基于Web Services的企业应用集成方案

介绍了Web Services技术及其体系结构。给出了遗留系统Web Services封装与实现方法以及基于Web Services架构实现企业信息共享的实例。实践表明,基于Web Services的企业应用集成方案,增强了系统间集成的方便度,降低了更新维护的成本和难度。     

基于混合语言的Web服务容器构建方法

针对在面向对象架构中集成C/C++开发的遗留系统和代码的问题,提出了一种基于混合语言的Web服务容器的构建方法。该方法利用Python语言的扩展和嵌入机制,将Web服务容器对服务的调用、部署和管理被转换为Python解释器对Python模块的调用、部署和管理,并在此基础上构建了一个灵活的可动态扩展的处理结构。与现有的解决方案相比,该方法弥补了它们的缺陷和不足,进一步完善了在面向服务架构中集成遗留系统和代码的集成技术。     

科学数据集成体系及最新进展研究

[目的/意义]调研国内外科学数据集成的最新研究和实践进展,为促进我国科学数据共享的进一步发展提供参考。[方法/过程]将科学数据集成体系划分为5个分支：科学数据集成理论、科学数据集成方法、科学数据集成技术工具、科学数据集成模型、科学数据集成平台,深入评析每个分支的最新进展。[结果/结论]为了提高科学数据集成效率,应大力推进科学数据的标准化工作,制定体现科学数据共享特点的、完善的标准体系,推动实现科学数据全生命周期管理的规范化。为了科学评价科学数据集成的成效,需要推进科学数据集成评估研究。     

基于SOA的遗留系统集成研究与应用

面向服务体系机构(Service-Oriented Architecture,SOA)使企业IT环境能够随着业务需求的变化而自由调整,为企业带来了机动性和灵活性。由于遗留系统通常执行着企业最重要的业务流程,所以能否很好地集成遗留系统是SOA成功的关键。首先介绍了基于SOA的遗留系统集成框架,然后着重研究了将遗留系统封装为Web服务的方法。     

基于JAVA-COM Bridge与J2EE连接器的COM数据源信息获取

研究了基于COM组件的非结构化数据源集成和转换,同时结合了J2EE体系结构中遗留信息系统数据访问的解决方案J2EE连接器,设计了访问COM数据源的资源适配器COMJCA,并将其应用于＂福州大学图书馆异构信息源整合信息获取项目＂中。     

基于.NET平台的遗留系统应用研究

本文分析了遗留系统的运行模式,提出了将遗留系统包装为Web服务的模型,并使用.NET系统实现了对遗留服务的Web服务包装,从而为企业既保持原有系统的完整性,又能进行遗留服务的应用集成和Web访问提供了一个可行的方案。     

面向WEB的数字图书馆异构数据集成技术

对数字图书馆数据集成的实现与应用技术进行了研究。在对数字图书馆各类数据资源体系结构、运行环境和使用方法等分析基础上，综合运用数据仓库与数据挖掘、XML、分布式Web应用等技术，提出了一种基于数据仓库的数据集成设计体系。     

集成的网上技术交易平台架构研究

阐述了集成的网上技术交易平台的三层系统架构,包括用户层、中间层和数据层,用面向对象建模语言UML语言分析了系统用户的用例,并结合集成网上技术交易平台的其他研究,得出集成网上技术交易平台的用户层、中间层和数据层组成,提出了数据集成和业务集成的策略和方案。     

基于SOA和Web Services架构高校数字图书馆数据集成研究

本文分析高校数字图书馆管理信息系统的建设应用现状及数据集成要求,提出了基于SOA和Web Services技术的高校数字图书馆数据集成的框架结构以及它的组成,分析了其中的关键技术和实现方法,并讨论了高校数字图书馆管理信息系统中运用这些技术的优点。     

基于XML的Web数据库集成设计和实现

本文探讨了Web数据库的集成技术,给出了一种Web数据向XML数据格式转换的模型及其实现方法。     

Wed使用挖掘数据预处理中的一种会话识别改进算法

本文论述了Web用户访问模式挖掘中的数据预处理,主要提出了数据预处理中如何识别会话的一种改进算法。该方法通过使用三个因素来构造会话:①根据先验知识,确定会话时间阈值识别会话;②根据页面访问时间统计分布,确定相邻网页访问时间间隔阈值识别会话;③页面内容及站点结构确定页面重要程度识别会话。实验结果表明,相对于传统的单一方法进行会话识别的方法,该方法能够准确的识别会话,更为合理有效。     

Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL,RDF and relational data stores

This paper proposes an approach to tackle the problem of querying large volume of statistical RDF data. Our approach relies on pre-aggregation strategies to better manage the analysis of this kind of data. Specifically, we define a conceptual model to represent original RDF data with aggregates in a multidimensional structure. A set of translations rules for converting a well-known multidimensional RDF modelling vocabulary into the proposed conceptual model is then proposed. We implement the conceptual model in six different data stores: two RDF triple stores (Jena TDB and Virtuoso), one graph-oriented NoSQL database (Neo4j), one column-oriented data store (Cassandra), and two relational databases (MySQL and PostGreSQL). We compare the querying performance, with and without aggregates, in these data stores. Experimental results, on real-world datasets containing 81.92 million triplets, show that pre-aggregation allows for reducing query runtime in all data stores. Neo4j NoSQL and relational databases with aggregates outperform triple stores speeding up to 99% query runtime.     

校园一卡通与图书馆管理信息系统的集成研究

采用组件封装和提供SOAP客户端的方法将遗产系统迁移至SOAP的具体实现技术,设计了相关的模块结构、同步表、信息流程、数据库,并进行了软件的实现和接口组件的开发。成功实现了校园一卡通系统与现有图书馆管理系统的无缝连接。     

Exploring adverse drug reactions of diabetes medicine using social media analytics and interactive visualizations

The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach’s performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines.     

政府信息资源共享与交换模式研究

政府信息资源的共享与交换模式是政府信息资源管理的重要内容之一.在分析传统政府信息资源共享与交换模式以及在充分考虑政府遗留系统的特征基础上,对政府信息资源共享与交换模式进行了改进,提出一种类似"工业总线"式的信息资源共享与交换模式.实践证明该模式具有保证政府遗留系统之间的安全性和独立性基础上的系统可扩展性,具有一定理论和实践意义.     

Data fusion with factored quantization for stock trend prediction using neural networks

As compared to the continuous temporal distributions, discrete data representations may be desired for simplified and faster data analysis and forecasting. Data compression can introduce one of the efficient ways to reduce continuous historical stock market data and present them in discrete forms; while predicting stock trend, a primary concern is towards up and down directions of the price movement and thus, data discretization for a focused approach can be beneficial. In this article, we propose a quantization-based data fusion approach with a primary motivation to reduce data complexity and hence, enhance the prediction ability of a model. Here, the continuous time-series values are transformed into discrete quantum values prior to applying them to a prediction model. We extend the proposed approach and factorize quantization by integrating different quantization step sizes. Such fused data can reduce the data to mainly concentrate on the stock price movement direction. To empirically evaluate the proposed approach for stock trend prediction, we adopt long short-term memory, deep neural network, and backpropagation neural network models and compare our prediction results with five existing approaches on several datasets using ten performance metrics. We analyze the impact of specific quantization factors and determine the individual best as well as overall best factor sizes; the results indicate a consistent performance enhancement in stock trend prediction accuracy as compared to the considered baseline methods with an improvement up to 7%. To evaluate the impact of quantization-based data fusion, we analyze time required to execute the experiments along with percentage reduction in the number of unique numeric terms. Further, these results are statistically evaluated using Wilcoxon signed-rank test. We discuss the superiority and applicability of factored quantization-based data fusion approach and conclude our work with potential future research directions.     

Question classification using limited labelled data

Question classification (QC) involves classifying given question based on the expected answer type and is an important task in the Question Answering(QA) system. Existing approaches for question classification use full training dataset to fine-tune the models. It is expensive and requires more time to develop labelled datasets in huge size. Hence, there is a need to develop approaches that can achieve comparable or state of the art performance using limited training instances. In this paper, we propose an approach that uses data augmentation as a tool to generate additional training instances. We evaluate our proposed approach on two question classification datasets namely TREC and ICHI datasets. Experimental results show that our proposed approach reduces the requirement of labelled instances (a) up to 81.7% and achieves new state of the art accuracy of 98.11 on TREC dataset and (b) up to 75% and achieves 67.9 on ICHI dataset.     

A scalable privacy-preserving recommendation scheme via bisecting k-means clustering

Privacy-preserving collaborative filtering is an emerging web-adaptation tool to cope with information overload problem without jeopardizing individuals’ privacy. However, collaborative filtering with privacy schemes commonly suffer from scalability and sparseness as the content in the domain proliferates. Moreover, applying privacy measures causes a distortion in collected data, which in turn defects accuracy of such systems. In this work, we propose a novel privacy-preserving collaborative filtering scheme based on bisecting k-means clustering in which we apply two preprocessing methods. The first preprocessing scheme deals with scalability problem by constructing a binary decision tree through a bisecting k-means clustering approach while the second produces clones of users by inserting pseudo-self-predictions into original user profiles to boost accuracy of scalability-enhanced structure. Sparse nature of collections are handled by transforming ratings into item features-based profiles. After analyzing our scheme with respect to privacy and supplementary costs, we perform experiments on benchmark data sets to evaluate it in terms of accuracy and online performance. Our empirical outcomes verify that combined effects of the proposed preprocessing schemes relieve scalability and augment accuracy significantly.     

数据挖掘技术在远程教育中的应用

数据挖掘技术在国外的大型商业、金融业、保险业、民航等大型企业得到了广泛应用。国内目前总体上处于理论探讨、应用试验阶段。以人力资源开发为出发点，本文拟从现代远程教育的角度，谈谈数据挖掘技术的应用，最后指出数据挖掘技术成功应用的关键编程语言是XML。     

Automatic ranking of information retrieval systems using data fusion

Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the “(pseudo) relevant documents,” and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods.