FIU-Miner (a fast, integrated, and user-friendly system for data mining) and its applications

作者:Tao Li, Chunqiu Zeng, Wubai Zhou, Wei Xue, Yue Huang, Zheng Liu, Qifeng Zhou, Bin Xia, Qing Wang, Wentao Wang, Xiaolong Zhu

摘要

The advent of Big Data era drives data analysts from different domains to use data mining techniques for data analysis. However, performing data analysis in a specific domain is not trivial; it often requires complex task configuration, onerous integration of algorithms, and efficient execution in distributed environments. Few efforts have been paid on developing effective tools to facilitate data analysts in conducting complex data analysis tasks. In this paper, we design and implement FIU-Miner, a Fast, Integrated, and User-friendly system to ease data analysis. FIU-Miner allows users to rapidly configure a complex data analysis task without writing a single line of code. It also helps users conveniently import and integrate different analysis programs. Further, it significantly balances resource utilization and task execution in heterogeneous environments. Case studies of real-world applications demonstrate the efficacy and effectiveness of our proposed system.

论文关键词:Feature Selection, Inventory Management, Feature Selection Method, Data Mining Algorithm, Runtime Environment

论文评审过程:

论文官网地址:https://doi.org/10.1007/s10115-016-1014-0