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1402 10th Ave S, Birmingham, Alabama 35294
Title: Big Data Mining on Trees and Graphs: A Survey
Abstract: Data mining has been a key component of data analytics for decades, and the recent trend of Big Data has given rise to many distributed systems for mining big data, as well as building giant models as represented by deep learning. In this survey, we focus on the non-regular type of data mining (i.e., not on the traditional tabular data), with special emphasis on graphs and trees. We consider data mining problems such as mining subgraphs from a big graph (e.g., community finding and graph matching), frequent pattern mining for various patterns like subgraphs and subtrees, and decision tree models. We then review the recent Big Data solutions to them, such as distributed systems to scale out, and big models like deep forest. As we shall see, existing solutions are dominantly IO-bound, making CPU cores underutilized, and thus we envision our future work to address the IO bottleneck to allow CPU-bound mining of Big Data.
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