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.
Original language | English |
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Title of host publication | Flexible Query Answering Systems - 13th International Conference, FQAS 2019, Proceedings |
Editors | Alfredo Cuzzocrea, Sergio Greco, Domenico Saccà, Henrik Legind Larsen, Troels Andreasen, Henning Christiansen |
Publisher | Springer Verlag |
Pages | 250-261 |
Number of pages | 12 |
ISBN (Print) | 9783030276287 |
DOIs | |
State | Published - 2019 |
Event | 13th International Conference on Flexible Query Answering Systems, FQAS 2019 - Amantea, Italy Duration: 2 Jul 2019 → 5 Jul 2019 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11529 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 13th International Conference on Flexible Query Answering Systems, FQAS 2019 |
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Country/Territory | Italy |
City | Amantea |
Period | 2/07/19 → 5/07/19 |
Bibliographical note
Publisher Copyright:© 2019, Springer Nature Switzerland AG.
Keywords
- Data analytics
- Data mining
- Data science
- Information retrieval
- Movie
- Movie analytics
- Movie rating
- Predictive analytics