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关键词： 商务智能   赋能   因数   商业价值   数据挖掘   数据仓库   甲骨文公司   商业决策  

摘要： 大数据时代,越来越多的组织转型为数据驱动型组织.这类组织的特点是以数据挖掘商业价值,从而驱动企业做出正确的商业决策.然而,如何有效分析数据并挖掘出其商业价值仍然困扰着诸多企业.近日,甲骨文公司副总裁及中国区云平台总经理吴承杨撰文,从数据仓库-商务智能的基石、当数据仓库云实现自治、自治调优可解放劳动力、让人力真正发挥其价值等方面进行了精彩的分析,详情请关注我刊微信号：shuzitongxin.

关键词： aged   data warehouse   patient readmission   risk factors  

摘要： What is knownHospital readmission within 30 days of patient discharge has become a standard to judge the quality of hospitalizations. It is estimated that 14% of the elderly, people over 75 years old or those over 65 with comorbidities, are at risk of readmission, of which 23% are avoidable. It may be possible to identify elderly patients at risk of readmission and implement steps to reduce avoidable *** aim of this study was to identify iatrogenic risk factors for readmission. The secondary objective was to evaluate the rate of drug‐related readmissions (DRRs) among all readmissions and compare it to the rate of readmissions for other *** conducted a retrospective, matched, case‐control study to identify non‐demographic risk factors for avoidable readmission, specifically DRRs. The study included patients hospitalized between 1 September 2014 and 31 October 2015 in an 800‐bed university hospital. We included patients aged 75 and over. Cases consisted of patients readmitted to the emergency department within 30 days of initial discharge. Controls did not return to the emergency department within 30 days. Cases and controls were matched on sex and age because they are known as readmissions risk factors. After comparison of the mean or percentage between cases and controls for each variable, we conducted a conditional logistic *** risk factors identified were an emergency admission at the index hospitalization, returning home after discharge, a history of unplanned readmissions and prescription of nervous system drugs. Otherwise, 11.4% of the readmissions were DRRs, of which 30% were caused by an overdose of antihypertensive. The number of drugs at readmission was higher, and potentially inappropriate medications were more widely prescribed for DRRs than for readmissions for other *** is new and conclusionIn this matched case‐control retrospective study, after controlling for gender and

关键词： 数据仓库   OLAP   多维数据集  

摘要： 本文基于企业真实的数据仓库销售数据,进行了OLAP分析方法的研究,并通过SQL Server的Analysis Services工具进行了分析方法与过程的实践,为企业管理人员提供决策支持,为后续的数据挖掘提供基础。

关键词： 数据仓库   冰山立方体   位图索引   动态剪枝  

摘要： 为了提高冰山立方体的计算性能,提出一种基于位图索引改进的DPBUC_BI(dynamic pruning based BUC_BI)算法。该算法利用位图索引按列组织的特性重新定义BUC(bottom-up computation)算法的分组操作,加快了数据的加载和查询;通过使用逻辑位运算实现聚合计算,提高了算法的计算性能。针对部分数据聚集现象增加动态剪枝策略,在保证算法正确性的情况下进一步提高了冰山立方体计算性能。最后将DPBUC_BI算法应用于机票结算数据的冰山立方体计算中,实验结果表明:该算法可以很好地提升计算性能,相对于经典BUC算法在时间性能上有一定的提高。

关键词： 数据仓库   DW   OLAP   OLTP   数据方体  

摘要： 在大数据时代的背景下,社会各领域都在筹建自己的数据仓库,供大数据挖掘等技术分析,获取有价值的信息。本文就民航空管自动转报系统DB向DW升级的原理、优缺点以及可行性做出论述。

关键词： 数据仓库   高校青年教师   发展性教学评价  

摘要： 发展性教学评价是促进青年教师专业成长的一种重要手段。本文根据发展性教学评价的特点,以学校原有数据库为基础,构建面向高校青年教师发展性教学评价的数据仓库。该数据仓库既体现了学生评价的重要性,又实现了多主体评价和尊重被评价教师的主体地位;结合青年教师的专业成长阶段,充分利用历史数据,从不同视角、不同层次对数据进行纵向分析,为客观、公正评价青年教师的教学水平提供依据,为青年教师持续改进教学质量提供支持,为教育主管部门决策提供参考。

关键词： 数据仓库   集装箱码头   吞吐量  

摘要： 随着信息技术的快速发展,数据仓库技术在信息技术领域日趋成熟且逐渐成为研究热点,成为信息技术领域的前沿技术.实践证明,数据仓库的建立提升了企业的效益.集装箱码头也能通过数据仓库的建立来提升企业的核心竞争力.以泉州太平洋集装箱码头数据仓库项目需求为基础,主要研究码头吞吐量数据仓库的设计方法,通过对集装箱码头业务的分析和整理,选择合适的技术路线和数据仓库架构,实现数据仓库的建立.

关键词： 城市轨道交通   数据中心   网络化运营   数据仓库   运营评估   数据挖掘   决策   指标  

摘要： 在分析网络化运营条件下大规模数据特征的基础上,根据业务系统的数据融合需求,提出城市轨道交通数据中心平台的分层框架和功能定位。探讨了线网管理的数据结构体系、数据仓库的递阶逻辑建模、面向运营业务决策的应用集市等构建方法,并以线网客流特征识别的业务应用为对象,提出了数据集市的关联规则挖掘原理、预测立方体在贯通多类运营评估应用的计算方法。结合某城市轨道交通数据中心建设案例,描述了基于数据仓库的搭建过程及相关业务的调用逻辑,表明了线网数据管理对跨业务系统融合数据的意义,有效地提高了运营管理效率。

关键词： 大数据   数据治理   数据仓库  

摘要： 数据治理是实现持续改进数据质量、实现数据价值的基础。在传统行业领域,以DAMA国际、IBM为典型的组织建立了数据管理知识框架和数据管理流程,为金融、物流等多领域提供数据治理实施指导。当下,我们正在被数据淹没,来自工作、生活各个方面的海量数据接踵而至。该文提出一个大数据治理框架,对其技术架构和组织机构做了阐述,并结合一个智能制造工业4.0系统为例进行了阐述。该框架对于整理数据资产、构建数据仓库、建立数据关联,以及运用大数据分析技术、实现基于数据的辅助决策有着现实意义。

关键词： Multi-label   Software   Tools   Datasets   Repository   CLASSIFICATION   SOFTWARE   SPACE  

摘要： New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection of datasets, partitioning, training, analysis of results and, finally, comparison with existing methods. This last step is often hampered since it involves using exactly the same datasets, partitioned in the same way and using the same validation strategy. In this paper we present a set of tools whose objective is to facilitate the management of multi-label datasets, aiming to standardize the experimentation procedure. The two main tools are an R package, ***, and a web repository with datasets, Cometa. Together, these tools will simplify the collection of datasets, their partitioning, documentation and export to multiple formats, among other functions. Some tips, recommendations and guidelines for a good experimental analysis of multi-label methods are also presented. (C) 2018 Elsevier B.V. All rights reserved.