A review on knowledge extraction for Business operations using data mining

Sanyam Bharara, A. Sabitha, Abhay Bansal
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引用次数: 3

Abstract

The Knowledge Economy is of great importance in various business fields which had resulted in increased demand for the people having high order thinking skills and unpredicted-problem-solving at workplace. Every organization has a Knowledge Management (KM) department as Knowledge itself is a precious resource of the organization. The latest trends in KM include Customer and Vendor knowledge, Mobile Applications for KM, Collaborative Knowledge Management System (KMS) and Social intranet which can be integrated with business processes. The knowledge extracted can be stored and processed to enhance business intelligence. KM works with various business fields like Marketing, Sales, Human Resource, Operations, Supply Chain, etc. Due to frequent changes in operation of processes and Quality policies, the knowledge extracted from these processes can play a vital role in enhancing business processes. In this paper we had proposed various models of KM & Business Operations and the need of data mining technique which can be used to deliver appropriate knowledge to the user.
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基于数据挖掘的业务操作知识提取综述
知识经济在各个商业领域都非常重要,这导致了对具有高阶思维能力和在工作场所解决意外问题的人才的需求增加。知识本身就是组织的宝贵资源,每个组织都设有知识管理部门。知识管理的最新趋势包括客户和供应商知识、知识管理的移动应用、协作知识管理系统(KMS)和可与业务流程集成的社会内部网。提取的知识可以存储和处理,以增强业务智能。KM与市场、销售、人力资源、运营、供应链等各个业务领域合作。由于过程操作和质量政策的频繁变化,从这些过程中提取的知识可以在增强业务过程中发挥至关重要的作用。在本文中,我们提出了知识管理与业务运营的各种模型,以及数据挖掘技术的需求,这些技术可以用来向用户提供适当的知识。
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