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关键词： ERP   金属矿山   工作流   模糊决策   信息融合   数据仓库  

摘要： 实施ERP是金属矿山企业进行信息化建设的重要部分。通过对比分析ERP在金属矿山行业的应用情况,指出了目前存在的行业通用性差、数据来源不统一、成本管理粗糙、行业针对性差等问题。应基于成本控制理论,采用基于工作流的构建组装技术、模糊决策理论、信息融合技术以及数据仓库等技术,建立面向金属矿山行业的ERP系统。

关键词： 数据仓库   关系数据库   替代方案  

摘要： 本文针对专业化信息管理系统提出一种基于关系数据库OLTP方式下的数据仓库替代实现方案,在软硬件要求不作改变的情况下,实现数据量大、实时性强、多维信息度查询效果,具有较强的实用价值。

关键词： 海洋地球物理   空间数据仓库   数字海底   多维数据模型  

摘要： 本文将空间数据仓库技术引入到海洋地球物理空间数据的管理和应用上来,探索一套适合于海洋地球物理调查和科研工作的空间数据仓库构建方法.本文重点从数据提取工具、空间数据仓库和应用服务器等几个方面对海洋地球物理空间数据仓库系统进行设计和开发,实现海洋地球物理空间数据的组织和管理,向用户提供便捷、直观、高效的面向应用主题的空间数据查询、分析、统计和可视化等服务.

关键词： 数据仓库   数据挖掘   决策支持系统  

摘要： 基于农作物生产的相关数据,创建农作物病虫害分析的数据仓库,运用数据挖掘技术提取分类规则,再将获取的规则用于农业病虫害的诊断;通过对实例运行结果的分析,验证了数据仓库和数据挖掘技术与农业决策支持系统相结合方案的可行性。

关键词： 数据挖掘   高职院校   招生专业  

摘要： 随着知识经济的发展,信息技术得到广泛的应用,本文详细描述了使用数据仓库和数据挖掘的方法,对高职院校的招生录取数据进行挖掘分析,为招生录取提供有用信息,为高职院校跨省招生专业决策提供技术支撑,使高职院校能够合理的分配跨省招生专业计划,减少招生计划的浪费,提高学生的报到率。

关键词： Data warehousing   pushing   Knowledge based engineering   Conference Papers   paper   Knowledge Bases   Artificial Intelligence   Database  

摘要： This special issue on "Pushing Artificial Intelligence in Database and Data Warehouse Systems" of Data & Knowledge Engineering presents a rigorous selection of the best papers of the Invited Session "Advanced Knowledge-based Systems" of the LNCS/LNA1 13th International Conference on Knowledge-based and Intelligent Information & Engineering Systems (KES 2009), held in Santiago, Chile, during September 28-30,2009. Following the success of the Invited Session "Advanced Knowledge-based Systems" of LNCS/ LNAI KES 2008, the 2009 event has attracted a large number of submissions, and, after a rigorous review process, only 8 papers have been selected for final publication. After the conference these papers have been invited for submission to the Data & Knowledge Engineering special issue on "Pushing Artificial Intelligence in Database and Data Warehouse Systems". After two rigorous review rounds, only 4 papers have been accepted for final publication in this special issue.

关键词： Query processing  

摘要： Today, data warehouse systems are faced with challenges for providing nearly realtime response times even for complex analytical queries on enormous data volumes. Highly scalable computing clusters in combination with parallel in-memory processing of compressed data are valuable techniques to address these challenges. In this paper, we give an overview on core techniques of the IBM Smart Analytics Optimizer—an accelerator engine for IBM’s mainframe database system DB2 for z/OS. We particularly discuss aspects of a seamless integration between the two worlds and describe techniques exploiting features of modern hardware such as parallel processing, cache utilization, and SIMD. We describe issues encountered during the development and evaluation of our system and outline current research activities for solving them. © 2011, Springer-Verlag.

关键词： On-line Analytical Processing (OLAP)   Data warehouse   Conceptual multidimensional model   Aggregation   OO modeling   UML   Production rule   CONCEPTUAL-MODEL   HIERARCHIES   CUBE  

摘要： Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in the Production Rule Representation language (PRR). Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge enables an early modeling of user requirements in a data warehouse project. A prototype has been developed based on the Java Expert System Shell (Jess). (C) 2011 Elsevier B.V. All rights reserved.

关键词： 数据仓库   在线分析处理   供电可靠性   数学分析模型   辅助决策   指标信息   供电可靠率   业务指标   关键绩效指标   商务智能  

摘要： 供电企业实时KPI系统在选用中要应用商务智能先进的原理、工具,以业务需求为主线,围绕供电局关键绩效指标,综合运用数据仓库、在线分析处理、数学分析模型等手段,建立自上而下一致的业务指标视图,通过横向主题分析、纵向专题分析,让决策者、管理者、业务人员能及时获取所关注的指标信息,为辅助决策提供依据,提高经营、决策的能力,提高以目标指标为导向的管理能力。