Advanced Behavioral Analyses Using Inferred Social Networks: A Vision
Authors
Holubová, I.; Svoboda, M.; Skopal, T.; Bernhauer, D.; Peška, L.
Year
2019
Published
Database and Expert Systems Applications. Springer, Cham, 2019. p. 210-219. ISSN 1865-0929. ISBN 978-3-030-27683-6.
Type
Proceedings paper
Departments
Annotation
The success of many businesses is based on a thorough knowledge of their clients. There exists a number of supervised as well as unsupervised data mining or other approaches that allow to analyze data about clients, their behavior or environment. In our ongoing project focusing primarily on bank clients, we propose an innovative strategy that will overcome shortcomings of the existing methods. From a given set of user activities, we infer their social network in order to analyze user relationships and behavior. For this purpose, not just the traditional direct facts are incorporated, but also relationships inferred using similarity measures and statistical approaches, with both possibly limited measures of reliability and validity in time. Such networks would enable analyses of client characteristics from a new perspective and could provide otherwise impossible insights. However, there are several research and technical challenges making the outlined pursuit novel, complex and challenging as we outline in this vision paper.
Inferred Social Networks: A Case Study
Authors
Holubová, I.; Svoboda, M.; Bernhauer, D.; Skopal, T.; Paščenko, P.
Year
2019
Published
19th IEEE International Conference on Data Mining Workshops. Los Alamitos: IEEE Computer Society, 2019. p. 65-68. ISBN 978-1-7281-4603-4.
Type
Proceedings paper
Departments
Annotation
The behavior, environment, and characteristics of clients form a crucial source of information for various businesses. There exists a number of supervised as well as unsupervised data mining or other approaches that allow analyzing the respective data. In our ongoing project, focusing primarily on the financial sector, we suggest an innovative strategy that will overcome persisting shortcomings of the state-of-the-art methods using an analysis of a social network of clients. In addition, we do not assume the existence of such a network, but from a given set of client financial activities, we are able to infer a social network representing their relationships and behavior. Using real-world data and selected use cases from our domain, we show (a part of) the process of construction of an inferred social network, i.e., what kind of "hidden" information can, for example, be found and exploited.
SIMILANT: An Analytic Tool for Similarity Modeling
Authors
Bernhauer, D.; Skopal, T.; Holubová, I.; Peška, L.; Svoboda, M.
Year
2019
Published
Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: Association for Computing Machinery, 2019. p. 2889-2892. ISBN 978-1-4503-6976-3.
Type
Proceedings paper
Departments
Annotation
We present SIMILANT, a data analytics tool for modeling similarity in content-based retrieval scenarios. In similarity search, data elements are modeled using black-box descriptors, where a pair-wise similarity function is the only way how to relate data elements to each other. Only these relations provide information about the dataset structure. Data analysts need to identify meaningful combinations of descriptors and similarity functions effectively. Therefore, we proposed a tool enabling a data analyst to systematically browse, tune, and analyze similarity models for a specific domain.