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(ICCC 2020) Cognitive and Predictive Analytics on Big Open Data

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Abstract: Nowadays, big data are everywhere because huge amounts of valuable data can be easily generated and collected from a wide variety of data sources at a rapid rate. Embedded into these big data are implicit, previously unknown and potentially useful information and valuable knowledge that can be discovered by data science. Due to their value, these big data are often considered as the new oil. In recent years, many governments make their collected big data freely available to their citizens, who could then gain some insights about services available in the city from these open data. In this paper, we present a cognitive and predictive analytic approach to analyze open data for discovering interesting patterns such as tipping patterns. In general, tipping is a voluntary action conceived as social norm that is valuable to service workers in many countries. With the introduction of ride hailing services, traditional taxi services have began facing increased competition. As such, there are increasing interests in factors that are associated with generous tips. Hence, to evaluate the practicality of our approach, we conduct a case study on applying our approach to transportation data (e.g., taxi trip records) from New York City (NYC) to examine and predict tip generosity. Although we conducted the case study on NYC data, our presented approach is expected to be applicable to perform cognitive and predictive analytics on big open data from other cities.

Authors: Carson K. Leung (University of Manitoba, Canada)

Email: kleung@cs.umanitoba.ca

Who can view: Everyone
Webinar Price: Free
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Carson Leung is currently a Professor at the University of Manitoba, Canada. He has contributed more than 250 refereed publications on the topics of big data, computational intelligence, cognitive computing, data analytics, data mining, data science, fuzzy systems, machine learning, social network analysis, and visual analytics. He has also served on the Organizing Committee of the ACM CIKM, ACM SIGMOD, IEEE DSAA, IEEE ICDM, and other conferences.
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