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关键词： 数据仓库   搜索引擎   用户行为分析  

摘要： 针对传统分布式模型在海量日志并行处理时的可扩展性和并行程序编写困难的问题,提出了基于数据仓库的海量搜索日志分析系统架构。利用Hadoop分布式文件系统(HDFS)存储海量搜索日志,并对搜索日志进行清洗处理,采用impala对数据进行高速的处理,将处理后的统计结果导入到数据仓库中,使用Pentaho BI对数据进行多维分析和统计报表。获取了关键词分析、查询频率、热词排行、查询词和时间分布、网站排名、用户统计等6个分析主题。分析结果对于搜索引擎的排序算法和系统优化都有一定的指导意义。

关键词： Data sharing   Data repositories   Data infrastructure   Imaging data   Common data elements   Consents   GENE-EXPRESSION OMNIBUS   HIGH-QUALITY   BIG DATA   PROJECT   MICROARRAY   SEQUENCE   ARCHIVE   HIPPOCAMPAL   MULTISITE   FRAMEWORK  

摘要： There are many challenges to developing treatments for complex diseases. This review explores the question of whether it is possible to imagine a data repository that would increase the pace of understanding complex diseases sufficiently well to facilitate the development of effective treatments. First, consideration is given to the amount of data that might be needed for such a data repository and whether the existing data storage infrastructure is enough. Several successful data repositories are then examined to see if they have common characteristics. An area of science where unsuccessful attempts to develop a data infrastructure is then described to see what lessons could be learned for a data repository devoted to complex disease. Then, a variety of issues related to sharing data are discussed. In some of these areas, it is reasonably clear how to move forward. In other areas, there are significant open questions that need to be addressed by all data repositories. Using that baseline information, the question of whether data archives can be effective in understanding a complex disease is explored. The major goal of such a data archive is likely to be identifying biomarkers that define sub-populations of the disease. Published by Elsevier Ltd.

关键词： Machine learning   Classification   Biomedical literature   Clinical data repository   Ontology   Disease-specific vocabulary   DOMAIN KNOWLEDGE   CENTRALITY   TESTS  

摘要： Objective: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance: and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies. Method: We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s). Results: Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvem

关键词： UNIFICATION   SEQUENCES   ONTOLOGY   GENE   TOOL  

摘要： The last decade has seen a surge in published genome-scale information for Chinese hamster ovary (CHO) cells, which are the main production vehicles for therapeutic proteins. While a single access point is available at www. CHOgenome. org, the primary data is distributed over several databases at different institutions. Currently research is frequently hampered by a plethora of gene names and IDs that vary between published draft genomes and databases making systems biology analyses cumbersome and elaborate. Here we present CHOmine, an integrative data warehouse connecting data from various databases and links to other ones. Furthermore, we introduce CHOmodel, a web based resource that provides access to recently published CHO cell line specific metabolic reconstructions. Both resources allow to query CHO relevant data, find interconnections between different types of data and thus provides a simple, standardized entry point to the world of CHO systems biology.

关键词： Pattern discovery   Data mining   Prediction   Imbalanced data   Clinical data repository   SEQUENCE PATTERNS   BOOSTED SVM   RULES   RISK  

摘要： Background: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. Methods: We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e. g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. Results: We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naive Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. Conclusions: Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data

关键词： Data analysis   Database   Neuroscience   Statistics   Visualization   Mouse behaviour   AHCODA   Phenotyping   BEHAVIOR   MICE  

摘要： Background: Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available for standardized quality control, data analysis and mining at the resolution of individual mice. Description: Here, we present AHCODA-DB, a public data repository with standardized quality control and exclusion criteria aimed to enhance robustness of data, enabled with web-based mining tools for the analysis of individually and group-wise collected mouse phenotypic data. AHCODA-DB allows monitoring in vivo effects of compounds collected from conventional behavioural tests and from automated home-cage experiments assessing spontaneous behaviour, anxiety and cognition without human interference. AHCODA-DB includes such data from mutant mice (transgenics, knock-out, knock-in), (recombinant) inbred strains, and compound effects in wildtype mice and disease models. AHCODA-DB provides real time statistical analyses with single mouse resolution and versatile suite of data presentation tools. On March 9th, 2017 AHCODA-DB contained 650 k data points on 2419 parameters from 1563 mice. Conclusion: AHCODA-DB provides users with tools to systematically explore mouse behavioural data, both with positive and negative outcome, published and unpublished, across time and experiments with single mouse resolution. The standardized (automated) experimental settings and the large current dataset (1563 mice) in AHCODA-DB provide a unique framework for the interpretation of behavioural data and drug effects. The use of common ontologies allows data export to other databases such as the Mouse Phenome Database. Unbiased presentation of positive and negative data obtained under the highly standardized screening conditions increase cost efficiency of publicly funded mouse screening projects and help to reach consensus conclusions on drug responses and mouse behavioural phenotype

关键词： 院校研究   数据仓库   基础数据平台   逻辑模型  

摘要： 为加强信息完整性与一致性、改进信息实时性、提升数据质量和共享程度,更好地支持数据挖掘与决策,高校需要搭建统一、全方位的数据仓库。文章基于中国大学数据仓库设计与应用的实际过程,对数据仓库的总体架构、建设过程、基础平台等方面进行简述,以期构建支撑决策管理、提高科研和辅助教学一体化的服务体系,进而为高校智能体系建设奠定基础,促进高校跨越式发展。

关键词： 不动产单元   不动产登记   信息管理基础平台   面向服务   数据仓库  

摘要： 提出了广东省不动产登记信息管理基础平台的技术框架,重点对应用系统的体系构成及主要功能、数据库的逻辑结构及物理存储进行了总体设计,通过采用ESB、My CAT分布式数据库中间件、OLAP多维分析等关键技术实现了平台的开发和应用,为广东省全面开展不动产登记工作提供了重要的技术保障。

关键词： content area   intellectual/developmental disability   methodological development   oral health   physical health care and policy   systematic review  

摘要： Background: This review describes the methods used for a systematic review of oral health intervention literature in a target population (people with intellectual and developmental disability (I/DD)), which spans a broad range of interventions and study types, conducted with specialized software. Objective: The aim of this article is to demonstrate the review strategy, using the free, online systematic review data repository (SRDR) tool, for oral health interventions aimed at reducing disparities between people with I/DD and the general population. Research Design: Researchers used online title/abstract review (Abstrackr) and data extraction (SRDR) tools to structure the literature review and data extraction. A practicing clinician and an expert methodologist completed the quality review for each study. The data extraction team reported on the experience of using and customizing the SRDR. Results: Using the SRDR, the team developed four extraction templates for eight key questions and completed extraction on 125 articles. Conclusions: This report discusses the advantages and disadvantages of using an electronic tool, such as the SRDR, in completing a systematic review in an area of growing research. This review provides valuable insight for researchers who are considering the use of the SRDR.

关键词： HANA   石油销售企业   内存数据库   数据仓库   决策支持系统  

摘要： 自从HANA(德国SAP(思爱普)公司于2010年发布的高性能分析应用软件)问世以来,其强大的数据查询和处理能力吸引了越来越多的用户,采用HANA内存数据库的BW(数据仓库)方案已越来越被广泛使用。阐述了HANA内存数据库的技术特点及基于HANA内存数据库的决策支持技术架构和该架构下的大数据分析前景,分析了某石油销售企业销售BW从Oracle(甲骨文)迁移到HANA后的系统空间使用情况。结果表明,迁移至HANA的数据库运行效率较之前传统数据库的运行效率有了2~7倍的提升,使决策支持系统的整体性能获得大幅提高,为今后决策支持系统的发展奠定了良好的基础。