A NoSQL approach for aspect mining of cultural heritage streaming data

Gerasimos Vonitsanos, Andreas Kanavos, Alaa Mohasseb, Dimitrios Tsolis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Aspect mining constitutes an essential part of delivering concise and, perhaps more importantly, accurately tailored cultural content. With the advent of social media, there is a data abundance so that analytics can be reliably designed for ultimately providing valuable information towards a given product or service. Naturally representing and efficiently processing a large number of opinions can be implemented with the use of streaming technologies. Big data analytics are especially important in the case of cultural content management where reviews and opinions may be analyzed in order to extract meaningful representations. In this paper, a NoSQL database method for aspect mining of a cultural heritage scenario by taking advantage of Apache Spark streaming architecture is presented.
Original languageEnglish
Title of host publicationProceedings of 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)
PublisherIEEE
Number of pages4
ISBN (Electronic)978-1-7281-4959-2
ISBN (Print)978-1-7281-4960-8
DOIs
Publication statusPublished - 14 Nov 2019
EventInternational Conference on Information, Intelligence, Systems and Applications - Patras, Greece
Duration: 15 Jul 201917 Jul 2019
Conference number: 10th
https://ieeexplore.ieee.org/xpl/conhome/8893976/proceeding

Conference

ConferenceInternational Conference on Information, Intelligence, Systems and Applications
Abbreviated titleIISA
Country/TerritoryGreece
CityPatras
Period15/07/1917/07/19
Internet address

Keywords

  • Apache Cassandra
  • Apache Spark Streaming
  • big data analytics
  • cultural heritage management
  • knowledge representation
  • topic modeling tweet
  • stream analysis

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