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.     

新工科视域下农业院校大数据专业人才培养模式研究

国内数据科学与大数据技术专业是“新工科”专业之一。现阶段,大数据专业人才缺乏,因此探索大数据专业人才培养模式已然成为高校建设的研究重点。本文研究了新工科视域下农业院校大数据专业建设方法,采用校企合作的方式搭建大数据实践平台,以大幅提升学生的实验环境。结合农业院校的特点提出校企协同育人、科研项目融入教学和学科竞赛相结合的大数据专业创新人才培养模式。     

煤炭大数据研究及发展方向

介绍了煤炭大数据的来源及特征,分析了当前煤炭大数据应用现状;阐述了煤炭大数据平台化发展对于煤炭大数据应用的意义,分析了煤炭大数据平台建设过程中在数据共享、数据质量、安全性、专业人才等方面存在的问题,提出建设煤炭大数据平台应进行顶层设计、完善煤炭数据标准体系、培养专业人才、合理设计平台框架。     

大数据本科专业申报及认识

从产业发展、数据科学的学科特征、大数据专业与其他相关专业的不同等3个方面,阐述增设大数据本科专业的合理性和必要性;以对外经济贸易大学专业建设为例,指出大数据专业人才应该能够围绕互联网平台上经济金融数据的商业价值进行挖掘并揭示数据间关系。     

以就业为导向的大数据技术及应用专业人才培养研究

目前,大数据技术及应用专业人才培养方案中对于学生就业能力培养环节较为薄弱,无法缓解学生的就业压力.因此,设计以就业为导向的大数据技术及应用专业人才培养方案.通过树立大数据技术及应用专业人才培养观念,优化完善大数据技术及应用人才课程,壮大专业人才培养师资力量以及构建"校企合作"机制等几方面,完成以就业为导向的大数据技术及...     

基于云计算的大数据项目实训室创新设计研究

在贯彻落实教育部要求在职业院校启动"1+X"证书试点工作的大背景下,高职院校建设大数据项目实训室对满足相关专业人才培养需求、拓展专业教师科研平台、服务校园和地方行业应用等方面显得尤为重要.就当前高职院校大数据项目实训室建设中面临的建设困境提出了解决对策,设计了基于云计算技术符合职业院校实训室建设现状和发展需求的创新方案.     

应用型高校数据科学与大数据技术人才培养体系探究

随着整个社会对大数据人才需求的井喷，如何培养出契合社会需求的数据科学与大数据技术专业人才成为高校急需解决的问题。文章通过分析大数据技术专业人才培养当前面临的主要问题，结合大数据专业的特点，从多个维度对普通应用型本科院校大数据人才培养体系进行了探究，并指出作为普通应用型本科院校，应结合自身的专业特色，形成以应用为主导的特色大数据人才培养体系。     

高校大数据实验室及实验体系的规划与建设

大数据产业已上升至国家战略,建立大数据实验室及实验课程体系是培养大数据技术人才的必要条件.本文对大数据的知识体系进行了梳理,分析了"数据科学与大数据技术"专业和"大数据技术与应用"专业的培养目标及职业定位,明确了大数据专业的学生应该掌握的关键知识和需要重点培养的专业技能,介绍了主流的大数据生态系统,选取了最通用的大数据架构,提出了在单机环境、单机虚拟化环境、共享大数据集群环境、云计算环境下建设大数据实验室的不同方案,并设计了大数据实验课程体系及实验项目.     

新工科背景下大数据专业建设及人才培养模式探索

在新工科背景下,国内数据科学与大数据技术专业设立不久,现阶段社会发展过程中大数据专业人才缺乏,因此探索大数据专业建设及人才培养模式已然成为高校建设的重点。本文研究了新工科背景下农业院校大数据专业建设方法,同时结合农业院校的特点提出“三个要素、二个协同育人、二个培养载体”大数据专业创新人才培养模式。     

大数据时代背景下信息与计算科学专业多学科交叉融合人才培养模式探索

大数据时代对信息与计算科学专业带来新的发展契机,但同时,大数据分析处理中的多学科交叉融合,使专业人才培养面临更多前所未有的挑战。为此,通过对大数据背景下,信息与计算科学专业人才培养中存在问题的分析,提出从稳固专业思想、构建课程体系、改革教学方法、搭建实践平台,以及提高师资队伍水平等五方面,对现有的专业人才培养模式进行完善和提升,这对于探索多学科交叉融合人才培养模式,培养高素质的应用复合型人才具有重要的借鉴作用。     

Linking building data in the cloud: Integrating cross-domain building data using linked data

Within the operational phase buildings are now producing more data than ever before, from energy usage, utility information, occupancy patterns, weather data, etc. In order to manage a building holistically it is important to use knowledge from across these information sources. However, many barriers exist to their interoperability and there is little interaction between these islands of information.As part of moving building data to the cloud there is a critical need to reflect on the design of cloud-based data services and how they are designed from an interoperability perspective. If new cloud data services are designed in the same manner as traditional building management systems they will suffer from the data interoperability problems.Linked data technology leverages the existing open protocols and W3C standards of the Web architecture for sharing structured data on the web. In this paper we propose the use of linked data as an enabling technology for cloud-based building data services. The objective of linking building data in the cloud is to create an integrated well-connected graph of relevant information for managing a building. This paper describes the fundamentals of the approach and demonstrates the concept within a Small Medium sized Enterprise (SME) with an owner-occupied office building.     

Improving data accuracy: Selecting the best data checking technique

Data entry errors can have catastrophic effects on the results of a statistical analysis. Therefore, researchers often spend considerable effort checking their data. This paper compared the effectiveness of three data checking methods – double entry, read aloud, and visual checking – using the types of data and data entry personnel that are typically used in psychological research. To compare these techniques, we created 20 data sheets and entered them into the computer. Next, we deliberately introduced errors into this data set. Participants’ job was to locate and correct these errors.     

Identifying mislabeled training data with the aid of unlabeled data

This paper presents a new approach for identifying and eliminating mislabeled training instances for supervised learning algorithms. The novelty of this approach lies in the using of unlabeled instances to aid the detection of mislabeled training instances. This is in contrast with existing methods which rely upon only the labeled training instances. Our approach is straightforward and can be applied to many existing noise detection methods with only marginal modifications on them as required. To assess the benefit of our approach, we choose two popular noise detection methods: majority filtering (MF) and consensus filtering (CF). MFAUD/CFAUD is the new proposed variant of MF/CF which relies on our approach and denotes majority/consensus filtering with the aid of unlabeled data. Empirical study validates the superiority of our approach and shows that MFAUD and CFAUD can significantly improve the performances of MF and CF under different noise ratios and labeled ratios. In addition, the improvement is more remarkable when the noise ratio is greater.     

Scientific data integration system in the linked open data space

The paper is concerned with a project of data integration system designed for work in the Linked Open Data space. A system architecture is suggested, and basic principles of its functioning are discussed. For data integration, it is proposed to use a combined approach based on Linked Data sets. The system can be applied for integration of data from numerous autonomous sources, between which sufficiently stable links may be identified.     

Safeguarding the data warehouse

Data warehousing and the Internet are regarded as the two most important changes to the information systems landscape in the past few years. Although they are agents of powerful change, their deployment presents many challenges and opportunities. Here is outlined one set of challenges that has emerged with the convergence of the Internet and data warehousing security.     

Abstract data types for the logical modeling of complex data

In this paper we propose a logical data model for complex data. Our proposal extends the relational model by using abstract data types for domains specification and an extended relational algebra is also introduced. The introduction of the parameterized type Geometry(S), where S is a ground set of elements, allows the representation of complex aggregated data. As an example, we discuss how our model supports the definition of geographical DBMSs. Moreover, to show the generality of our approach, we sketch how the model can be used in the framework of statistical applications.     

关系数据模型与面向对象数据模型的关系探讨

面向对象数据模型的应用打破了油田单一关系数据库体系架构。如何在面向对象数据模型和关系数据模型并存条件下构建和优化协调统一的数据库体系架构,是油田数据库建设的重要技术方向之一。本文从油田数据库应用的角度概要对比了关系数据模型和面向对象数据模型的特点,对关系数据模型和面向对象数据模型共存条件下建立油田数据库体系架构的可能性进行了探讨。     

Storing and analysing voice of the market data in the corporate data warehouse

Web opinion feeds have become one of the most popular information sources users consult before buying products or contracting services. Negative opinions about a product can have a high impact in its sales figures. As a consequence, companies are more and more concerned about how to integrate opinion data in their business intelligence models so that they can predict sales figures or define new strategic goals. After analysing the requirements of this new application, this paper proposes a multidimensional data model to integrate sentiment data extracted from opinion posts in a traditional corporate data warehouse. Then, a new sentiment data extraction method that applies semantic annotation as a means to facilitate the integration of both types of data is presented. In this method, Wikipedia is used as the main knowledge resource, together with some well-known lexicons of opinion words and other corporate data and metadata stores describing the company products like, for example, technical specifications and user manuals. The resulting information system allows users to perform new analysis tasks by using the traditional OLAP-based data warehouse operators. We have developed a case study over a set of real opinions about digital devices which are offered by a wholesale dealer. Over this case study, the quality of the extracted sentiment data is evaluated, and some query examples that illustrate the potential uses of the integrated model are provided.