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Encyclopedia of Big Data

為了解決Location service的問題，作者Schintler, Laurie A. (EDT)/ McNeely, Connie L. (EDT) 這樣論述：

This encyclopedia will be an essential resource for our times, reflecting the fact that we currently are living in an expanding data-driven world. Technological advancements and other related trends are contributing to the production of an astoundingly large and exponentially increasing collection o

f data and information, referred to in popular vernacular as "Big Data." Social media and crowdsourcing platforms and various applications ― "apps" ― are producing reams of information from the instantaneous transactions and input of millions and millions of people around the globe. The Internet-of-

Things (IoT), which is expected to comprise tens of billions of objects by the end of this decade, is actively sensing real-time intelligence on nearly every aspect of our lives and environment. The Global Positioning System (GPS) and other location-aware technologies are producing data that is spec

ific down to particular latitude and longitude coordinates and seconds of the day. Large-scale instruments, such as the Large Hadron Collider (LHC), are collecting massive amounts of data on our planet and even distant corners of the visible universe. Digitization is being used to convert large coll

ections of documents from print to digital format, giving rise to large archives of unstructured data. Innovations in technology, in the areas of Cloud and molecular computing, Artificial Intelligence/Machine Learning, and Natural Language Processing (NLP), to name only a few, also are greatly expan

ding our capacity to store, manage, and process Big Data. In this context, the Encyclopedia of Big Data is being offered in recognition of a world that is rapidly moving from gigabytes to terabytes to petabytes and beyond. While indeed large data sets have long been around and in use in a variety of

fields, the era of Big Data in which we now live departs from the past in a number of key respects and with this departure comes a fresh set of challenges and opportunities that cut across and affect multiple sectors and disciplines, and the public at large. With expanded analytical capacities at h

and, Big Data is now being used for scientific inquiry and experimentation in nearly every (if not all) disciplines, from the social sciences to the humanities to the natural sciences, and more. Moreover, the use of Big Data has been well established beyond the Ivory Tower. In today's economy, busin

esses simply cannot be competitive without engaging Big Data in one way or another in support of operations, management, planning, or simply basic hiring decisions. In all levels of government, Big Data is being used to engage citizens and to guide policy making in pursuit of the interests of the pu

blic and society in general. Moreover, the changing nature of Big Data also raises new issues and concerns related to, for example, privacy, liability, security, access, and even the veracity of the data itself.Given the complex issues attending Big Data, there is a real need for a reference book th

at covers the subject from a multi-disciplinary, cross-sectoral, comprehensive, and international perspective. The Encyclopedia of Big Data will address this need and will be the first of such reference books to do so. Featuring some 500 entries, from "Access" to "Zillow," the Encyclopedia will serv

e as a fundamental resource for researchers and students, for decision makers and leaders, and for business analysts and purveyors. Developed for those in academia, industry, and government, and others with a general interest in Big Data, the encyclopedia will be aimed especially at those involved i

n its collection, analysis, and use. Ultimately, the Encyclopedia of Big Data will provide a common platform and language covering the breadth and depth of the topic for different segments, sectors, and disciplines. Laurie A. Schintler is an Associate Professor in the School of Policy, Government

, and International Affairs at George Mason University. Dr. Schintler received her Ph.D.in Urban and Regional Planning at the University of Illinois at Champaign-Urbana and is a well-known computational social scientist and expert in the areas of ＂Big Data,＂ network analysis, geospatial analysis, sc

ience and technology, health and medicine, transportation, and regional science. Dr. Schintler has over 70 peer-reviewed articles, book chapters, and technical reports, as well as a co-edited book entitled New Advances in Transportation and Telecommunications Modeling: Cross-Atlantic Perspectives (2

005), and numerous blog posts, invited presentations, and media appearances. She is also the recipient of a patent for ＂System and method for analyzing the structure of logical networks＂ (USPTO: 20100306372, July 2010; S. Gorman, R. Kulkarni, L. Schintler, and R. Stough). Dr. Schintler has been a Pr

incipal or Co-Principal Investigator on a number of grants from various sponsors, including the United States Department of Transportation, National Institutes of Health, Department of Homeland Security, and National Park Service, among others. She is currently an Associate Director of the Center fo

r Study of International Medical Practices and Policies and Director of the Transportation, Policy, Operations, and Logistics Masters program at George Mason University. She teaches courses in advanced analytical methods and Big Data. Laurie Schintler is also a co-founder of the company Fortiusone (

Geoiq), a geospatial data intelligence company (acquired by ESRI, Inc.). Connie L. McNeely received her Ph.D. from Stanford University in the field of Sociology and is currently Professor in the School of Policy, Government, and International Affairs at George Mason University, where she also serves

as Co-Director of the Center for Science and Technology Policy. Dr. McNeely’s teaching and research address various aspects of science, technology, and innovation, organizational behavior, globalization, public policy, law and governance, social theory, and culture. She also is Principal Investigat

or on major research projects examining national and international scientific networks and policy impacts on diversity in the science and technology workforce, and has received recognition for her work emphasizing complex data analytics, systems mapping, and model construction. Her recent work has i

ncluded research in the areas of ＂Big Data＂ and data science, education, culture and innovation, and health and medical policy, with ongoing projects examining cultural and institutional dynamics and broader matters of inequality and polity participation. Moreover, in addition to new and forthcoming

articles in major journals on ＂Big Data＂ and the organization of related symposia, she has been an invited speaker and participant in various workshops and conferences on the topic, and has prepared reports for public and private entities on computational scientists and exascale computing activitie

s. She also leads a Research Group on Global Innovation in Science and Technology. Dr. McNeely has numerous publications and is active in several professional associations, serves as a reviewer and evaluator in a variety of programs and venues, and sits on several advisory boards and committees.

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客製化服務、品牌形象、知覺價值對消費者購買品牌電腦意願影響之研究

為了解決Location service的問題，作者李孟倫 這樣論述：

摘要台灣是個美麗的寶島，不只是得天獨厚的地理位置，而是眾多產業的發展享譽全球，尤其在資訊科技產業這塊領域，可謂獨領風騷，也因如此，除了擠身成為全球IT重鎮，同時造就國內市場隨處可見的資訊產品，而在這樣環境的孕育下，國人對於電腦設備的要求，也越趨嚴苛。面對擁擠競爭的市場，電腦品牌廠的經營如何能撼動廣大需求的消費者，進而提升購買意願，是本研究主要之議題。透過文獻蒐集、瞭解，以客製化服務、品牌形象、知覺價值等關聯因素之假說和分析，主要目的在探討消費者購買品牌電腦意願之影響。本研究採問卷調查法，時間為2022年5月20日到2022年6月3日，以購買A品牌電腦之消費者為主進行網路問卷方式填答，總共回收

有效樣本為420份。為了假說研究上的精確度，本研究採用SPSS統計軟體進行敘述性統計分析、取樣適切性量數、巴氏球形檢定、信度分析、效度分析、迴歸分析以及中介效果分析。研究結果顯示：客製化服務將會影響品牌形象，並且客製化服務會影響知覺價值以及購買意願；品牌形象會影響購買意願，同時知覺價值也會影響購買意願；品牌形象在客製化服務與購買意願之間具有中介效果，知覺價值在客製化服務與購買意願之間也是具有中介效果。

Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies: ICoCIST 2021

為了解決Location service的問題，作者 這樣論述：

Multi-Choice Programming with Benefits using Kriging Interpolation Method.- Prediction of Alzheimer’s Disease using Machine Learning Algorithm.- A Study of the Caputo-Fabrizio Fractional Model for Atherosclerosis Disease.- Fourth order computations of forced convection heat transfer past an isotherm

al/isoflux cylinder in cylindrical geometry with pseudo time iteration technique.- Implementation of Arithmetic Logic Unit using Area Efficient Adder.- Optimized design of ALU using Reversible Gates.- On-street parking management in Urban CBD: A Review.- Optimal Location of IPFC on LFC Studies Consi

dering PI-TIDN controller and RT-Lab.- Redox Flow Battery Support for Combined ALFC-AVR Control of Multiarea Thermal system Incorporating Renewable Energy Sources.- Cascaded Neural Network Approach for Template Based Array Synthesis.- Deep Learning Assisted Technology for MIMO OFDM 5G Application.-

Multi objective Hydro-Thermal-Wind Scheduling applying PSO.- Design and Implementation of Recommendation System using Sentiment Analysis in Social Media.- Content Based Movie Recommendation System with Sentiment Evaluation of Viewer’s Reviews.- New characterization of electrode of supercapacitor wit

h its application as a backup power supply.- Classification of Indian classical dance hand gestures: A Dense SIFT based approach.- A Grid Connecting Control Scheme for Reactive Power Compensation of PV Inverter.- Quantification of Urinary bladder for early detection of hazard in Oliguric patient und

er dialysis using Embedded System.- Shorted Non-radiating Edges Integrated 2x1 Rectangular Microstrip Antenna for Concurrent Improvement of Gain and Polarization Purity.- A mutual authentication and key agreement protocol for smart grid environment using lattice.- Identification of Malignant Lymphob

last Cell in Bone Marrow using Machine Learning.- Predicting Tamil Nadu Election 2021 Results using Sentimental Analysis Before Counting.- Ticket Dispensation Using Face Detection and Classification.- Estimating the Effectiveness of Paratransit Service in Guwahati City.- A state-of-the-art review on

multi-criteria decision making approaches for micro-grid planning.- Availability of different agricultural biomass for energy applications in Villupuram and Cuddalore district.- Deep Wavelet based Compressive Sensing Data Reconstruction for Wireless Visual Sensor Networks.- Optimized 64-bit reversi

ble BCD Adder for low power applications and its comparative study.- Disease Prediction using Various Data Mining Techniques.- Classification Of Medicinal Plant Species Using Neural Network Classifier: A Comparative Study.- Stock Market Prediction of Neural network: A Literature Review.- Expanding E

lectricity Access in Rural Uttarakhand by Mobilization of Local Resources.- Detection of Abnormalities in Mammograms using Deep Convolutional Neural Networks.- Orange Fruit Recognition using Neural Networks.- Machine learning based method for recognition of paddy leaf diseases.- Data-Path Designing

in Multi-Voltage Domain.- State Space Approach of Automatic Generation Control of Two-Area Multi Source Power systems.- A neural network model to estimate parameters of DBSCAN for flood image segmentation.- Quality Assessment of Public Transport: A Review.- Blockchain Technology Used in the Mid-Day

Meal Scheme Program Supply Chain Management.- Economic Load Dispatch: A Holistic Review on Modern Bio-Inspired Optimization Techniques.- Enhanced Multigradient Dilution Preparation.- Breakdown Voltage Improvement in AlGaN/GaN HEMT by Introducing a Field Plate.- Engineering optimization using an adva

nced hybrid algorithm.- Image Enhancement using Chicken Swarm Optimization.- AHP-utility Approach for Mode Choice Analysis of Online Delivery System.- Medical Electronics Device to Provide Non-Invasive Therapy to Treat Deep Vein Thrombosis using BLE and Embedded Systems.- Applying Efforts for Behavi

or Based Authentication for Mobile Cloud Security.- A Comprehensive Study on SCADA based Intake, W

基於非疊代同步相量運算技術之雙端帶有線路負載輸電線路故障定位演算法研究

為了解決Location service的問題，作者周廣凱 這樣論述：

目錄摘要 iAbstract ii誌謝 iv目錄 v表目錄 vii圖目錄 viii第一章 緒論 11.1 背景與動機 11.2 文獻回顧 21.3 研究貢獻 51.4 論文架構 6第二章 雙端帶有一線路負載故障定位演算法 72.1 長程輸電線模型 72.2 雙端故障定位演算法概述 92.3 故障定位指標推導 112.3.1 故障定位指標用於雙端單一線徑帶有一線路負載輸電線路 112.3.2 故障定位指標用於雙端多區段複合線徑帶有一線路負載輸電線路 17第三章 雙端帶有兩線路負載故障定位演算法 253.1 故障定位指標用於雙端單一線徑帶有兩線路負載輸電線路

253.2 故障定位指標用於雙端多區段複合線徑帶有兩線路負載輸電線路 34第四章 演算法性能評估 474.1 雙端單一線徑帶有一線路負載輸電線路模擬測試與分析 474.1.1 雙端單一線徑帶有一線路負載輸電線路性能測試 474.1.2 雙端單一線徑帶有一線路負載輸電線路模擬統計 524.2 雙端多區段複合線徑帶有一線路負載輸電線路模擬測試與分析 534.2.1 雙端多區段複合線徑帶有一線路負載輸電線路性能測試 534.2.2 雙端多區段複合線徑帶有一線路負載輸電線路模擬統計 594.3 雙端單一線徑帶有兩線路負載輸電線路模擬測試與分析 604.3.1 雙端單一線徑帶有兩線

路負載輸電線路性能測試 604.3.2 雙端單一線徑帶有兩線路負載輸電線路模擬統計 644.4 雙端多區段複合線徑帶有兩線路負載輸電線路模擬測試與分析 654.4.1 雙端多區段複合線徑帶有兩線路負載輸電線路性能測試 654.4.2 雙端多區段複合線徑帶有兩線路負載輸電線路模擬統計 714.5 不平衡負載輸電線路模擬測試與分析 724.5.1 三相不平衡線路負載輸電線路性能測試 724.5.2 單相線路負載輸電線路性能測試 74第五章 現有技術之比較分析 76第六章 結論與未來研究方向 846.1 結論 846.2 未來研究方向 84參考文獻 85