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

UPM academic transformation

UPM Academic Transformation is implemented through progressive institutional convergence by the people as agent of transformation towards achieving inclusive and equitable education. This transformation is made possible by dynamic and vibrant policies, mechanisms and processes. It will provide greater opportunity for students to graduate with full potential and talent in confronting future challenges. Student has the autonomy to co-curate their study path and experience inclusive education in a learning environment that celebrates flexible curriculum, personalized delivery and competency-based assessment. Educators pulse the transformation and escalate their academic profession through global and scholarly participation in knowledge enrichment and new academic discovery. These will accelerate UPM international reputation and recognition