Publikation:

Visual Analytics and Similarity Search : Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2017

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

BEECKS, Christian, ed. and others. Similarity Search and Applications. Cham: Springer, 2017, pp. 324-332. Lecture Notes in Computer Science. 10609. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-68473-4. Available under: doi: 10.1007/978-3-319-68474-1_23

Zusammenfassung

A major challenge of the contemporary information age is the overwhelming and increasing data amount, especially when looking for specific information. Searching for relevant information is no longer manually possible, but has to rely on automatic methods, specifically, similarity search. From a formal perspective, similarity search can be seen as the problem of finding entities, which are considered to be similar to a query with respect to certain describing features. The question which features or which weighted combination of features to use for a given query creates a need for semi-automatic methods to address the needs of diverse users. Furthermore, the quality of the results of a similarity search is more than effectiveness, measured by precision and recall. The user ideally needs to trust the results and understand how they were computed. We propose to apply Visual Analytics methodologies, for synergistic cooperation of user and algorithms, to integrate three key dimensions of similarity search: users, tasks, and data for effective search. However, there exists a gap in knowledge how user, task as well as the available data influence each other and the similarity search. In this concept paper, we envision how Visual Analytics can be used to tackle current challenges of similarity search.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Similarity search, Recommender systems, Visual analytics

Konferenz

10th International Conference, SISAP 2017, 4. Okt. 2017 - 6. Okt. 2017, Munich, Germany
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690SEEBACHER, Daniel, Johannes HÄUSSLER, Manuel STEIN, Halldor JANETZKO, Tobias SCHRECK, 2017. Visual Analytics and Similarity Search : Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data. 10th International Conference, SISAP 2017. Munich, Germany, 4. Okt. 2017 - 6. Okt. 2017. In: BEECKS, Christian, ed. and others. Similarity Search and Applications. Cham: Springer, 2017, pp. 324-332. Lecture Notes in Computer Science. 10609. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-68473-4. Available under: doi: 10.1007/978-3-319-68474-1_23
BibTex
@inproceedings{Seebacher2017Visua-41299,
  year={2017},
  doi={10.1007/978-3-319-68474-1_23},
  title={Visual Analytics and Similarity Search : Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data},
  number={10609},
  isbn={978-3-319-68473-4},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Similarity Search and Applications},
  pages={324--332},
  editor={Beecks, Christian},
  author={Seebacher, Daniel and Häußler, Johannes and Stein, Manuel and Janetzko, Halldor and Schreck, Tobias}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/41299">
    <dc:creator>Janetzko, Halldor</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-02-13T11:59:12Z</dc:date>
    <dc:language>eng</dc:language>
    <dc:creator>Seebacher, Daniel</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2017</dcterms:issued>
    <dc:creator>Schreck, Tobias</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-02-13T11:59:12Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/41299"/>
    <dc:creator>Häußler, Johannes</dc:creator>
    <dc:contributor>Häußler, Johannes</dc:contributor>
    <dc:creator>Stein, Manuel</dc:creator>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dcterms:abstract xml:lang="eng">A major challenge of the contemporary information age is the overwhelming and increasing data amount, especially when looking for specific information. Searching for relevant information is no longer manually possible, but has to rely on automatic methods, specifically, similarity search. From a formal perspective, similarity search can be seen as the problem of finding entities, which are considered to be similar to a query with respect to certain describing features. The question which features or which weighted combination of features to use for a given query creates a need for semi-automatic methods to address the needs of diverse users. Furthermore, the quality of the results of a similarity search is more than effectiveness, measured by precision and recall. The user ideally needs to trust the results and understand how they were computed. We propose to apply Visual Analytics methodologies, for synergistic cooperation of user and algorithms, to integrate three key dimensions of similarity search: users, tasks, and data for effective search. However, there exists a gap in knowledge how user, task as well as the available data influence each other and the similarity search. In this concept paper, we envision how Visual Analytics can be used to tackle current challenges of similarity search.</dcterms:abstract>
    <dc:contributor>Stein, Manuel</dc:contributor>
    <dcterms:title>Visual Analytics and Similarity Search : Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data</dcterms:title>
    <dc:contributor>Seebacher, Daniel</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Janetzko, Halldor</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen