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Toward a New Model of Indexing Big Uncertain Data

Published: 07 November 2017 Publication History

Abstract

Nowadays, due to the growth of technology, there is a mass production of data (of large volume), available in a digital form. These currently available data are not unified but appear in different formats and types. The diversity of the data is based on the type of information, they contain, such as text, image, video and audio documents and also on their sources, such as data from sensors (high variety). In addition, with the expansion of the Internet and the World Wide Web, the majority of these data become the publicity available for a wide range of users (at high speed). The main objective of this work is to propose an efficient Big Uncertainty Web Data Services Indexing Model able to reasoning in uncertain data environment. More concretely, the proposed approach is based on two main phases: the first one consists on processing uncertain data in the syntactic indexing phase and the second one consists on the semantic indexing phase. These two phases are presented as two algorithms syntactic and semantic.

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MEDES '17: Proceedings of the 9th International Conference on Management of Digital EcoSystems
November 2017
299 pages
ISBN:9781450348959
DOI:10.1145/3167020
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Association for Computing Machinery

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Published: 07 November 2017

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Author Tags

  1. Big Data
  2. Indexing
  3. Uncertainty

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MEDES '17

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MEDES '17 Paper Acceptance Rate 41 of 65 submissions, 63%;
Overall Acceptance Rate 267 of 682 submissions, 39%

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