Motivation

Systems, application functionality, concepts, frameworks and technical systems are changing and increasing in short intervals. Due to this fact obtaining relevant information for a specific issue is a frequently executed and time-consuming process. One corresponding problem is that the availability of nearly unlimited amount of data results in overflow. A major problem is the efficiency of information search and retrieval processes. Studies predicted that employees spent a large amount of time in searching for information. Another problem is the fact that a great number of people have suboptimal strategies while using web search engines in order to find relevant information.

Main Objectives

  • Decrease duration of information search activities 
  • Providing information push
  • High participation rate of the crowd
  • Automation of search processes

Publikationen

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  • Beul, Michael; Eicker, Stefan: Crowdsourcing Supported Context Detection for Improving Information Search Activities. In: International Academy, Research; Association, Industry (Hrsg.): Proceedings of The Seventh International Multi-Conference on Computing in the Global Information Technology. (to be published), 2012, S. 254-259. BIB DownloadDetails
    Crowdsourcing Supported Context Detection for Improving Information Search Activities

    In environments that require the use of software applications, intervals where application functionality, tools, methods and technical systems are changing are often very short. The process of searching for relevant information about a specific issue is frequently executed and time-consuming. Because of the availability of a nearly unlimited amount of data, people spend a lot of time in formulating search queries and evaluating the relevance of the search results. In this paper, we describe a generic approach that improves the information search and retrieval process of different activities with the use of context information. One main goal is the integration of the crowd€ at different stages of this process by combining collective intelligence concepts with context-aware systems. This combination can be used to automatically reduce information overflow by filtering irrelevant data. Furthermore, a real-time information retrieval process without manual search im pulses is provided. We also present a prototype as a proof of concept in order to validate feasibility and benefit.

  • Beul, Michael; Eicker, Stefan: "Don't Call Us, We Call You" - A Community Driven Approach for (Domain Independent) Context Driven Information Retrieval (CoDIR). In: Yetongnon, Kokou; Chbeir, Richard; Dipanda, Albert (Hrsg.): The Fifth International Conference on Signal Image Technology & Internet Based Systems - SITIS 2009. IEEE Computer Society, Marrakesh, Morocco 2010, S. 458-464. doi:10.1109/SITIS.2009.77BIB DownloadDetails
    "Don't Call Us, We Call You" - A Community Driven Approach for (Domain Independent) Context Driven Information Retrieval (CoDIR)

    This paper discusses the combination of context injection, metadata enrichment and information broking with current semantic web and community oriented concepts in order to optimize the search and retrieval process of relevant information. The presented approach focuses the shift from information pull to information push, and thus reduces the effort on finding the needed information for a specific issue in a concrete context.

Vorträge

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  • Beul, Michael: Crowdsourcing Supported Context Detection for Improving Information Search Activities; The Seventh International Multi-Conference on Computing in the Global Information Technology, 24.06.2012, Venice, Italy.
  • Beul, Michael: "Don’t call us, we call you" - A community driven approach for (domain independent) context driven information retrieval (CoDIR); The Fifth International Conference on Signal Image Technology & Internet Based Systems - SITIS 2009, 29.11.2009, Marrakesch, Marokko. Details

Abschlussarbeiten

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  • Beul, Michael: Echtzeit-Informationsbeschaffung in softwareintensiven Systemen - Entwicklung eines Frameworks unter Verwendung von Kontextinformationen und kollektiver Intelligenz
    Dissertation Wirtschaftsinformatik, 2017, Ansprechpartner: Prof. Dr. Stefan EickerDetails
  • Benutzeraktivität in Anwendungsprogrammen - Konzeption und prototypische Implementierung eines Systems zur Erfassung des Benutzerverhaltens
    Bachelorarbeit Wirtschaftsinformatik, 2011, Ansprechpartner: Dr. Michael BeulDetails
  • Adaption der Nutzung kontextspezifischer Informationen zur Unterstützung des Informationsmanagements
    Masterarbeit Wirtschaftsinformatik, 2010, Ansprechpartner: Dr. Michael BeulDetails
  • Metadaten-Management zur Anreicherung webbasierter Daten um kontextspezifische Informationen
    Diplomarbeit Wirtschaftsinformatik, 2010, Ansprechpartner: Dr. Michael BeulDetails