Technology in Zurich", 
              "id": "https://www.grid.ac/institutes/grid.5801.c", 
              "name": [
                "Scientific Visualization Group, Department of Computer Science, ETH Z\u00fcrich, Z\u00fcrich, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Heine", 
            "givenName": "Christian", 
            "id": "sg:person.01322065347.76", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322065347.76"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Lawrence Berkeley National Laboratory", 
              "id": "https://www.grid.ac/institutes/grid.184769.5", 
              "name": [
                "Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Weber", 
            "givenName": "Gunther H.", 
            "id": "sg:person.0764637220.09", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764637220.09"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Leipzig University", 
              "id": "https://www.grid.ac/institutes/grid.9647.c", 
              "name": [
                "Image and Signal Processing Group, Institute of Computer Science, Leipzig University, Leipzig, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Scheuermann", 
            "givenName": "Gerik", 
            "id": "sg:person.0777577160.41", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777577160.41"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00454-002-2885-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001786843", 
              "https://doi.org/10.1007/s00454-002-2885-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00454-002-2885-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001786843", 
              "https://doi.org/10.1007/s00454-002-2885-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15390-7_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005520175", 
              "https://doi.org/10.1007/978-3-642-15390-7_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15390-7_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005520175", 
              "https://doi.org/10.1007/978-3-642-15390-7_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0925-2312(98)00039-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008416911"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012153938"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-08968-2_16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014585116", 
              "https://doi.org/10.1007/978-3-662-08968-2_16"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/505282.505283", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023316280"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-56927-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026330191", 
              "https://doi.org/10.1007/978-3-642-56927-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-56927-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026330191", 
              "https://doi.org/10.1007/978-3-642-56927-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-12116-6_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027648643", 
              "https://doi.org/10.1007/978-3-642-12116-6_27"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-12116-6_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027648643", 
              "https://doi.org/10.1007/978-3-642-12116-6_27"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0306-4573(88)90021-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032478827"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0306-4573(88)90021-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032478827"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0925-7721(02)00093-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033448965"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0925-7721(02)00093-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033448965"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1008690008856", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038914205", 
              "https://doi.org/10.1023/a:1008690008856"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/5.163414", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061178948"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/t-c.1969.222678", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061455087"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvcg.2007.70443", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061812856"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvcg.2007.70601", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061812930"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvcg.2008.153", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061813005"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvcg.2008.85", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061813103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvcg.2011.27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061813657"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvcg.2012.120", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061813736"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2412323", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069920775"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/visual.1990.146402", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086261669"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/vast.2010.5652940", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093403540"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/infvis.1995.528686", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094041644"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/sibgrapi.2007.21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094106586"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/vast.2009.5332629", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094623830"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/visual.1998.745302", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094632142"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iv.2006.104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094787904"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1100484362", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4135/9781412985130", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100484362"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4135/9781412985130", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100484362"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2014", 
        "datePublishedReg": "2014-01-01", 
        "description": "The thematic composition of document collections is commonly conceptualized by clusters of high-dimensional point clouds. However, illustrating these clusters is challenging: typical visualizations such as colored projections or parallel coordinate plots suffer from feature occlusion and noise covering the whole visualization. We propose a method that avoids structural occlusion by using topology-based visualizations to preserve primary clustering features and neglect geometric properties that cannot be preserved in low-dimensional representations. Abstracting the input points as nested dense regions with individual properties, we provide the user with intuitive landscape visualizations that illustrate the high-dimensional clustering structure occlusion-free.", 
        "editor": [
          {
            "familyName": "Biemann", 
            "givenName": "Chris", 
            "type": "Person"
          }, 
          {
            "familyName": "Mehler", 
            "givenName": "Alexander", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-12655-5_4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-319-12654-8", 
            "978-3-319-12655-5"
          ], 
          "name": "Text Mining", 
          "type": "Book"
        }, 
        "name": "A Topology-Based Approach to Visualize the Thematic Composition of Document Collections", 
        "pagination": "63-85", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-12655-5_4"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "904caa9843de4259323719cabb3039d6ea417278f6df2b7fce31404c05e83e3a"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1030290889"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-12655-5_4", 
          "https://app.dimensions.ai/details/publication/pub.1030290889"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T16:50", 
        "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_8675_00000558.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-3-319-12655-5_4"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-12655-5_4'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-12655-5_4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-12655-5_4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-12655-5_4'


     

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

    189 TRIPLES      23 PREDICATES      56 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-12655-5_4 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nd6f84c77480b4999b329301bb7be2cd8
    4 schema:citation sg:pub.10.1007/978-3-642-12116-6_27
    5 sg:pub.10.1007/978-3-642-15390-7_4
    6 sg:pub.10.1007/978-3-642-56927-2
    7 sg:pub.10.1007/978-3-662-08968-2_16
    8 sg:pub.10.1007/s00454-002-2885-2
    9 sg:pub.10.1023/a:1008690008856
    10 https://app.dimensions.ai/details/publication/pub.1100484362
    11 https://doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9
    12 https://doi.org/10.1016/0306-4573(88)90021-0
    13 https://doi.org/10.1016/s0925-2312(98)00039-3
    14 https://doi.org/10.1016/s0925-7721(02)00093-7
    15 https://doi.org/10.1109/5.163414
    16 https://doi.org/10.1109/infvis.1995.528686
    17 https://doi.org/10.1109/iv.2006.104
    18 https://doi.org/10.1109/sibgrapi.2007.21
    19 https://doi.org/10.1109/t-c.1969.222678
    20 https://doi.org/10.1109/tvcg.2007.70443
    21 https://doi.org/10.1109/tvcg.2007.70601
    22 https://doi.org/10.1109/tvcg.2008.153
    23 https://doi.org/10.1109/tvcg.2008.85
    24 https://doi.org/10.1109/tvcg.2011.27
    25 https://doi.org/10.1109/tvcg.2012.120
    26 https://doi.org/10.1109/vast.2009.5332629
    27 https://doi.org/10.1109/vast.2010.5652940
    28 https://doi.org/10.1109/visual.1990.146402
    29 https://doi.org/10.1109/visual.1998.745302
    30 https://doi.org/10.1145/505282.505283
    31 https://doi.org/10.2307/2412323
    32 https://doi.org/10.4135/9781412985130
    33 schema:datePublished 2014
    34 schema:datePublishedReg 2014-01-01
    35 schema:description The thematic composition of document collections is commonly conceptualized by clusters of high-dimensional point clouds. However, illustrating these clusters is challenging: typical visualizations such as colored projections or parallel coordinate plots suffer from feature occlusion and noise covering the whole visualization. We propose a method that avoids structural occlusion by using topology-based visualizations to preserve primary clustering features and neglect geometric properties that cannot be preserved in low-dimensional representations. Abstracting the input points as nested dense regions with individual properties, we provide the user with intuitive landscape visualizations that illustrate the high-dimensional clustering structure occlusion-free.
    36 schema:editor N443d18fcc0194c4ebc08a4da1b77a168
    37 schema:genre chapter
    38 schema:inLanguage en
    39 schema:isAccessibleForFree false
    40 schema:isPartOf Na4e4fbde04674a04a9972ffd14520be2
    41 schema:name A Topology-Based Approach to Visualize the Thematic Composition of Document Collections
    42 schema:pagination 63-85
    43 schema:productId Na23b5caf78e84f889f4d1c1406d381db
    44 Nb139b9a49cc147ea9b65e451f2e8d846
    45 Nb6c25200c91743028a9d728f35206c27
    46 schema:publisher N1692b73dfe1e4cc5bcb29edd3fc25922
    47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030290889
    48 https://doi.org/10.1007/978-3-319-12655-5_4
    49 schema:sdDatePublished 2019-04-15T16:50
    50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    51 schema:sdPublisher N16a160e1f0ef4eee8048b3f82232c059
    52 schema:url http://link.springer.com/10.1007/978-3-319-12655-5_4
    53 sgo:license sg:explorer/license/
    54 sgo:sdDataset chapters
    55 rdf:type schema:Chapter
    56 N1692b73dfe1e4cc5bcb29edd3fc25922 schema:location Cham
    57 schema:name Springer International Publishing
    58 rdf:type schema:Organisation
    59 N16a160e1f0ef4eee8048b3f82232c059 schema:name Springer Nature - SN SciGraph project
    60 rdf:type schema:Organization
    61 N3736eed207af40f6a26290f08dc6fd18 rdf:first sg:person.01322065347.76
    62 rdf:rest N748f443215d84b58a45bc74f723dfd25
    63 N4382deebbafb470b822286485532384c rdf:first sg:person.0777577160.41
    64 rdf:rest rdf:nil
    65 N443d18fcc0194c4ebc08a4da1b77a168 rdf:first N8ec35caa2cff47beba8757556ea79a93
    66 rdf:rest Nca55582876444370979f352a9a741a6f
    67 N630ed36fff8340cc8705a113f08b5d79 schema:familyName Mehler
    68 schema:givenName Alexander
    69 rdf:type schema:Person
    70 N748f443215d84b58a45bc74f723dfd25 rdf:first sg:person.0764637220.09
    71 rdf:rest N4382deebbafb470b822286485532384c
    72 N8ec35caa2cff47beba8757556ea79a93 schema:familyName Biemann
    73 schema:givenName Chris
    74 rdf:type schema:Person
    75 Na23b5caf78e84f889f4d1c1406d381db schema:name dimensions_id
    76 schema:value pub.1030290889
    77 rdf:type schema:PropertyValue
    78 Na4e4fbde04674a04a9972ffd14520be2 schema:isbn 978-3-319-12654-8
    79 978-3-319-12655-5
    80 schema:name Text Mining
    81 rdf:type schema:Book
    82 Nb139b9a49cc147ea9b65e451f2e8d846 schema:name doi
    83 schema:value 10.1007/978-3-319-12655-5_4
    84 rdf:type schema:PropertyValue
    85 Nb6c25200c91743028a9d728f35206c27 schema:name readcube_id
    86 schema:value 904caa9843de4259323719cabb3039d6ea417278f6df2b7fce31404c05e83e3a
    87 rdf:type schema:PropertyValue
    88 Nca55582876444370979f352a9a741a6f rdf:first N630ed36fff8340cc8705a113f08b5d79
    89 rdf:rest rdf:nil
    90 Nd6f84c77480b4999b329301bb7be2cd8 rdf:first sg:person.012502263423.09
    91 rdf:rest N3736eed207af40f6a26290f08dc6fd18
    92 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    93 schema:name Information and Computing Sciences
    94 rdf:type schema:DefinedTerm
    95 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    96 schema:name Artificial Intelligence and Image Processing
    97 rdf:type schema:DefinedTerm
    98 sg:person.012502263423.09 schema:affiliation https://www.grid.ac/institutes/grid.9647.c
    99 schema:familyName Oesterling
    100 schema:givenName Patrick
    101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012502263423.09
    102 rdf:type schema:Person
    103 sg:person.01322065347.76 schema:affiliation https://www.grid.ac/institutes/grid.5801.c
    104 schema:familyName Heine
    105 schema:givenName Christian
    106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322065347.76
    107 rdf:type schema:Person
    108 sg:person.0764637220.09 schema:affiliation https://www.grid.ac/institutes/grid.184769.5
    109 schema:familyName Weber
    110 schema:givenName Gunther H.
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764637220.09
    112 rdf:type schema:Person
    113 sg:person.0777577160.41 schema:affiliation https://www.grid.ac/institutes/grid.9647.c
    114 schema:familyName Scheuermann
    115 schema:givenName Gerik
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777577160.41
    117 rdf:type schema:Person
    118 sg:pub.10.1007/978-3-642-12116-6_27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027648643
    119 https://doi.org/10.1007/978-3-642-12116-6_27
    120 rdf:type schema:CreativeWork
    121 sg:pub.10.1007/978-3-642-15390-7_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005520175
    122 https://doi.org/10.1007/978-3-642-15390-7_4
    123 rdf:type schema:CreativeWork
    124 sg:pub.10.1007/978-3-642-56927-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026330191
    125 https://doi.org/10.1007/978-3-642-56927-2
    126 rdf:type schema:CreativeWork
    127 sg:pub.10.1007/978-3-662-08968-2_16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014585116
    128 https://doi.org/10.1007/978-3-662-08968-2_16
    129 rdf:type schema:CreativeWork
    130 sg:pub.10.1007/s00454-002-2885-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001786843
    131 https://doi.org/10.1007/s00454-002-2885-2
    132 rdf:type schema:CreativeWork
    133 sg:pub.10.1023/a:1008690008856 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038914205
    134 https://doi.org/10.1023/a:1008690008856
    135 rdf:type schema:CreativeWork
    136 https://app.dimensions.ai/details/publication/pub.1100484362 schema:CreativeWork
    137 https://doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012153938
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1016/0306-4573(88)90021-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032478827
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1016/s0925-2312(98)00039-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008416911
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1016/s0925-7721(02)00093-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033448965
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1109/5.163414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061178948
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1109/infvis.1995.528686 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094041644
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1109/iv.2006.104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094787904
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1109/sibgrapi.2007.21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094106586
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1109/t-c.1969.222678 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061455087
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1109/tvcg.2007.70443 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061812856
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1109/tvcg.2007.70601 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061812930
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1109/tvcg.2008.153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061813005
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1109/tvcg.2008.85 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061813103
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1109/tvcg.2011.27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061813657
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1109/tvcg.2012.120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061813736
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1109/vast.2009.5332629 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094623830
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1109/vast.2010.5652940 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093403540
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1109/visual.1990.146402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086261669
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1109/visual.1998.745302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094632142
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1145/505282.505283 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023316280
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.2307/2412323 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069920775
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.4135/9781412985130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100484362
    180 rdf:type schema:CreativeWork
    181 https://www.grid.ac/institutes/grid.184769.5 schema:alternateName Lawrence Berkeley National Laboratory
    182 schema:name Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
    183 rdf:type schema:Organization
    184 https://www.grid.ac/institutes/grid.5801.c schema:alternateName Swiss Federal Institute of Technology in Zurich
    185 schema:name Scientific Visualization Group, Department of Computer Science, ETH Zürich, Zürich, Switzerland
    186 rdf:type schema:Organization
    187 https://www.grid.ac/institutes/grid.9647.c schema:alternateName Leipzig University
    188 schema:name Image and Signal Processing Group, Institute of Computer Science, Leipzig University, Leipzig, Germany
    189 rdf:type schema:Organization
     




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


    ...