Enhanced learner centered pedagogical strategy: Promoting STEM with underrepresented populations

John, Benneaser; Thavavel, V.; Jayakumar, Jaikishan; Muthukumar, A; Poornaselvan, K. J.

doi:10.1109/isecon.2014.6891026

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Balboni et al [37] Evolution Analysis natural language processing engines to build temporal graph database Got large amount of open source documents Wu and Chen [38] Frequent Sub-graph Mining By normalizing the incidence matrix Achieved higher speed and efficiency John et al [39] Learning process enhancement against population Natural Language Processing enhanced learner centered online learning experience Xu and Luo [40] Expression-Driven Sketch Graph Matching for Face Recognition multi-layer grammatical face model recognition rates were improved, especially for the smiling and screaming faces whose line-edge maps are greatly distorted Figueira and Libkin [41] Querying Graphs Parikh automata real-life querying…”

Section: Business Process Monitoring Domain For Query Workload Balancingmentioning

confidence: 99%

A Survey on Graph Database Management Techniques for Huge Unstructured Data

Patil¹,

Kiran²,

Patel³

2018

IJECE

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Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management.

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