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Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications

Burkhardt, Dirk and Pattan, Sachin and Nazemi, Kawa and Kuijper, Arjan (2017):
Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications.
In: Procedia Computer Science, 104, pp. 539-547. ISSN 18770509,
DOI: 10.1016/j.procs.2017.01.170,
[Article]

Abstract

A new approach for classifying users' search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data.

Item Type: Article
Erschienen: 2017
Creators: Burkhardt, Dirk and Pattan, Sachin and Nazemi, Kawa and Kuijper, Arjan
Title: Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications
Language: English
Abstract:

A new approach for classifying users' search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data.

Journal or Publication Title: Procedia Computer Science
Journal volume: 104
Uncontrolled Keywords: Human-computer interaction (HCI), Information retrieval, User-centered design, Predictions
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 04 May 2020 12:51
DOI: 10.1016/j.procs.2017.01.170
Official URL: https://doi.org/10.1016/j.procs.2017.01.170
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