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

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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.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2022-12
Language
English
Citation

2022 IEEE International Conference on Big Data, Big Data 2022, pp.6787 - 6789

DOI
10.1109/BigData55660.2022.10021024
URI
http://hdl.handle.net/10203/306690
Appears in Collection
CS-Conference Papers(학술회의논문)
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