以OBE为导向的信管专业Python课程实践教学改革

为了推进信管专业Python课程实践教学的工程教育改革,提升学生的大数据实践能力,将OBE理论基础融入到对Python实践教学改革中.分析传统教学模式下Python课程实践教学的现状,设计了三重实践模式的教学改革,改进了实践形式、实践内容、实践评价.通过以OBE为导向的Python课程实践教学改革,课程的教学效果得到提高.     

基于MOOC嵌入式教学的数据挖掘教学改革初探

针对大数据、网络教学等新形势下数据挖掘课程的教学现状,提出通过借鉴国外知名大学软件人才培养的模式和理念,从课程教学和项目实践两方面进行改革,以课堂教学、MOOC嵌入式课程和项目实践相结合的方式,推进数据挖掘课程在大数据环境下的教学和实践,为培养国际一流的软件人才服务。     

基于AI的劳动教育课程实践评测研究

利用微信小程序搭建劳动教育课程评测平台,对劳动教育课程实践活动情况进行评价.评价分为基于数据挖掘的活动前评价、基于AI的活动中践行评价、基于数据分析的活动后评价三个阶段.通过大数据对劳动教育课程师生的反馈和评价进行分析,适时地对劳动教育活动内容和教学设计进行调整和优化,以使劳动教育课程发挥其应有的作用.     

大数据时代下的“商务智能与数据挖掘”课程教学改革研究

文章介绍在"商务智能与数据挖掘"课程教学内容中如何反映大数据时代的基本特征,在教学过程中如何引导学生对具有大数据特征的课程项目进行实践并开发相应智能教学工具,同时还讨论如何将数据挖掘与数据库和法律课程在大数据背景下进行联动教学。     

大数据分析与应用课程体系构建

针对高校大数据方向教学与实践课程体系构建,分析国内外教学现状,提出以大数据分析与应用课程为核心,延伸大数据分析在数据挖掘、人工智能等领域应用的课程体系,兼顾大数据技术实践任务与科研项目驱动教学模式,探讨大数据分析与应用课程体系的构建与实施。     

大数据时代计算机网络技术课程教学改革的实践与探索

随着我国经济建设的迅速发展,科学技术也在不断地进步,互联网技术也在飞速地发展,信息技术已经渗透到各行各业,大数据已经影响着不同行业,渗透到人们的日常生活。数据存储、数据计算、数据挖掘、云计算技术等互联网行业的技术改革和创新为大数据的发展和应用奠定了基础。随着大数据应用越来越广泛,对大数据相关的人才需求也不断加大。大数据的发展离不开计算机网络,计算机网络技术是大数据相关人才必须掌握的技术之一,计算机网络技术课程是大数据专业的必修课程之一。因此,对大数据时代计算机网络技术课程教学改革的实践和探索是非常必要的。     

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。     

新工科背景下基于项目引导和线上线下联动的数据挖掘课程教学改革

随着时代的发展与技术的进步,人们生产、收集、存储数据的能力越来越强,数据已经成为人们生产生活中必不可少的关键因素.而数据挖掘技术使得从海量的高维数据中挖掘出有用信息成为可能.目前越来越多的高校开设了数据挖掘课程.依据数据挖掘课程的自身特点和高校学生普遍存在的知识基础不牢、实践能力缺失等问题,本文依据新工科建设教育规范提...     

基于新工科教育理念《数据结构与算法分析》课程教学改革与实践

在大数据背景下,基于新工科教育理念对应用型本科计算机专业《数据结构与算法分析》课程教学改革进行了探索与实践,分析了课程定位、教学现状、对改革的内容和目标、具体措施进行了研究、探讨和实践。     

大数据智能分析与数据挖掘研究

大数据智能分析与数据挖掘是从海量数据中提取更加本质和更加有用的规律性信息的重要手段,是挖掘智能和有价值信息的重要抓手.通过运用文献研究法和系统法,对大数据智能分析与大数据挖掘进行了阐述,给出大数据智能分析涉及到的关键技术,对其关键技术进行了阐述,提出大数据挖掘方法、类型、工具和流程及应用,并阐明大数据挖掘中使用的关键技术,希望能为大数据智能分析以及大数据挖掘的研究者提供借鉴.     

One-to-many data transformations through data mappers

The optimization capabilities of RDBMSs make them attractive for executing data transformations. However, despite the fact that many useful data transformations can be expressed as relational queries, an important class of data transformations that produce several output tuples for a single input tuple cannot be expressed in that way.

To overcome this limitation, we propose to extend Relational Algebra with a new operator named data mapper. In this paper, we formalize the data mapper operator and investigate some of its properties. We then propose a set of algebraic rewriting rules that enable the logical optimization of expressions with mappers and prove their correctness. Finally, we experimentally study the proposed optimizations and identify the key factors that influence the optimization gains.     

Efficient data reduction in multimedia data

As the amount of multimedia data is increasing day-by-day thanks to cheaper storage devices and increasing number of information sources, the machine learning algorithms are faced with large-sized datasets. When original data is huge in size small sample sizes are preferred for various applications. This is typically the case for multimedia applications. But using a simple random sample may not obtain satisfactory results because such a sample may not adequately represent the entire data set due to random fluctuations in the sampling process. The difficulty is particularly apparent when small sample sizes are needed. Fortunately the use of a good sampling set for training can improve the final results significantly. In KDD’03 we proposed EASE that outputs a sample based on its ‘closeness’ to the original sample. Reported results show that EASE outperforms simple random sampling (SRS). In this paper we propose EASIER that extends EASE in two ways. (1) EASE is a halving algorithm, i.e., to achieve the required sample ratio it starts from a suitable initial large sample and iteratively halves. EASIER, on the other hand, does away with the repeated halving by directly obtaining the required sample ratio in one iteration. (2) EASE was shown to work on IBM QUEST dataset which is a categorical count data set. EASIER, in addition, is shown to work on continuous data of images and audio features. We have successfully applied EASIER to image classification and audio event identification applications. Experimental results show that EASIER outperforms SRS significantly. Surong Wang received the B.E. and M.E. degree from the School of Information Engineering, University of Science and Technology Beijing, China, in 1999 and 2002 respectively. She is currently studying toward for the Ph.D. degree at the School of Computer Engineering, Nanyang Technological University, Singapore. Her research interests include multimedia data processing, image processing and content-based image retrieval. Manoranjan Dash obtained Ph.D. and M. Sc. (Computer Science) degrees from School of Computing, National University of Singapore. He has worked in academic and research institutes extensively and has published more than 30 research papers (mostly refereed) in various reputable machine learning and data mining journals, conference proceedings, and books. His research interests include machine learning and data mining, and their applications in bioinformatics, image processing, and GPU programming. Before joining School of Computer Engineering (SCE), Nanyang Technological University, Singapore, as Assistant Professor, he worked as a postdoctoral fellow in Northwestern University. He is a member of IEEE and ACM. He has served as program committee member of many conferences and he is in the editorial board of “International journal of Theoretical and Applied Computer Science.” Liang-Tien Chia received the B.S. and Ph.D. degrees from Loughborough University, in 1990 and 1994, respectively. He is an Associate Professor in the School of Computer Engineering, Nanyang Technological University, Singapore. He has recently been appointed as Head, Division of Computer Communications and he also holds the position of Director, Centre for Multimedia and Network Technology. His research interests include image/video processing & coding, multimodal data fusion, multimedia adaptation/transmission and multimedia over the Semantic Web. He has published over 80 research papers.     

大数据环境下的数据安全性探讨

随着互联网的高速发展,特别是近年来云计算、物联网等新兴技术的出现,社交网络等服务的广泛应用,人类社会的数据的规模正快速地增长,大数据时代已经到来。如何获取,分析大数据已经成为广泛的问题。但随着带来的数据的安全性必须引起高度重视。本文从大数据的概念和特征说起,阐述大数据面临的安全挑战,并提出大数据的安全应对策略。     

Fuzzy data mining for time-series data

Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation.     

Compression-based data mining of sequential data

The vast majority of data mining algorithms require the setting of many input parameters. The dangers of working with parameter-laden algorithms are twofold. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process. Data mining algorithms should have as few parameters as possible. A parameter-light algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics, learning, and computational theory hold great promise for a parameter-light data-mining paradigm. The results are strongly connected to Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen lines of code. We will show that this approach is competitive or superior to many of the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/XML/video datasets. As a further evidence of the advantages of our method, we will demonstrate its effectiveness to solve a real world classification problem in recommending printing services and products. Responsible editor: Johannes Gehrke     

Scattered data interpolation of Radon data

Linear combinations of translates of a given basis function have long been successfully used to solve scattered data interpolation and approximation problems. We demonstrate how the classical basis function approach can be transferred to the projective space ℙ ^d−1. To be precise, we use concepts from harmonic analysis to identify positive definite and strictly positive definite zonal functions on ℙ ^d−1. These can then be applied to solve problems arising in tomography since the data given there consists of integrals over lines. Here, enhancing known reconstruction techniques with the use of a scattered data interpolant in the “space of lines”, naturally leads to reconstruction algorithms well suited to limited angle and limited range tomography. In the medical setting algorithms for such incomplete data problems are desirable as using them can limit radiation dosage.     

数据竞争的规制：数据的利用与数据保护的协调

自互联网出现以来,数据保护一直是个难题。当社交媒体网站在数字市场上大展拳脚的那一刻,对用户数据和信息的保护让决策者们不得不保持警惕。在数字经济时代的背景下,数据逐渐成为企业提升竞争力的重要要素,围绕着数据展开的市场竞争越来越多。数字经济时代,企业对数据资源的重视与争夺,将网络平台权利与用户个人信息保护、互联网企业之间有关数据不正当竞争的纠纷和冲突,推上了风口浪尖。因此,如何协调和把握数据的合理利用和保护之间的关系,规制不正当竞争行为,以求在数字经济快速发展的洪流中,占据竞争优势显得尤为重要。文章将通过分析数据的二元性,讨论数据在数字经济时代的价值,并结合反不正当竞争法和实践案例,进一步讨论数据利用和保护的关系。     

Test data regeneration: generating new test data from existing test data

Existing automated test data generation techniques tend to start from scratch, implicitly assuming that no pre‐existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre‐existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search‐based test data regeneration, a technique that can generate additional test data from existing test data using a meta‐heuristic search algorithm. The proposed technique is compared to a widely studied test data generation approach in terms of both efficiency and effectiveness. The empirical evaluation shows that test data regeneration can be up to 2 orders of magnitude more efficient than existing test data generation techniques, while achieving comparable effectiveness in terms of structural coverage and mutation score. Copyright © 2010 John Wiley & Sons, Ltd.