LEADER 05758nam 22006855 450 001 9910863112203321 005 20240619145317.0 010 $a981-15-6695-X 024 7 $a10.1007/978-981-15-6695-0 035 $a(CKB)4100000011401131 035 $a(MiAaPQ)EBC6318845 035 $a(DE-He213)978-981-15-6695-0 035 $a(PPN)250213206 035 $a(EXLCZ)994100000011401131 100 $a20200825d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing /$fedited by Simon James Fong, Richard C. Millham 205 $a1st ed. 2021. 210 $cSpringer Singapore$d2021 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2021. 215 $a1 online resource (228 pages) 225 1 $aSpringer Tracts in Nature-Inspired Computing,$x2524-552X 311 $a981-15-6694-1 327 $aChapter 1. The Big Data Approach Using Bio-Inspired Algorithms: Data Imputation -- Chapter 2. Parameter Tuning onto Recurrent Neural Network and Long Short Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-dimensional Bioinformatics Datasets -- Chapter 3. Data Stream Mining in Fog Computing Environment with Feature Selection Using Ensemble of Swarm Search Algorithms -- Chapter 4. Pattern Mining Algorithms -- Chapter 5. Extracting Association Rules: Meta-Heuristic and Closeness Preference Approach -- Chapter 6. Lightweight Classifier-based Outlier Detection Algorithms from Multivariate Data Stream -- Chapter 7. Comparison of Contemporary Meta-heuristic Algorithms for Solving Economic Load Dispatch Problem -- Chapter 8. The paradigm on fog computing with bio-inspired search methods and the ?5Vs? of big data -- Chapter 9. Approach for sentiment analysis on social media sites -- Chapter 10. Data Visualisation techniques and Algorithms -- Chapter 11. Business Intelligence -- Chapter 12. Big Data Tools for Tasks. 330 $aThis book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research. . 410 0$aSpringer Tracts in Nature-Inspired Computing,$x2524-552X 606 $aComputational intelligence 606 $aAlgorithms 606 $aBig data 606 $aDatabase management 606 $aApplication software 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 615 0$aComputational intelligence. 615 0$aAlgorithms. 615 0$aBig data. 615 0$aDatabase management. 615 0$aApplication software. 615 14$aComputational Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aBig Data. 615 24$aDatabase Management. 615 24$aInformation Systems Applications (incl. Internet). 676 $a571.0284 702 $aFong$b Simon James$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMillham$b Richard C$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910863112203321 996 $aBio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing$94166239 997 $aUNINA