大数据背景下的数据挖掘课程教学新思考

当今社会已经步入大数据时代,数据挖掘已经成为商业、医疗、制造业和政务管理等应用领域的重要技术,具有十分重要的社会价值。数据挖掘课程综合了多门学科知识,其教学设计和教学方式直接影响到教学效果和人才培养的质量。针对大数据的特点,以构建课程核心知识体系为主题,采用案例教学法,改革传统的教学评价方式,理论结合实践进行了研究生数据挖掘课程教学创新尝试,其教学达到了预期效果,受到学生好评。     

Understanding the human context in requirements elicitation

The human context within which a software system will operate is fundamental for its requirements. It may not appear to be very much related to the system, but it is very relevant in achieving its successful adoption. However, requirements engineers have usually a background in Software Engineering and are not trained to elicit this kind of information. This situation raises the need for analytical tools to deal with these features. These tools should enable collaborative work between requirements engineers, who use them in development, social practitioners, who provide the knowledge and processes underlying these tools, and the customers, who know the domain and intended application of the projects. The framework presented in this paper is based on the socio-psychological Activity Theory and its analysis of human contexts. It includes a repository of social properties and a process to perform this elicitation using it. The paper illustrates its application through a case study on the impact of a new system in the organization of a firm.     

大数据时代下计算机软件技术的应用探析

近年来,随着计算机互联网信息技术的蓬勃发展,我国已经进入大数据时代。在此背景之下,计算机软件技术已被广泛应用于各大领域和产业中。文章首先介绍了大数据时代计算机软件技术的发展现状,重点解析了现代计算机技术中几种常见的计算机软件技术类型,剖析了大数据时代计算机软件技术的实际应用价值,并探讨了大数据时代计算机软件关键技术的应用,旨在促进当代计算机软件技术更好地为人类社会和企业服务。     

新时代背景下数据科学与大数据专业人才培养的若干思考

聚焦新时代复合型数据科学与大数据专业人才的培养,提出要围绕育人目标制定人才培养方案,并不断优化;要坚持"引进来,走出去"原则建设师资队伍;要坚持高标准、宽视野的要求进行课程建设;要调用校内外一切资源建设实验室.文章提出要从系统论、控制理论的视角构建数据科学与大数据专业人才培养协同系统.     

大数据时代下计算机软件技术的应用探析

近年来,随着计算机互联网信息技术的蓬勃发展,我国已经进入大数据时代。在此背景之下,计算机软件技术已被广泛应用于各大领域和产业中。文章首先介绍了大数据时代计算机软件技术的发展现状,重点解析了现代计算机技术中几种常见的计算机软件技术类型,剖析了大数据时代计算机软件技术的实际应用价值,并探讨了大数据时代计算机软件关键技术的应用,旨在促进当代计算机软件技术更好地为人类社会和企业服务。     

大数据时代下计算机信息处理技术分析

随着社会经济高速发展﹐各个行业产生了大量的数据信息﹐尤其是在计算机技术的广泛应用下,需要做好信息处理,从中挖掘有用的信息。在大数据时代背景下,合理应用计算机信息处理技术、创设相应情景,可以提高计算机信息处理水平。鉴于此,文章以大数据时代为着手点,分析了常用的计算机信息处理技术,希望切实发挥大数据提升计算机技术水平的作用。     

新时代背景下数据科学与大数据专业人才培养的若干思考

聚焦新时代复合型数据科学与大数据专业人才的培养,提出要围绕育人目标制定人才培养方案,并不断优化;要坚持"引进来,走出去"原则建设师资队伍;要坚持高标准、宽视野的要求进行课程建设;要调用校内外一切资源建设实验室.文章提出要从系统论、控制理论的视角构建数据科学与大数据专业人才培养协同系统.     

面向大数据的内存数据管理研究现状与展望

对面向大数据的内存数据管理技术的相关研究进行综述。梳理大数据环境下数据管理技术发展的脉络和格局的变化;分析新环境下的内存数据管理技术面临的发展机遇与研究挑战;介绍相关的前沿研究,其中包括分布式编程模型、混合存储体系结构、内存数据管理等;给出技术和管理上的发展展望。     

Systematizing requirements elicitation technique selection

ContextThis research deals with requirements elicitation technique selection for software product requirements and the overselection of open interviews.ObjectivesThis paper proposes and validates a framework to help requirements engineers select the most adequate elicitation techniques at any time.MethodWe have explored both the existing underlying theory and the results of empirical research to build the framework. Based on this, we have deduced and put together justified proposals about the framework components. We have also had to add information not found in theoretical or empirical sources. In these cases, we drew on our own experience and expertise.ResultsA new validated approach for requirements technique selection. This new approach selects techniques other than open interview, offers a wider range of possible techniques and captures more requirements information.ConclusionsThe framework is easily extensible and changeable. Whenever any theoretical or empirical evidence for an attribute, technique or adequacy value is unearthed, the information can be easily added to the framework.     

大数据时代数据挖掘与分析课程教学改革探究

从理论知识、编程实践、工程实战三个方面对数据挖掘与分析课程进行教学改革.课程采用Python编程语言,围绕贵州省经济文化特色及大数据发展战略,充分运用大数据、数据挖掘、知识图谱等新兴技术来分析贵州省各领域现状,挖掘其内在价值.近三年的教学实践证明了该教学改革的创新性和有效性,培养了学生的大数据思维,为贵州省聚焦大数据战...     

大数据时代下计算机网络安全及防范措施

经过调查显示,2021年我国互联网的普及率达到70.4%。互联网已经充分渗入我国国民的日常生产以及生活中,我国已经进入大数据时代。随着互联网与日常生活、生产的交融,人们对于计算机网络安全问题也越来越重视。对于网络安全,如何进行正确的防范以及在面临问题时采取正确的解决措施,成为计算机技术日益发展,成熟所要面对的问题。文章主要研究了计算机网络安全面临的一些问题,并就这些问题提出计算机网络安全防范措施。     

大数据时代网络安全及预测技术研究

大数据时代信息技术的快速发展,依托于各类硬件防护设备的网络体系架构的异构数据量每天以指数级的量级递增,基于传统的网络安全防护技术无法有效的适用于具有海量数据的特征网络安全和分析预测等工作,因此海量数据的保存、使用、以及分析等信息挖掘和数据分析预测逐步成为社会各界重视和当前的研究趋势。本文以海量的异构数据为研究对象,识别网络安全大数据的典型特征,结合情报预测的主要方法,创新性的提出了大数据特征下的网络安全预测分析技术,提高网络安全风险识别和预测、俞静能力,有效的改善网络防护效果。     

The state of the art in automated requirements elicitation

ContextIn large software development projects a huge number of unstructured text documents from various stakeholders becomes available and needs to be analyzed and transformed into structured requirements. This elicitation process is known to be time-consuming and error-prone when performed manually by a requirements engineer. Consequently, substantial research has been done to automate the process through a plethora of tools and technologies.ObjectiveThis paper aims to capture the current state of automated requirements elicitation and derive future research directions by identifying gaps in the existing body of knowledge and through relating existing works to each other. More specifically, we are investigating the following research question: What is the state of the art in research covering tool support for automated requirements elicitation from natural language documents?MethodA systematic review of the literature in automated requirements elicitation is performed. Identified works are categorized using an analysis framework comprising tool categories, technological concepts and evaluation approaches. Furthermore, the identified papers are related to each other through citation analysis to trace the development of the research field.ResultsWe identified, categorized and related 36 relevant publications. Summarizing the observations we made, we propose future research to (1) investigate alternative elicitation paradigms going beyond a pure automation approach (2) compare the effects of different types of knowledge on elicitation results (3) apply comparative evaluation methods and multi-dimensional evaluation measures and (4) strive for a closer integration of research activities across the sub-fields of automatic requirements elicitation.ConclusionThrough the results of our paper, we intend to contribute to the Requirements Engineering body of knowledge by (1) conceptualizing an analysis framework for works in the area of automated requirements elicitation, going beyond former classifications (2) providing an extensive overview and categorization of existing works in this area (3) formulating concise directions for future research.     

DocFlow: workflow based requirements elicitation

Use cases are the favoured technique for defining the functional requirements of a software system, but their use implies that the desired functionality of the new system is well known. The aim of this work is to present an alternative technique—and a supporting tool—to accurately define this functionality, expressed as use cases, starting from the workflows that describe the end user work. The use of hypergraphs in the proposed algorithm of transformation reinforces the generation process. In addition, the technique is independent of the development paradigm and a variation in the algorithm allows obtaining Data Flow Diagrams.     

大数据时代用户信息隐私问题对策研究

在大数据时代,如何解决用户隐私问题已经成为近年来大数据领域学者研究的热点问题之一.文章提出从大数据收集、大数据存储和大数据应用三个阶段进行综合治理,各方联动以共同应对用户信息隐私问题,并针对具体阶段制定相应的对策.     

A novel data-driven graph-based requirement elicitation framework in the smart product-service system context

Nowadays, industrial companies are facing ever-increasing challenges to generate new value-in-use and maintain their high competitiveness in the market. With the rapid development of Information and Communication Technology (ICT), IT is embedded in the products themselves, i.e. smart, connected products (SCPs) to generate values. Hence, an emerging value proposition paradigm, smart product-service system (Smart PSS) was introduced, by leveraging both SCPs and its generated services as a solution bundle to meet individual customer needs. Unlike other types of PSS, in Smart PSS, massive user-generated data and product-sensed data are collected during the usage phase, where potential requirements can be elicited readily in a value co-creation manner with context-awareness. Nevertheless, only few scholars discuss any systematic manner to support requirement elicitation in such context. To fill the gaps, this research proposes a novel data-driven graph-based requirement elicitation framework in the Smart PSS, so as to assist engineering/designers make better design improvement or new design concept generation in a closed-loop manner. It underlines the informatics-based approach by integrating heterogeneous data sources into a holistic consideration. Moreover, by leveraging graph-based approach, context-product-service information can be linked by the edges and nodes in-between them to derive reliable requirements. To validate its feasibility and advantages, an illustrative example of smart bicycle design improvement is further adopted. As an explorative study, it is hoped that this work provides useful insights for the requirement elicitation process in today’s smart connected environment.     

SDLER: stacked dedupe learning for entity resolution in big data era

The Journal of Supercomputing - In the Big Data Era, Entity Resolution (ER) faces many challenges such as high scalability, the coexistence of complex similarity metrics, tautonymy and synonym, and...     

大数据时代MOOC对计算机专业教育的影响

信息社会产生的海量数据让人类社会迈入大数据时代,MOOC则是大数据时代在教育领域引发的信息化革命新浪潮。从分析大数据的特征入手,剖析了MOOC对教育和教学的深刻影响。从计算机专业的理论和实践教学出发,阐述了MOOC给计算机专业教育带来的变化和影响。以期能对广大计算机专业教育工作者有所启迪。     

大数据时代学员课程学习成败预测研究

为了顺应教育"大数据"时代,设计了学员课程学习大数据体系架构,给出了基于该架构的学员课程学习成败预测模型,并进行了基于大数据的课程学习成败因素分析。通过挖掘学员大数据的价值,为学员课程学习给以提醒和建议,为教员提供了学员全面的学习情况和全过程反馈,从而不断提高教学效果。