Towards Online Data Mining System for Enterprises

Jan Kupcík, Tomás Hruska
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Abstract

As the amount of generated and stored data in enterprises increases, the significance of fast analyzing of this data rises. This paper introduces data mining system designed for high performance analyses of very large data sets, and presents its principles. The system supports processing of data stored in relational databases and data warehouses as well as processing of data streams, and discovering knowledge from these sources with data mining algorithms. To update the set of installed algorithms the system does not need a restart, so high availability can be achieved. Data analytic tasks are defined in a programming language of the Microsoft .NET platform with libraries provided by the system. Thus, experienced users are not limited by graphical designers and their features and are able to create complex intelligent analytic tasks. For storing and querying results a special storage system is outlined.
面向企业的在线数据挖掘系统
随着企业中生成和存储的数据量的增加,对这些数据进行快速分析的重要性也越来越高。本文介绍了为超大数据集的高性能分析而设计的数据挖掘系统,并介绍了其原理。该系统支持处理存储在关系数据库和数据仓库中的数据以及处理数据流,并通过数据挖掘算法从这些数据源中发现知识。要更新已安装的算法集,系统不需要重新启动,因此可以实现高可用性。数据分析任务是用microsoft.net平台的编程语言定义的,系统提供了库。因此,有经验的用户不受图形设计师及其特性的限制,能够创建复杂的智能分析任务。为存储和查询结果,概述了一种特殊的存储系统。
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