An Efficient Data Analysis For Edge-Enabled Distributed Environments using Tractable Probabilistic Models

Kihyuk Nam, Taewhi Lee, Sung-Soo Kim, Choon Seo Park, Taek Yong Nam, Insik Shin

Abstract: Huge amounts of data are ceaselessly being generated by a variety of devices, and the processing efforts for their collection and analysis grows exponentially as well. Storing them in one place and getting exact answers is almost impractical. Furthermore, computing aggregation and statistics that most exploratory data analysis would require imposes a heavy burden on networking and computing infrastructures. By adopting the edge/fog computing paradigm that has recently been developing can reduce such overheads by offloading jobs from central clouds to edge devices. We try to go one step further in this direction by approximating aggregate values and statistics for data analysis using tractable probabilistic models and optimizing network performance. This paper evaluates our preliminary result of our on-going project that was gained by fast-prototyping using Sum-Product Networks.