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| 1 | 欧特克携手Dodge Data & Analytics发布《中国BIM应用价值研究报告》显示文摘4月27日,全球二维和三维设计、工程及娱乐软件的领导者欧特克有限公司("欧特克"或"Autodesk")与Dodge Data&Analytics在上海国金中心的利思卡尔顿酒店共同发布了最新的《中国BIM应用价值研究报告》。欧特克与Dodge Data&Analytics(DD&A)的高层管理人员出席了活动并发表了演讲,与参会者分享了BIM技术在中国市场的最新应用和发展趋势。同时,他们还与媒体朋友共同探讨如何深化BIM应用在中国的普及, | 宁忠意 | 2015 | 中外建筑2015，，6： | 6 |
| 2 | Considering Monopoly Maintenance Cost for an Automobile Purchase in China: A DEA-Based Approach显示文摘With the fast growing economy,China has become the biggest automobile market in the world.Many Chinese families buy automobiles to promote individual wellbeing and facilitate the convenience of lives.Automobile purchase has become a general and important decision for a Chinese family.However,it is always difficult to make a decision on the automobile purchase that balance the automobiles performance and its cost.Moreover,the automobile maintenance market in China is monopolistic.Thus,the maintenance cost is a significant consideration in automobile purchase decision.This paper employs the concept of data envelopment analysis(DEA)to measure cost performance of the automobile with considering the monopoly maintenance cost.The RCA(ratio of the total price of accessories of an automobile to the price of the automobile)100 index system is used to represent the maintenance cost of each automobile.The structure of the automobile maintenance market can be reflected with including the RCA 100 index system in the performance evaluation.Results of the case study of 28 automobiles in China verify the necessity of considering the maintenance cost in automobile purchase decision. | DAI Qianzhi LI Lin LEI Xiyang AN Qingxian TANG Xiao | 2019 | Journal of Systems Science & Complexity2019，32，4： | 3 |
| 3 | Big Data Analytics in Healthcare: Data-Driven Methods for Typical Treatment Pattern Mining显示文摘A huge volume of digitized clinical data is generated and accumulated rapidly since the widespread adoption of Electronic Medical Records (EMRs).These big data in healthcare hold the promise of propelling healthcare evolving from a proficiency-based art to a data-driven science,from a reactive mode to a proactive mode,from one-size-fits-all medicine to personalized medicine.This paper first discusses the research background-big data analytics in healthcare,the research framework of big data analytics in healthcare,analysis of medical process,and the literature summary of treatment pattern mining.Then the challenges for data-driven typical treatment pattern mining are highlighted,including similarity measure between treatment records,typical treatment pattern extraction,evaluation and recommendation,when considering the rich temporal and heterogeneous medical information in EMRs.Furthermore,three categories of typical treatment patterns are mined from doctor order content,duration,and sequence view respectively,which can provide a data-driven guideline to achieve the '5R' goal for rational drug use and clinical pathways. | Chonghui Guo Jingfeng Chen | 2019 | Journal of Systems Science and Systems Engineering2019，28，6： | 3 |
| 4 | SuPoolVisor:a visual analytics system for mining pool surveillance显示文摘Cryptocurrencies represented by Bitcoin have fully demonstrated their advantages and great potential in payment and monetary systems during the last decade.The mining pool,which is considered the source of Bitcoin,is the cornerstone of market stability.The surveillance of the mining pool can help regulators effectively assess the overall health of Bitcoin and issues.However,the anonymity of mining-pool miners and the difficulty of analyzing large numbers of transactions limit in-depth analysis.It is also a challenge to achieve intuitive and comprehensive monitoring of multi-source heterogeneous data.In this study,we present SuPoolVisor,an interactive visual analytics system that supports surveillance of the mining pool and de-anonymization by visual reasoning.SuPoolVisor is divided into pool level and address level.At the pool level,we use a sorted stream graph to illustrate the evolution of computing power of pools over time,and glyphs are designed in two other views to demonstrate the influence scope of the mining pool and the migration of pool members.At the address level,we use a force-directed graph and a massive sequence view to present the dynamic address network in the mining pool.Particularly,these two views,together with the Radviz view,support an iterative visual reasoning process for de-anonymization of pool members and provide interactions for cross-view analysis and identity marking.Effectiveness and usability of SuPoolVisor are demonstrated using three cases,in which we cooperate closely with experts in this field. | Jia-zhi XIA Yu-hong ZHANG Hui YE Ying WANG Guang JIANG Ying ZHAO Cong XIE Xiao-yan KUI Sheng-hui LIAO Wei-ping WANG | 2020 | Frontiers of Information Technology & Electronic Engineering2020，21，4： | 2 |
| 5 | Big spatial data for urban and environmental sustainability显示文摘Eighty percent of big data are associated with spatial information,and thus are Big Spatial Data(BSD).BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics.To fully leverage the advantages of BSD,it is integrated with conventional data(e.g.remote sensing images)and improved methods are developed.This paper introduces four case studies:(1)Detection of polycentric urban structures;(2)Evaluation of urban vibrancy;(3)Estimation of population exposure to PM2.5;and(4)Urban land-use classification via deep learning.The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD.Meanwhile,they can also improve the effectiveness of big data itself.Finally,this study makes three key recommendations for the development of BSD with regards to data fusion,data and predicting analytics,and theoretical modeling. | Bo Huang Jionghua Wang | 2020 | Geo-Spatial Information Science2020，23，2： | 1 |
| 6 | Latent Variable Regression for Supervised Modeling and Monitoring显示文摘A latent variable regression algorithm with a regularization term(r LVR) is proposed in this paper to extract latent relations between process data X and quality data Y. In rLVR,the prediction error between X and Y is minimized, which is proved to be equivalent to maximizing the projection of quality variables in the latent space. The geometric properties and model relations of rLVR are analyzed, and the geometric and theoretical relations among r LVR, partial least squares, and canonical correlation analysis are also presented. The rLVR-based monitoring framework is developed to monitor process-relevant and quality-relevant variations simultaneously. The prediction and monitoring effectiveness of rLVR algorithm is demonstrated through both numerical simulations and the Tennessee Eastman(TE) process. | Qinqin Zhu | 2020 | IEEE/CAA Journal of Automatica Sinica2020，7，3： | 1 |
| 7 | Disseminating Authorized Content via Data Analysis in Opportunistic Social Networks显示文摘Authorized content is a type of content that can be generated only by a certain Content Provider(CP).The content copies delivered to a user may bring rewards to the CP if the content is adopted by the user. The overall reward obtained by the CP depends on the user's degree of interest in the content and the user's role in disseminating the content copies. Thus, to maximize the reward, the content provider is motivated to disseminate the authorized content to the most interested users. In this paper, we study how to effectively disseminate the authorized content in Interest-centric Opportunistic Social Networks(IOSNs) such that the reward is maximized.We first derive Social Connection Pattern(SCP) data to handle the challenging opportunistic connections in IOSNs and statistically analyze the interest distribution of the users contacted or connected. The SCP is used to predict the interests of possible contactors and connectors. Then, we propose our SCP-based Dissemination(SCPD)algorithm to calculate the optimum number of content copies to disseminate when two users meet. Our dataset based simulation shows that our SCPD algorithm is effective and efficient to disseminate the authorized content in IOSNs. | Chenguang Kong Guangchun Luo Ling Tian Xiaojun Cao | 2019 | Big Data Mining and Analytics2019，2，1： | 1 |
| 8 | Reducing Carbon Emission through Container Shipment Consolidation and Optimization显示文摘Human’s impact on earth through global warming is more or less an accepted fact.Ocean freight is estimated to contribute 4-5%of global carbon emissions.Many manufacturing companies that transfer ship goods through full container loads found themselves under-utilizing the containers and resulting in higher carbon footprint per volume shipment.One of the reasons is the choice of non-ideal container sizes for their shipments.In this paper,we first provide an Integer Programming model to minimize the companies’shipping carbon footprints by selecting the ideal container sizes appropriate for their shipment volumes.Secondly,we proposed a strategy to minimize the carbon footprint by consolidating the shipments in the same country from multiple domestic locations at a port of loading by road freight,before the international sea shipment.A mixed-Integer Programming model has been developed to determine if one should ship each shipment separately or have shipments consolidated first before being shipped.Consolidation fills up the containers more efficiently that reduces the overall carbon footprint.Computational results using real-world data indicates a significant 13.4%reduction carbon emission when selecting the optimal combinations of different sizes of containers and an additional 12.1%reduction in carbon emission when shipment consolidation is applied. | Nang Laik Ma Kar Way Tan | 2019 | Journal of Traffic and Transportation Engineering2019，7，3： | 0 |
| 9 | A survey of analytical methods for inclusion in a new energy-water nexus knowledge discovery framework显示文摘The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stresses are evaluated for future infra-structure planning.Changes in water and energy demand are related to changes in regional temperature,and precipitation extremes can affect water resources available for energy genera-tion for those regional populations.Additionally,the vulnerabilities to the energy and water nexus are beyond the physical infrastruc-tures themselves and extend into supporting and interdependent infrastructures.Evaluation of these vulnerabilities relies on the integration of the disparate and distributed data associated with each of the infrastructures,environments and populations served,and robust analytical methodologies of the data.A capability for the deployment of these methods on relevant data from multiple components on a single platform can provide actionable informa-tion for interested communities,not only for individual energy and water systems,but also for the system of systems that they com-prise.Here,we survey the highest priority data needs and analy-tical methods for inclusion on such a platform. | Melissa R.Allen Syed Mohammed Arshad Zaidi Varun Chandola April M.Morton Christa M.Brelsford Ryan A.McManamay Binita KC Jibonananda Sanyal Robert N.Stewart Budhendra L.Bhaduri | 2018 | Big Earth Data2018，2，3： | 0 |
| 10 | American Senior Communities: Healthcare Fraud Detection显示文摘Healthcare fraud is an increasingly large problem in the United States for patients, taxpayers, and the government, with the National Healthcare Anti-Fraud Association (NHCAA) estimating the costs to be more than tens of billions each year (NHCAA, 2018). To address this issue, government agencies and insurers can utilize data analytics to detect and prevent healthcare fraud. The American Senior Communities (ASC) case is a recent example of a complex healthcare fraud scheme committed by several high ranking officers involving kickbacks, fictitious vendors, and money laundering through shell companies. The indictment details how $16 million was stolen is particularly given the population cared for by ASC—the elderly, individuals with disabilities, low income adults, pregnant women and children. This case demonstrates several ways healthcare fraud can be perpetrated, highlights the role of the auditor, and introduces students to the importance of employing data analytics to prevent and detect fraud. | Devon Baranek | 2018 | Journal of Modern Accounting and Auditing2018，14，12： | 0 |
| 11 | Cluster analysis of MOOC learners' characteristics: A literature review显示文摘MOOC learners are generally divided into two categories according to whether they finish the courseor drop out. This binary classification is an oversimplified portrait of MOOC learners. For example, peoplemay be only interested in certain parts of a MOOC, hence ignoring 'irrelevant' activities, or they may beactively engaged in a MOOC but with no desire to obtain a course certificate, hence skipping assess-ment assignments. These learners are often classified into the 'drop-out' category, which, nevertheless,fails to explain why they 'drop out' and to effectively capture the heterogeneity of MOOCacteristics. | Mengqian Wang Yizhou Fan Wenge Guo Qiong Wang | 2018 | 中国远程教育2018，，7： | 0 |
| 12 | VisQAC: Visual Analytics for Online Q&A Communities显示文摘Online question and answer(Q&A)communities,which allow users to exchange knowledge by asking and answering questions,have become increasingly popular.As a result of user active participation,these communities store overwhelming volumes of information.However,existing related methods are unable to meet community operators’needs for analyzing multi-dimensional Q&A sequences and understanding user behavior.In this paper,collaborating with domain experts in online community,we present a system,VisQAC,which explores the patterns of Q&A sequence and user behavior.In the system,a novel visual design is proposed,which is combined with flexible mapping measures for analyzing critical characteristics of sequence data.Moreover,a timeline visualization method is designed to visualize data with categorical attributes and its correlation can be displayed flexibly by choosing time mode and time granularity.The usefulness and effectiveness of the system are demonstrated with several case studies of VisQAC with community operators based on the Zhihu dataset.Our evaluation shows that VisQAC is beneficial to the understanding of Q&A sequence and associated user behavior. | Jing Liang Ruoyu Jia Min Zhu Henry B L Duh | 2019 | Journal of Beijing Institute of Technology2019，28，2： | 0 |
| 13 | A Generic Data Analytics System for Manufacturing Production显示文摘The increase in the amount of manufacturing information available means that big data can be collected and, with appropriate deep analysis, could be of great value to manufacturers. However, most small manufacturers cannot afford the overhead of a professional data analytics team. To address this problem, in this paper a generic data analytics system, Generic Manufacturing Data Analytics system(GMDA), is proposed. This system can perform most manufacturing data analytics tasks and users can easily carry out data analysis even if they have no prior knowledge or experience of data analytics. To establish such a system, we designed an abstract language,GMDL, to describe the manufacturing data analytics tasks. Aimed at factory data analytics, several algorithms were selected, tuned, optimized, and finally integrated into the system. Some noteworthy techniques were developed in GMDA such as proper algorithm selection strategy and an optimal parameter determination algorithm. Case studies show the practicability and reliability of the system. | Hao Zhang Hongzhi Wang Jianzhong Li Hong Gao | 2018 | Big Data Mining and Analytics2018，1，2： | 0 |
| 14 | PhiBench 2.0: characterizing data analytics workloads on Intel Knights Landing显示文摘With high computational capacity, e.g. many-core and wide floating point SIMD units, Intel Xeon Phi shows promising prospect to accelerate high-performance computing(HPC) applications. But the application of Intel Xeon Phi on data analytics workloads in data center is still an open question. Phibench 2.0 is built for the latest generation of Intel Xeon Phi(KNL, Knights Landing), based on the prior work PhiBench(also named BigDataBench-Phi), which is designed for the former generation of Intel Xeon Phi(KNC, Knights Corner). Workloads of PhiBench 2.0 are delicately chosen based on BigdataBench 4.0 and PhiBench 1.0. Other than that, these workloads are well optimized on KNL, and run on real-world datasets to evaluate their performance and scalability. Further, the microarchitecture-level characteristics including CPI, cache behavior, vectorization intensity, and branch prediction efficiency are analyzed and the impact of affinity and scheduling policy on performance are investigated. It is believed that the observations would help other researchers working on Intel Xeon Phi and data analytics workloads. | 解壁伟 Zhan Jianfeng Wang Lei Zhang Lixin | 2019 | High Technology Letters2019，25，2： | 0 |
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