We propose a data processing platform that can analyze a large amount of tree-structured data. The proposed platform stores tree-structured data in separated files corresponding to each attribute, and uses MapReduce framework for distributed computing. These methods enable to reduce disk I/O load, and to avoid computationally-intensive processing, such as grouping or combining of records. An early stage of data mining needs try-and-error processes to find out how to analyze and utilize the data. Our platform speeds up computations of the try-and-error processes, such as appending new attributes and calculating statistics of attributes. Experimental results show that the proposed methods are efficient to process large-scale tree-structure data, and our platform is comparable or superior to a traditional relational database system. With the proposed platform, it became possible to process 90 GB data within 5 minutes on 6 benchmark tasks. We also describe system architecture for the try-and-error phase, which integrates the proposed platform and a few Web applications. The main contributions of this paper are: (1) formulation of vertical partitioning for tree-structured data, (2) effective utilization of MapReduce, and (3) construction of large-scale data mining system for a try-and-error phase.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.