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Extending search to crowds : A model-driven approach. / Bozzon, Alessandro; Brambilla, Marco; Ceri, Stefano; Mauri, Andrea.

Search Computing: Broadening Web Search. Vol. 7538 2012. p. 207-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7538).

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

Harvard

Bozzon, A, Brambilla, M, Ceri, S & Mauri, A 2012, Extending search to crowds: A model-driven approach. in Search Computing: Broadening Web Search. vol. 7538, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7538, pp. 207-222. https://doi.org/10.1007/978-3-642-34213-4_14

APA

Bozzon, A., Brambilla, M., Ceri, S., & Mauri, A. (2012). Extending search to crowds: A model-driven approach. In Search Computing: Broadening Web Search (Vol. 7538, pp. 207-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7538). https://doi.org/10.1007/978-3-642-34213-4_14

Vancouver

Bozzon A, Brambilla M, Ceri S, Mauri A. Extending search to crowds: A model-driven approach. In Search Computing: Broadening Web Search. Vol. 7538. 2012. p. 207-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-34213-4_14

Author

Bozzon, Alessandro ; Brambilla, Marco ; Ceri, Stefano ; Mauri, Andrea. / Extending search to crowds : A model-driven approach. Search Computing: Broadening Web Search. Vol. 7538 2012. pp. 207-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inbook{5e420bd96b5544998ea64bd68572330b,
title = "Extending search to crowds: A model-driven approach",
abstract = "In many settings, the human opinion provided by an expert or knowledgeable user can be more useful than factual information retrieved by a search engine. Search systems do not capture the subjective opinions and recommendations of friends, or fresh, online-provided information that require contextual or domain-specific expertise. Search results obtained from conventional search engines can be complemented by crowdsearch, an online interaction with crowds, selected among friends, experts, or people who are presently at a given location; an interplay between conventional and search-based queries can occur, so that the two search methods can support each other. In this paper, we use a model-driven approach for specifying and implementing a crowdsearch application; in particular we define two models: the {"}Query Task Model{"}, representing the meta-model of the query that is submitted to the crowd and the associated answers; and the {"}User Interaction Model{"}, showing how the user can interact with the query model to fulfil her needs. Our solution allows for a top-down design approach, from the crowd-search task design, down to the crowd answering system design. Our approach also grants automatic code generation, thus leading to quick prototyping of crowd-search applications.",
author = "Alessandro Bozzon and Marco Brambilla and Stefano Ceri and Andrea Mauri",
year = "2012",
doi = "10.1007/978-3-642-34213-4_14",
language = "English",
isbn = "9783642342127",
volume = "7538",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "207--222",
booktitle = "Search Computing: Broadening Web Search",

}

RIS

TY - CHAP

T1 - Extending search to crowds

T2 - A model-driven approach

AU - Bozzon, Alessandro

AU - Brambilla, Marco

AU - Ceri, Stefano

AU - Mauri, Andrea

PY - 2012

Y1 - 2012

N2 - In many settings, the human opinion provided by an expert or knowledgeable user can be more useful than factual information retrieved by a search engine. Search systems do not capture the subjective opinions and recommendations of friends, or fresh, online-provided information that require contextual or domain-specific expertise. Search results obtained from conventional search engines can be complemented by crowdsearch, an online interaction with crowds, selected among friends, experts, or people who are presently at a given location; an interplay between conventional and search-based queries can occur, so that the two search methods can support each other. In this paper, we use a model-driven approach for specifying and implementing a crowdsearch application; in particular we define two models: the "Query Task Model", representing the meta-model of the query that is submitted to the crowd and the associated answers; and the "User Interaction Model", showing how the user can interact with the query model to fulfil her needs. Our solution allows for a top-down design approach, from the crowd-search task design, down to the crowd answering system design. Our approach also grants automatic code generation, thus leading to quick prototyping of crowd-search applications.

AB - In many settings, the human opinion provided by an expert or knowledgeable user can be more useful than factual information retrieved by a search engine. Search systems do not capture the subjective opinions and recommendations of friends, or fresh, online-provided information that require contextual or domain-specific expertise. Search results obtained from conventional search engines can be complemented by crowdsearch, an online interaction with crowds, selected among friends, experts, or people who are presently at a given location; an interplay between conventional and search-based queries can occur, so that the two search methods can support each other. In this paper, we use a model-driven approach for specifying and implementing a crowdsearch application; in particular we define two models: the "Query Task Model", representing the meta-model of the query that is submitted to the crowd and the associated answers; and the "User Interaction Model", showing how the user can interact with the query model to fulfil her needs. Our solution allows for a top-down design approach, from the crowd-search task design, down to the crowd answering system design. Our approach also grants automatic code generation, thus leading to quick prototyping of crowd-search applications.

UR - http://www.scopus.com/inward/record.url?scp=84893116735&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-34213-4_14

DO - 10.1007/978-3-642-34213-4_14

M3 - Chapter

SN - 9783642342127

VL - 7538

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 207

EP - 222

BT - Search Computing: Broadening Web Search

ER -

ID: 33899183