LEADER 00826nam--2200277---450- 001 990001085060203316 005 20050630102846.0 035 $a000108506 035 $aUSA01000108506 035 $a(ALEPH)000108506USA01 035 $a000108506 100 $a20020612d1999----km-y0enga0103----ba 200 1 $aAltruism$fJames R. Ozinga 210 $aWestport$aLondon$cPreager$dcopyr.1999 215 $aXVII, 174 p.$d24 cm 700 1$aOZINGA,$bJames R.$0553899 912 $a990001085060203316 951 $aII.3. 570(IV C 3333)$b162758 L.M.$cIV C$d00082370 959 $aBK 969 $aUMA 979 $aMARIA$b10$c20020612$lUSA01$h0934 979 $aPAOLA$b90$c20030121$lUSA01$h1320 979 $aPATRY$b90$c20040406$lUSA01$h1715 979 $aCOPAT3$b90$c20050630$lUSA01$h1028 996 $aAltruism$9978649 997 $aUNISA LEADER 03961nam 2200493 450 001 9910467172703321 005 20200520144314.0 035 $a(CKB)4100000000880904 035 \\$a(Safari)9781787120730 035 $a(OCoLC)1000390969 035 $a(MiAaPQ)EBC4925644 035 $a(CaSebORM)9781787120730 035 $a(PPN)220199124 035 $a(Au-PeEL)EBL4925644 035 $a(CaPaEBR)ebr11444592 035 $a(OCoLC)999651647 035 $a(EXLCZ)994100000000880904 100 $a20171018h20172017 uy 0 101 0 $aeng 135 $aurunu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalytics for the Internet of things (IoT) $eintelligent analytics for your intelligent devices /$fAndrew Minteer 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai, [India] :$cPackt,$d2017. 210 4$d©2017 215 $a1 online resource (1 volume) $cillustrations 300 $aIncludes index. 311 $a1-78712-757-5 311 $a1-78712-073-2 330 $aBreak through the hype and learn how to extract actionable intelligence from the flood of IoT data About This Book Make better business decisions and acquire greater control of your IoT infrastructure Learn techniques to solve unique problems associated with IoT and examine and analyze data from your IoT devices Uncover the business potential generated by data from IoT devices and bring down business costs Who This Book Is For This book targets developers, IoT professionals, and those in the field of data science who are trying to solve business problems through IoT devices and would like to analyze IoT data. IoT enthusiasts, managers, and entrepreneurs who would like to make the most of IoT will find this equally useful. A prior knowledge of IoT would be helpful but is not necessary. Some prior programming experience would be useful What You Will Learn Overcome the challenges IoT data brings to analytics Understand the variety of transmission protocols for IoT along with their strengths and weaknesses Learn how data flows from the IoT device to the final data set Develop techniques to wring value from IoT data Apply geospatial analytics to IoT data Use machine learning as a predictive method on IoT data Implement best strategies to get the most from IoT analytics Master the economics of IoT analytics in order to optimize business value In Detail We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You?ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We?ll also review the economics of IoT analytics and you?ll discover ways to optimize business value. By the end of the book, you?ll know how to handle scale for both data storage and analytics, how Apache... 606 $aEmbedded Internet devices 608 $aElectronic books. 615 0$aEmbedded Internet devices. 676 $a004.678 700 $aMinteer$b Andrew$0996775 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910467172703321 996 $aAnalytics for the Internet of things (IoT)$92285387 997 $aUNINA LEADER 03739nam 2200913z- 450 001 9910367744103321 005 20210211 010 $a3-03921-817-4 035 $a(CKB)4100000010106276 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54914 035 $a(oapen)doab54914 035 $a(EXLCZ)994100000010106276 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNumerical and Evolutionary Optimization 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 online resource (230 p.) 311 08$a3-03921-816-6 330 $aThis book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications. 606 $aHistory of engineering and technology$2bicssc 610 $aaveraged Hausdorff distance 610 $abasic differential evolution algorithm 610 $aBloat 610 $abulbous bow 610 $acrop planning 610 $adecision space diversity 610 $adifferential evolution algorithm 610 $adriving events 610 $adriving scoring functions 610 $aeconomic crops 610 $aevolutionary computation 610 $aevolutionary multi-objective optimization 610 $aEvoSpace 610 $aflexible job shop scheduling problem 610 $agenetic algorithm 610 $agenetic programming 610 $aGenetic Programming 610 $aimproved differential evolution algorithm 610 $aimprovement differential evolution algorithm 610 $aintelligent transportation systems 610 $aIV-optimality criterion 610 $aLocal Search 610 $alocal search and jump search 610 $alocation routing problem 610 $ametric measure spaces 610 $amixture experiments 610 $amodel order reduction 610 $amodel predictive control 610 $amodify differential evolution algorithm 610 $amulti-objective optimization 610 $amultiobjective optimization 610 $aNEAT 610 $anumerical simulations 610 $aopen-source framework 610 $aoptimal control 610 $aoptimal solutions 610 $aPareto front 610 $aperformance indicator 610 $apower means 610 $arisky driving 610 $arubber 610 $ashape morphing 610 $asingle component constraints 610 $asurrogate-based optimization 610 $aU-shaped assembly line balancing 610 $avehicle routing problem 615 7$aHistory of engineering and technology 700 $aLara$b Adriana$4auth$01301160 702 $aQuiroz$b Marcela$4auth 702 $aSchütze$b Oliver$4auth 702 $aMezura-Montes$b Efrén$4auth 906 $aBOOK 912 $a9910367744103321 996 $aNumerical and Evolutionary Optimization$93025742 997 $aUNINA