Now showing items 1-6 of 6
Edge-based mining of frequent subgraphs from graph streams
In the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from ...
Frequent subgraph mining from streams of linked graph structured data
(CEUR Workshop Proceedings, 2015)
Nowadays, high volumes of high-value data (e.g., semantic web data) can be generated and published at a high velocity. A collection of these data can be viewed as a big, interlinked, dynamic graph structure of linked ...
A data analytic algorithm for managing, querying, and processing uncertain big data in cloud environments
Big data are everywhere as high volumes of varieties of valuable precise and uncertain data can be easily collected or generated at high velocity in various real-life applications. Embedded in these big data are rich sets ...
Effectively and efficiently mining frequent patterns from dense graph streams on disk
In this paper, we focus on dense graph streams, which can be generated in various applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. We also investigate the problem ...
Frequent pattern mining from dense graph streams
(CEUR Workshop Proceedings, 2014)
As technology advances, streams of data can be produced in many applications such as social networks, sensor networks, bioinformatics, and chemical informatics. These kinds of streaming data share a property in common--namely, ...
A combined deep-learning and transfer-learning approach for supporting social influence prediction
Social influence is a phenomenon describing the spread of opinions across the population. Nowadays, social influence analysis (SIA) has a great impact. For example, viral marketing and online content recommendation are ...