A Deep Web Data Integration System for Job Search

With the rapid development of Web, there are more and more Web databases available for users to access. At the same time, job searchers often have difficulties in first finding the right sources and then querying over them, providing such an integrated job search system over Web databases has become a Web application in high demand. Based on such consideration, we build a deep Web data integration system that supports unified access for users to multiple job Web sites as a job meta-search engine. In this paper, the architecture of the system is given first, and the key components in the system are introduced.

Web Data Cube Construction in Multidimensional On-line Analytical Processing Environment

This paper investigates how to integrate Web data into a multidimensional data warehouse （cube） for comprehensive on-line analytical processing （OLAP） and decision making. An approach for Web data-based cube construction is proposed, which includes Web data modeling based on MIX （ Metadam based Integration model for data X-change ）, generic and specific mapping rules design, and a transformation algorithm for mapping Web data to a multidimensional array. Besides, the structure and implementation of the prototype of a Web data base cube are discussed.

用PB8的Web Data Window DTC开发Web应用

浏览器/Web服务器+应用服务器/数据库服务器的三层或多层体系结构已成为当今应用开发技术的主流,本文着重介绍 PowerBuilder 8.0的 Web Data Window DTC的使用,阐述瘦客户技术的实现。

A Framework of Web Data Integrated LBS Middleware

In this paper, we propose a flexible locationbased service （LBS） middleware framework to make the development and deployment of new location based applications much easier. Considering the World Wide Web as a huge data source of location relative information, we integrate the common used web data extraction techniques into the middleware framework, exposing a unified web data interface for the upper applications to make them more attractive. Besides, the framework also emphasizes some common LBS issues, including positioning, location modeling, location-dependent query processing, privacy and secure management.

Web Data Aggregation in MOLAP:Approach,Language,and Implementation

This paper investigates the Web data aggregation issues in multidimensional on-line analytical processing （MOLAP） and presents a rule-driven aggregation approach. The core of the approach is defining aggregate rules. To define the rules for reading warehouse data and computing aggregates, a rule definition language - array aggregation language （AAL） is developed. This language treats an array as a function from indexes to values and provides syntax and semantics based on monads. External functions can be called in aggregation rules to specify array reading, writing, and aggregating. Based on the features of AAL, array operations are unified as function operations, which can be easily expressed and automatically evaluated. To implement the aggregation approach, a processor for computing aggregates over the base cube and for materializing them in the data warehouse is built, and the component structure and working principle of the aggregation processor are introduced.

Web Database Query Interface Annotation Based on User Collaboration

A vision based query interface annotation meth od is used to relate attributes and form elements in form based web query interfaces, this method can reach accuracy of 82%. And a user participation method is used to tune the result; user can answer ＂yes＂ or ＂no＂ for existing annotations, or manually annotate form elements. Mass feedback is added to the annotation algorithm to produce more accurate result. By this approach, query interface annotation can reach a perfect accuracy.

SCMR:a semantic-based coherence micro-cluster recognition algorithm for hybrid web data stream

Data aggregation from various web sources is very significant for web data analysis domain.In addition,the recognition of coherence micro cluster is one of the most interesting issues in the field of data aggregation.Until now,many algorithms have been proposed to work on this issue.However,the deficiency of these solutions is that they cannot recognize the micro-cluster data stream accurately.A semantic-based coherent micro-cluster recognition algorithm for hybrid web data stream is proposed.Firsdy,an objective function is proposed to recognize the coherence micro-cluster and then the coherence micro-cluster recognition algorithm for hybrid web data stream based on semantic is raised.Finally,the effectiveness and efficiency evaluation of the algorithm with extensive experiments is verified on real music data sets from Baidu inc.and Migu inc.The experimental results show that the proposed algorithm has better recall rate than the non-semantic micro cluster recognition algorithm and single source data flow micro cluster recognition algorithm.

Web data mining在远程教育中的应用

采用Web data mining对远程教育进行分析,根据受教育对象存在的个体差异,提出个性化远程学习系统的框架结构思想和个性化服务的理念,对相关信息进行数据挖掘并建立起一个集智能化、个性化为一体的远程教育系统,从而更好地改善远程教育服务的现状。

On Structure-based Web Data Extraction: The Model, Method and Application

Web data extraction is to obtain valuable data from the tremendous information resource of the World Wide Web according to the pre - defined pattern. It processes and classifies the data on the Web. Formalization of the procedure of Web data extraction is presented, as well as the description of crawling and extraction algorithm. Based on the formalization, an XML - based page structure description language, TIDL, is brought out, including the object model, the HTML object reference model and definition of tags. At the final part, a Web data gathering and querying application based on Internet agent technology, named Web Integration Services Kit (WISK) is mentioned.

The Optimization and Improvement of MapReduce in Web Data Mining

Extracting and mining social networks information from massive Web data is of both theoretical and practical significance. However, one of definite features of this task was a large scale data processing, which remained to be a great challenge that would be addressed. MapReduce is a kind of distributed programming model. Just through the implementation of map and reduce those two functions, the distributed tasks can work well. Nevertheless, this model does not directly support heterogeneous datasets processing, while heterogeneous datasets are common in Web. This article proposes a new framework which improves original MapReduce framework into a new one called Map-Reduce-Merge. It adds merge phase that can efficiently solve the problems of heterogeneous data processing. At the same time, some works of optimization and improvement are done based on the features of Web data.

基于Web自动化理念实现跨平台数据交互

在异构平台的时代,不同平台之间的数据分享和交换对于无缝的工作流和信息整合变得至关重要。然而,缺乏互操作性和兼容性经常妨碍了数据有效交换。本文提出了一种基于Web自动化概念的跨平台数据交互方法。该方法利用自动化脚本和浏览器自动化工具来模拟人类与Web应用程序的交互,实现自动数据检索、转换和传输,能够实现跨平台的无缝自动化数据交换,取代手工操作并减少人为错误。该方法在数据交换方面提高了效率、准确性和可靠性,增强了总体生产力。此外,该方法具有灵活性和可扩展性,以适应不同的数据交换要求,并支持实时数据同步。

基于Web方式下飞机飞行实测数据可视化方法研究

本文主要基于Web方式实现飞机飞行情况的可视化,分析、研究并实时监测飞机飞行状态。使用Google Earth API以及计算机语言,对飞机航行轨迹进行仿真设计,实现飞机飞行数据的实时采集、处理与加工,使用可视化建模手段,实现飞机飞行数据的可视化,使相关工作人员可以快速、灵活、客观地掌握各批次飞机的飞行航迹,有利于提升工作效率,保障运行安全,保障民航业安全。同时,在使用过程中,可以根据反馈效果动态调整优化功能,使系统更贴合实际,推动相关工作的有效开展。

基于云技术和大数据的Web服务优化策略

文中旨在探讨基于云技术和大数据的Web服务优化策略,通过综合利用云计算和大数据技术,优化Web服务的性能、安全性和资源管理策略。首先,介绍了云技术和大数据的基本概念,分析了大数据对Web服务优化的重要作用。然后,阐述了研究方法,包括数据收集和分析方法、实验设计和研究方法,并阐明了文章的理论框架和概念模型。其次,分析了云技术整合大数据驱动的Web服务优化面临的现实困境,包括数据隐私与安全、效率与性能、成本与资源管理等。最后,提出了针对上述问题的一系列策略,包括数据隐私保护与安全策略、效率优化与性能提升策略、成本管理与资源优化策略。通过该研究,可为基于云技术和大数据驱动的Web服务优化提供重要的理论和实践指导。

Classification of Big Data Security Based on Ontology Web Language

A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.

基于XML技术的Web数据库应用系统设计

随着互联网的普及与发展,数据库技术已成为支撑各类应用系统的核心。然而,传统的关系型数据库在跨平台的数据交换、异构系统间的信息共享等应用场景中存在诸多局限。在这一背景下,XML技术应运而生,其作为一种数据描述和交换的标准,为Web数据库应用系统的设计提供了新的思路和解决方案。文中介绍了XML技术的特点、XML结合Web数据库的优势,分析了基于XML技术的Web数据库应用系统的设计与实现,以期为数据的存储、查询和管理提供参考。

A Machine Learning-Based Web Application for Heart Disease Prediction

This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.

基于Web of Science的2013-2022年近视研究领域文献计量分析

目的应用文献计量学方法分析近10年来近视研究领域的现状、热点和未来的发展方向。方法检索Web of Science核心数据库中2013年1月1日至2022年12月31日近视相关的研究类和综述类文献,使用VOSviewer软件对国家、研究机构、作者进行共现分析,使用CiteSpace软件对关键词和共被引参考文献进行聚类分析。结果最终纳入9745篇文献,涉及123个国家或地区,7150个机构和29343位作者。通过分析发现全球在近视领域的发文量整体呈增长趋势,中国是发文量最多的国家,来自美国的研究总被引用次数最多。关键词分析结果表明,早期近视研究热点主要集中于屈光手术、并发症的诊断与治疗、遗传学研究以及流行病学特征,而近年来研究重点已迅速转向近视的预防和控制。共被引文献聚类分析结果显示,近视领域包含多个聚类模块,如#0学龄儿童、#1小切口角膜基质透镜取出术、#2近视控制、#3屈光不正、#4接触镜等研究方向。研究前沿主要聚焦于近视管理技术、近视与视网膜和脉络膜血管、人工智能在近视领域的应用等方面。结论近十年近视研究领域涵盖眼科学、分子生物学、遗传学、眼视光学、流行病学等多个学科领域。未来需要进一步探索近视的病因和发病机制、早期识别和筛查、管理技术、人工智能辅助诊断等,以制定更加有效、安全的近视防控策略。

基于Web挖掘技术的图书馆服务推荐算法分析

为实现精确化、实时化的图书馆图书信息推荐,保证图书馆的服务质量和效率,文中应用Web挖掘技术,设计了一种新型、先进的图书馆服务推荐算法。首先,系统论述了Web挖掘技术的概念、分类及流程。其次,从离线部分、在线部分两个方面入手,实现了图书馆服务推荐系统的框架设计。最后,在参照图书馆服务推荐系统框架的基础上,从建立推荐池、产生初步推荐集、融合离线部分产生个性化推荐集等方面入手,实现了对图书馆服务推荐算法的设计,为相关工作提供了有效的参考。

基于DataPool的Web测试数据生成与维护方法

针对Web应用测试数据所具有的特点,本文提出了一种基于DataPool的Web应用测试数据生成与维护方法。在形式化定义DataPool和明确其语义描述的基础上,根据浏览器端不同的输入域类型在DataPool的编辑视图中提供了相对应的测试数据生成方式,并提供了各种维护功能。在DataPool浏览视图中,支持以单个和批量的方式选择测试数据。

Intelligent and Adaptive Web Data Extraction System Using Convolutional and Long Short-Term Memory Deep Learning Networks

Data are crucial to the growth of e-commerce in today's world of highly demanding hyper-personalized consumer experiences,which are collected using advanced web scraping technologies.However,core data extraction engines fail because they cannot adapt to the dynamic changes in website content.This study investigates an intelligent and adaptive web data extraction system with convolutional and Long Short-Term Memory(LSTM)networks to enable automated web page detection using the You only look once(Yolo)algorithm and Tesseract LSTM to extract product details,which are detected as images from web pages.This state-of-the-art system does not need a core data extraction engine,and thus can adapt to dynamic changes in website layout.Experiments conducted on real-world retail cases demonstrate an image detection(precision)and character extraction accuracy(precision)of 97%and 99%,respectively.In addition,a mean average precision of 74%,with an input dataset of 45 objects or images,is obtained.