LEADER 03442nam 22005293 450 001 9911004815803321 005 20231110233234.0 010 $a9781523153381 010 $a1523153385 010 $a9781839534171 010 $a1839534176 035 $a(MiAaPQ)EBC29673284 035 $a(Au-PeEL)EBL29673284 035 $a(CKB)24753661600041 035 $a(NjHacI)9924753661600041 035 $a(OCoLC)1344160654 035 $a(EXLCZ)9924753661600041 100 $a20220901d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStreaming Analytics $eConcepts, Architectures, Platforms, Use Cases and Applications 205 $a1st ed. 210 1$aPiraí :$cInstitution of Engineering & Technology,$d2022. 210 4$d©2022. 215 $a1 online resource (625 pages) 225 1 $aComputing and Networks 311 08$aPrint version: Raj, Pethuru Streaming Analytics Piraí : Institution of Engineering & Technology,c2022 327 $aChapter 1: Streaming data processing - an introductionChapter 2: Event processing platforms and streaming databases for event-driven enterprisesChapter 3: A survey on supervised and unsupervised algorithmic techniques to handle streaming Big DataChapter 4: Sentiment analysis on streaming data using parallel computingChapter 5: Fog and edge computing paradigms for emergency vehicle movement in smart cityChapter 6: Real-time stream processing on IoT data for real-world use casesChapter 7: Rapid response system for road accidents using streaming sensor data analyticsChapter 8: Applying streaming analytics methods on edge and fog device clustersChapter 9: Delineating IoT streaming analyticsChapter 10: Describing the IoT data analytics methods and platformsChapter 11: Detection of anomaly over streams using isolation forestChapter 12: Detection of anomaly over streams using big data technologiesChapter 13: Scalable and real-time prediction on streaming data - the role of Kafka and streaming frameworksChapter 14: Object detection techniques for real-time applicationsChapter 15: EdgeIoTics: leveraging edge cloud computing and IoT for intelligent monitoring of logistics container volumesChapter 16: A hybrid streaming analytic model for detection and classification of malware using Artificial Intelligence techniquesChapter 17: Performing streaming analytics on tweets (text and images) dataChapter 18: Machine learning (ML) on the Internet of Things (IoT) streaming data toward real-time insights. 330 $aIn this book, the authors articulate the challenges associated with streaming data and analytics, describe data analytics algorithms and approaches, present edge and fog computing concepts and technologies and show how streaming analytics can be accomplished in edge device clouds. They also delineate several industry use cases. 410 0$aComputing and Networks 606 $aReal-time data processing$vCongresses 615 0$aReal-time data processing 676 $a004.3 700 $aRaj$b Pethuru$0786064 701 $aSurianarayanan$b Chellammal$01060028 701 $aSeerangan$b Koteeswaran$01822021 701 $aGhinea$b George$01765867 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911004815803321 996 $aStreaming Analytics$94388031 997 $aUNINA