首页> 外文会议>International Conference on Enterprise Information Systems (ICEIS 2001), Jul 7-10, 2001, Setubal, Portugal >AN INFORMATION SYSTEM VIEW OF CONSISTENCY AND INTEGRITY IN ENTERPRISE OPERATIONS
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AN INFORMATION SYSTEM VIEW OF CONSISTENCY AND INTEGRITY IN ENTERPRISE OPERATIONS

机译:企业运营中的一致性和完整性的信息系统视图

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Mining for association rules is one of the fundamental data mining methods. In this paper we describe how to efficiently integrate association rule mining algorithms with relational database systems. From our point of view direct access of the algorithms to the database system is a basic requirement when transferring data mining technology into daily operation. This is especially true in the context of large data warehouses, where exporting the mining data and preparing it outside the database system becomes annoying or even in-feasible. The development of our own approach is mainly motivated by shortcomings of current solutions. We investigate the most challenging problems by contrasting the prototypical but somewhat academic as-sociation mining scenario from basket analysis with a real-world application. We thoroughly compile the requirements arising from mining an operative data warehouse at DaimlerChrysler. We generalize the re-quirements and address them by developing our own approach. We explain its basic design and give the details behind our implementation. Based on the warehouse, we evaluate our own approach together with commercial mining solutions. It turns out that regarding runtime and scalability we clearly outperform the commercial tools accessible to us. More important, our new approach supports mining tasks that are not directly addressable by commercial mining solutions.
机译:关联规则的挖掘是基本的数据挖掘方法之一。在本文中,我们描述了如何有效地将关联规则挖掘算法与关系数据库系统集成在一起。从我们的角度来看,将数据挖掘技术转换为日常操作时,对算法的直接访问是数据库系统的基本要求。在大型数据仓库的情况下尤其如此,在这种情况下,导出挖掘数据并在数据库系统外部进行准备变得烦人甚至不可行。我们自己方法的发展主要是由于当前解决方案的不足。我们通过比较篮子分析中的典型但学术性较高的协会关联挖掘场景和实际应用程序来研究最具挑战性的问题。我们将在戴姆勒克莱斯勒(DaimlerChrysler)开采可操作的数据仓库时产生的需求进行全面汇编。我们概括了需求,并通过开发自己的方法来解决这些需求。我们将说明其基本设计,并提供实现背后的详细信息。基于仓库,我们评估自己的方法以及商业采矿解决方案。事实证明,在运行时和可伸缩性方面,我们明显优于我们可以使用的商业工具。更重要的是,我们的新方法支持无法通过商业采矿解决方案直接解决的采矿任务。

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