Abstract
With advances in technologies, huge volumes of a wide variety of valuable data—which may be of different levels of veracity—are easily generated or collected at a high velocity from a homogenous data source or various heterogeneous data sources in numerous real-life applications. Embedded in these big data are rich sources of information and knowledge. This calls for data science solutions to mine and analyze various types of big data for useful information and valuable knowledge. Movies are examples of big data. In this paper, we present a flexible query answering system (FQAS) for movie analytics. To elaborate, nowadays, data about movies are easy accessible. Movie analytics help to give insights about useful revenues, trends, marketing related to movies. In particular, we analyze movie datasets from data sources like Internet Movie Database (IMDb). Our FQAS makes use of our candidate matching process to generate a prediction of a movie IMDb rating as a response to user query on movie. Users also have flexibility to tune querying parameters. Evaluation results show the effectiveness of our data science approach—in particular, our FQAS—for movie analytics.
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This project is partially supported by (i) Natural Sciences and Engineering Research Council of Canada (NSERC) and (ii) University of Manitoba.
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Leung, C.K., Eckhardt, L.B., Sainbhi, A.S., Tran, C.T.K., Wen, Q., Lee, W. (2019). A Flexible Query Answering System for Movie Analytics. In: Cuzzocrea, A., Greco, S., Larsen, H., Saccà, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2019. Lecture Notes in Computer Science(), vol 11529. Springer, Cham. https://doi.org/10.1007/978-3-030-27629-4_24
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DOI: https://doi.org/10.1007/978-3-030-27629-4_24
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