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33rd International on Passive and Low Energy Architecture conference: Design to Thrive, PLEA 2017

作者： Rakha, Tarek Shin, Dongmin Moss, Todd W. School of Architecture Syracuse University SyracuseNY United States Whitman School of Management Syracuse University SyracuseNY United States

ISBN: (纸本)9780992895754

Third-party verification and certification processes for low energy built environments have played a critical role in influencing transitioning communities to reduce their emissions footprint in both residential and commercial buildings. One prominent example is the Leadership in Energy and Environmental Design (LEED) certification program, which was conceived by the United States Green Building Council (USGBC). Its residential platform, LEED for Homes, started as a pilot in 2004 and was fully implemented in 2008. This paper attempts to answer the question: what are the characteristics of growing communities for United States LEED residential buildings? The paper examines residential LEED market adoption trends in the United States by analysing a decade-worth of data from 2004 to 2015 (71438 certified units). The study implemented data visualization techniques and statistical analyses to explore predictors of residential LEED community adoption trends. The investigation concluded that market share was associated with educational attainment and number of applied policies, but was not associated with median household income or political orientation (democrat vs. republican). The findings suggest further research is warranted, specifically as related to local practice support from LEED Accredited Professionals as mechanisms of growing transitioning low carbon communities, as well as local level green market incentivization policies. © Copyright 2017 NCEUB.

关键词： data visualization

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Failure analysis helps sanitation district no. 1 of northern Kentucky improve its continuous sewer assessment program

Failure analysis helps sanitation district no. 1 of northern...

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Water Environment Federation Technical Exhibition and conference 2017, WEFTEC 2017

作者： Copeland, Bo Van Doren, Matthew Ahlberg, Gregory Hazen and Sawyer CincinnatiOH United States SD1 Fort WrightKY United States

ISBN: (纸本)9781510863682

Sanitation District No. 1 of Northern Kentucky (SD1) is the second largest public sewer utility in Kentucky, serving more than 290,000 residents and maintaining 1,600 miles of sanitary sewer. During implementation of its Capacity, Management, Operation, and Maintenance (CMOM) Program, SD1 developed a Continuous Sewer Assessment Program (CSAP) to develop a logical approach to prioritizing inspection, cleaning, and other maintenance of the collection system. This program was commenced in 2008 and has been effective in reducing operation and maintenance (O&M)-related overflows in the system. SD1 is now refining the program to become more efficient and to improve the likelihood of detecting pipe problems before they result in failures. As part of this program update, SD1 evaluated the historical sewer inspection and failure records since July 1, 2009, to determine if re-inspection triggers could be further refined and return frequencies lengthened without significantly increasing risk of failure and to make further data-driven recommendations. Separate lists of pipe structural failures and O&M failures were developed from an extensive data set and analyzed with a data visualization software. The results of this evaluation helped SD1 to better understand the historical behavior of the sewer system. This information will be used to revise and improve CSAP processes with the goal to better assess infrastructure condition and life cycle, cost-effectively minimize O&M related spills, and implement system renewal and rehabilitation in a way that targets dollars where they have the highest benefit. Copyright © 2017 Water Environment Federation.

关键词： Inspection

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Patent abstract analysis on Chinese big data

Patent abstract analysis on Chinese big data

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International conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zongbao Yang Zhiqian Zhang Shaohong Zhang Jing Wang Haodong Lin Baoying Zeng Department of Computer Science Guangzhou University Guangzhou P.R. China Guangzhou Library Guangzhou P.R. China

In recent years, big data technologies draw wide attention from academic and industry. Patents analysis is widely viewed as one of the major tools to tracking the development of technologies. In traditional patent analysis methods, some given keywords are used to query big data patent information from related patent web sites, and then analyze some trends of big data. But big data techniques are involved in a lot of applications, different patent applicants have different subjectivities in the setting for keywords. In view of this problem, we proposed a new method for patent analysis of big data in a more precise way, based on the analysis of big data patent abstracts in China's domestic. Our method extracted keywords from the abstract of big data patent documents to obtain more accurate data for a deeper analysis of patents. Through this study, we found that the development of big data in China's domestic can be divided into two distinct stages and it entered into a quickly growth stage after 2008. Industry development is faster than the academic community; of which Huawei has the most quantities of patent applications. There is a serious unbalance in the development of big data technology in China, and the development in the eastern region is far beyond that in the western region. These findings suggest that the government should pay great attention on the imbalanced development of big data and increase supports for backward areas. Also, the development of big data is expected to improve both government decision-making capabilities and service levels.

关键词： Big data Patents data visualization data mining Computer science Market research Industries

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Energy Consumption Prediction System of Mechanical Processes Based on Empirical Models and Computer-Aided Manufacturing

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2016年第4期16卷 041008页

作者： He, Keyan Tang, Renzhong Zhang, Zhongwei Sun, Wenjun Zhejiang Univ Ind Engn Ctr Hangzhou 310027 Zhejiang Peoples R China

Energy consumption prediction at the process planning stage is the basis of mechanical process optimization aiming at saving energy and reducing carbon emission. The accuracy and efficiency of the prediction method will be the most concerning issues. This paper presents an energy consumption prediction system of mechanical processes based on empirical models and computer-aided manufacturing (CAM). The system was developed based on analysis of energy-related data and data acquiring methods. The energy consumption sources of mechanical processes are divided into two parts: energy of auxiliary machine movements and intrinsic process movements. Considering data sources, there are two kinds of data acquiring methods: acquiring data from database or from CAM files. Process energy state is introduced to support calculation of energy consumption and presentation of calculation results. Example of the system was developed based on Microsoft SQL Server 2008 and UGS NX 7.0, and several examples of energy prediction of mechanical processes were also presented. The results demonstrate that the proposed system developing method is effective in predicting energy consumption of mechanical processes with high accuracy and high efficiency.

关键词： Machinery Physics based modeling Computer-aided manufacturing Simulation Energy levels (Quantum mechanics) Energy consumption Process planning visualization Manufacturing planning databases

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Agricultural Decision Support Framework for Visualisation and Prediction of Western Australian Crop Production 3

Agricultural Decision Support Framework for Visualisation an...

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3rd International conference on Computing for Sustainable Global Development (INDIACom)

作者： Armstrong, Leisa J. Nallan, Sreedhar A. Edith Cowan Univ Sch Sci Perth WA Australia

ISBN: (纸本)9789380544199

An automated decision support system (DSS) framework is proposed to assist Western Australian farmers to improve decision making in crop variety selection for different climatic and agronomic scenarios. The AgMine DSS comprises six major components including data input, data mining, and statistical analysis, database, prediction and visualisation modules. This paper proposes how the visualization and data mining modules can he used to improve farmer decision making for individual *** of climate and other data was carried out using ArcGIS tool using two districts (Milling and Wongan Hills) Collation and preprocessing of data was carried out to raster files and a large repository of raster files was created for later geospatial analysis and climate and crop yield mapping. data sets were sourced from public information resources, national and state agricultural cropping research data. Annual interpolation of rainfall and crop yield of different wheat varieties for the period 2008 to 2012 was carried out using the Arc Map and the Ordinary Kriging method with Spherical Semi-variogram models. Association rule mining techniques where also used to determine the relationship between, crop variety, rainfall and soil at district level. A framework, which combines these techniques, is proposed which can he used by farmers in decision making by providing visualisations of seasonal patterns of rainfall for individual districts and show the effect of various scenarios of dry and wet years on crop production.

关键词： ArcGIS crop production data mining GIS spatial interpolation

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Research of Large-Scale and Complex Agricultural data Classification Algorithms Based on the Spatial Variability 1

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9th IFIP WG 5.14 International conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Chen, Hang Chen, Guifen Cai, Lixia Yang, Yuqin Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Inst Sci & Tech Informat Jilin Beijing 130000 Peoples R China

ISBN: (数字)9783319483573

ISBN: (纸本)9783319483573;9783319483566

In the actual classification problems, as a result of lack of clear boundary information between classification objects, that could lead to loss of classification accuracy easily. Therefore, this article from the spatial patterns of the sample properties to proceed, fuzzy clustering algorithm is proposed based on the sensitivity of attribute weights, through using the attribute weights to improve the classification capability between confusing samples, that is for researching and analysing soil nutrient spatial data with consecutive years to collect in Nongan town. Then through the analysis of the visualization technology to realize the visualization of the algorithm. Experimental results show that introducing weights portray attribute information could reduce the objective function value, and effectively alleviate the phenomenon of boundary data that cannot distinguish. Ultimately to improve the classification accuracy. Meanwhile, use of MATLAB to form visualization of three-dimensional image. The results provide a basis for to improve the accuracy of data classification and clustering analysis of large and complex agricultural data.

关键词： Large-scale and complex data Spatial variation law Fuzzy clustering Soil nutrients Sensitive attribute weights

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Visual Analytics improving data Understandability in IoT Projects: An Overview of the US DOE ARM Program data Science Tools 13

Visual Analytics improving Data Understandability in IoT Pro...

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13th IEEE International conference on Mobile Ad Hoc and Sensor Systems (IEEE MASS)

作者： Batista, Andre F. M. Correa, Pedro L. P. Palanisamy, Giri Univ Sao Paulo POLI USP Escola Politecn Sao Paulo Brazil Oak Ridge Natl Lab Div Environm Sci POB 2008 Oak Ridge TN 37831 USA

ISBN: (纸本)9781509028337

The IoT advancement generates a massive volume and a variety of data at unprecedented velocity. There is a need to interpret and to convert such data into valuable information, enabling the discovery of knowledge as well as the development of new technologies and data products. From the data Science research field, Visual Analytics (VA) techniques seek to support in the analysis and understanding of large datasets using statistical techniques and data mining, aided by visualization techniques. This paper demonstrates how VA techniques can be applied in data Science projects enabling users to reason over a dataset generated by IoT devices, what we call as data Understandability. A practical case is presented in order to provide an overview of the data management procedures and how a set of Visual Analytics tools developed in the context of The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program are helping users to visualize and analyze the collected data from a climate sensor network spread throughout the world.

关键词： data Science Visual Analytics data Understandability IoT ARM Program

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Prominent voices and prevalent discourses: A corporate social responsibility application

Prominent voices and prevalent discourses: A corporate socia...

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International conference on Digital Information Management (ICDIM)

作者： Carlos M. Parra Monica Chiarini Tremblay Arturo Castellanos College of Business Florida International University Miami FL USA CUNY Baruch College New York NY USA

ISBN: (纸本)9781509026425

In this study we develop a simplified technique for identifying prominent voices (and characterizing prevalent discourses) using Text data Mining around Corporate Social Responsibility (CSR) issues or topics. We do this by analyzing a corpus of CSR reports produced by 7 US firms (Citi, Coca-Cola, Exxon-Mobil, General Motors, Intel, McDonald's and Microsoft) in 2004, 2008 and 2012, and focusing on a reduced set of vectors - or Singular Vector Decompositions (SVDs)-derived from these CSR reports while exploring term associations (Text Topics or Term Clusters). Specifically, we use centroid clustering on these SVDs to identify centroid-guiding-CSR-report-components (or firms with prominent voices and prevalent discourses around a CSR topic). The analysis is performed by year in order to discern the way in which prominent voices and prevalent discourses (around CSR topics) have evolved through time. Results indicate that it is difficult for firms to maintain a prominent voice around CSR issues through time, and that when they manage to do so it is because the prevalent discourse has direct business implications.

关键词： Loading Business Radar Time division multiplexing data visualization data mining Security

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Alpha-beta divergences discover micro and macro structures in data 32

Alpha-beta divergences discover micro and macro structures i...

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32nd International conference on Machine Learning, ICML 2015

作者： Narayan, Karthik Punjani, Ali Abbeel, Pieter University of California BerkeleyCA94720 United States University of Toronto ONM5S Canada

ISBN: (纸本)9781510810587

Although recent work in non-linear dimensionality reduction investigates multiple choices of divergence measure during optimization (Yang et al., 2013;Bunte et al., 2012), little work discusses the direct effects that divergence measures have on visualization. We study this relationship, theoretically and through an empirical analysis over 10 datasets. Our works shows how the a and f3 parameters of the generalized alpha-beta divergence can be chosen to discover hidden macro-structures (categories, e.g. birds) or micro-structures (fine-grained classes, e.g. toucans). Our method, which generalizes t-SNE (van der Maaten, 2008), allows us to discover such structure without extensive grid searches over (a, (3) due to our theoretical analysis: such structure is apparent with particular choices of (a, 13) that generalize across datasets. We also discuss efficient parallel CPU and GPU schemes which are non-trivial due to the tree-structures employed in optimization and the large datasets that do not fully fit into GPU memory. Our method runs 20x faster than the fastest published code (Vladymy-rov & Carreira-Perpinan, 2014). We conclude with detailed case studies on the following very large datasets: ILSVRC 2012, a standard computer vision dataset with 1.2M images;SUSY, a particle physics dataset with 5M instances;and HIGGS, another particle physics dataset with 11M instances. This represents the largest published visualization attained by SNE methods. We have open-sourced our visualization code: http://***/absne/.

关键词： visualization

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EpiHosp: A web-based visualization tool enabling the exploratory analysis of complications of implantable medical devices from a nationwide hospital database 26

EpiHosp: A web-based visualization tool enabling the explora...

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26th Medical Informatics in Europe (MIE) conference on Digital Healthcare Empowering Europeans

作者： Ficheur, Gregoire Ferreira Careira, Lionel Beuscart, Regis Chazard, Emmanuel Univ Lille EA 2694 Dept Publ Hlth Lille France

ISBN: (纸本)9781614995128;9781614995111

Administrative data can be used for the surveillance of the outcomes of implantable medical devices (IMDs). The objective of this work is to build a web-based tool allowing for an exploratory analysis of time-dependent events that may occur after the implementation of an IMD. This tool should enable a pharmacoepidemiologist to explore on the fly the relationship between a given IMD and a potential outcome. This tool mine the French nationwide database of inpatient stays from 2008 to 2013. The data are preprocessed in order to optimize the queries. A web tool is developed in PHP, MySQL and Javascript. The user selects one or a group of IMD from a tree, and can filter the results using years and hospital names. Four result pages describe the selected inpatient stays: (1) temporal and demographic description, (2) a description of the geographical location of the hospital, (3) a description of the geographical place of residence of the patient and (4) a table showing the rehospitalization reasons by decreasing order of frequency. Then, the user can select one readmission reason and display dynamically the probability of readmission by mean of a Kaplan-Meier curve with confidence intervals. This tool enables to dynamically monitor the occurrence of time-dependent complications of IMD.

关键词： Medical device data reuse patient safety big data

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