Optimizing Database Load and Extract for Big Data Era

Sridhar, K. T.; Sakkeer, M. A.

doi:10.1007/978-3-319-05813-9_34

Cited by 9 publications

(1 citation statement)

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“…Several researches have studied on various issues to map and fuse data on multiple sources as a means to translate the user query. The efforts include user interface design [20][21], usability [5], data management model [22][23][24][25][26], query language format (i.e., SPARQL) [27][28], query expressivity [4], [29][30], mapping [9], [26], [31][32], fusing [33][34][35] and ranking [3], [36][37][38]. Most of these approaches rely on linguistic triple (Subject-Predicate-Object) identification [38] which may be grammar and language dependent.…”

Section: Fig 1 Semantic Analysis Querying On Big Datamentioning

confidence: 99%

Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics

Sharef

Shafazand

Nazri

et al. 2018

IJIE

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Integration of heterogeneous data sources is a crucial step in big data analytics, although it creates ambiguity issues during mapping between the sources due to the variation in the query terms, data structure and granularity conflicts. However, there are limited researches on effective big data integration to address the ambiguity issue for big data analytics. This paper introduces a self-adaptive model for big data integration by exploiting the data structure during querying in order to mitigate and resolve ambiguities. An assessment of a preliminary work on the Geography and Quran dataset is reported to illustrate the feasibility of the proposed model that motivates future work such as solving complex query.

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