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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 571-578
Nikolaos Panagiotou , National and Kapodistrian University of Athens
Ioannis Katakis , National and Kapodistrian University of Athens
Dimitrios Gunopulos , National and Kapodistrian University of Athens
Vana Kalogeraki , Athens University of Economics and Business
Elizabeth Daly , IBM Research, Ireland
Jia Yuan Yu , Concordia University
Brendan O Brien , Dublin City Council, Ireland
ABSTRACT
In this paper we tackle the recently proposed problem of hidden streams. In many situations, the data stream that we are interested in, is not directly accessible. Instead, part of the data can be accessed only through applying filters (e.g. keyword filtering). In fact this is the case of the most discussed social stream today, Twitter. The problem in this case is how to retrieve as many relevant documents as possible by applying the most appropriate set of filters to the original stream and, at the same time, respect a number of constrains (e.g. maximum number of filters that can be applied). In this work we introduce a search approach on a dynamic filter space. We utilize heterogeneous filters (not only keywords) making no assumptions about the attributes of the individual filters. We advance current research by considering realistically hard constraints based on real-world scenarios that require tracking of multiple dynamic topics. We demonstrate the effectiveness of our approaches on a set of topics of static and dynamic nature. The development of the approach was motivated by a real application. Our system is deployed in Dublin City's Traffic Management Center and allows the city officers to analyze large sources of heterogeneous data and identify events related to traffic as well as emergencies.
INDEX TERMS
Twitter, Urban areas, Real-time systems, Roads, Data mining
CITATION

N. Panagiotou et al., "Mining hidden constrained streams in practice: Informed search in dynamic filter spaces," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 571-578.
doi:10.1109/ASONAM.2016.7752293
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