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

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

Towards Semantic Mashup Tools for Big Data Analysis

Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014International audienceBig Data is generally characterized by three V’s: volume, velocity, and variety. For the Semantic Web community, the variety dimension could be the most appropriate and interesting aspect to contribute in. Since the real-world use of Big Data is for data analytics purposes of knowledge workers in different domains, we can consider mashup approach as an effective tool to create user-generated solution based on available private/public resources. This paper gives brief overview and comparison of some semantic mashup tools which can be employed to mash up various data sources in heterogenous data format