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A Novel Approach to User Involved Big Data Provenance Visualization

Authors:
Ilkay Melek Yazici
Mehmet S. Aktas
Mehmet Gokturk

Keywords: big data; provenance; visualization; open provenance model (OPM); human machine interface(HMI)

Abstract:
Abstract—We are living in the “big data” age., In many ways, big data is equivalent to complexity or mess. Extracting relevant information from any complex environment is a challenging but necessary task required in every scientific field. An assortment of graphs, figures,s and charts have been developed to, visualize n-dimensional data since the early ages of science. In the recent past, the number of dimensions in a visualization were limited by computational factors. Visualizing n-dimension is difficult but achievable by using data projection and reduction methods. Unfortunately, these methods often introduce ambiguities and inaccuracies, which can subtly corrupt results. Data provenance chronicles the core life cycle of a data set, which includes data source and creation processes, accounts for many of the processing techniques that a data set is subject to, like debugging, auditing and quality control. Additionally, data protection mechanisms such as data access control and authenticity valuation methods are also tracked by provenance. In this paper, we introduce an effective method to visualize and analyze semantic provenance data by adhering to the Human Computer Interaction principles. Our proposed data provenance visualization system involves the user in the visualization process. By capturing and analyzing a user’s attentiveness and perception level, we develop a provenance visualization system with specific visualization types and methodologies.

Pages: 10 to 15

Copyright: Copyright (c) IARIA, 2017

Publication date: May 21, 2017

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-558-6

Location: Barcelona, Spain

Dates: from May 21, 2017 to May 25, 2017