A Modern Approach of Swarm Intelligence Analysis in Big Data: Methods, Tools, and Applications

A Modern Approach of Swarm Intelligence Analysis in Big Data: Methods, Tools, and Applications

Thirunavukkarasu Kannapiran, Krishna Patel, Thirusha T. K., Virendra Kumar Shrivastava, A Suresh Kumar
DOI: 10.4018/978-1-6684-6894-4.ch004
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Swarm intelligence is one of the most modern and less discovered artificial intelligence types. Until now it has been proven that the most comprehensive method to solve complex problems is using behaviours of swarms. Big data analysis plays a beneficial role in decision making, education domain, innovations, and healthcare in this digitally growing world. To synchronize and make decisions by analysing such a big amount of data may not be possible by the traditional methods. Traditional model-based methods may fail because of problem varieties such as volume, dynamic changes, noise, and so forth. Because of the above varieties, the traditional data processing approach will become inefficient. On the basis of the combination of swarm intelligence and data mining techniques, we can have better understanding of big data analytics, so utilizing swarm intelligence to analyse big data will give massive results. By utilizing existing information about this domain, more efficient algorithm can be designed to solve real-life problems.
Chapter Preview
Top

Literature Survey

Table 1.
Literature survey
Sr. noAuthorYearName of PaperTechnique UsedApplications
1.G. Thippa Reddy, M. Praveen Kumar Reddy, Kuruva Lakshmanna, Rajesh Kaluri, Dharmendra Singh Rajput, Gautam Srivastava, And Thar Baker2020Analysis of Dimensionality Reduction Techniques on Big DataPrinciple component analysis and Linear Discriminant analysisCardiotocography dataset
2.Sofia Oikonomidi2020Impact of Big Data Analytics in Industry 4SWOT Analysis TechniqueProduction data, Operational data, Supply chain data etc.
3.Ahmed Afif Monrat, Raihan Ul Islam, Mohammad Shahadat Hossain, and Karl Andersson2018Challenges and Opportunities of Using Big Data for Assessing Flood Risks from Applications of Big Data Analytics Trends, Issues, and ChallengesRule-based inference methodology using the evidential reasoning(RIMER)human-generated data (Twitter, web traffic)
4.Santosh Ray and Mohammed Saeed.2018Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education from Applications of Big Data Analytics Trends, Issues, and ChallengesEducational Data Mining and Learning Analytics-
5.Mohammed Dighriri, Gyu Myoung Lee, and Thar Baker2018Big Data Environment for Smart Healthcare Applications Over 5G Mobile Network from Applications of Big Data Analytics Trends, Issues, and ChallengesData Traffic Aggregation Model4G and 5G mobile networks data traffic
6.Amir Mosavi, Alvaro Lopez, and Annamária R. Varkonyi-Koczy2018Industrial Applications of Big Data: State of the Art Survey-Data generated by vehicles
7.Kan Zheng, Zhe Yang, Kuan Zhang, Periklis Chatzimisios, Kan Yang, and Wei Xiang2016Big Data-Driven Optimization for Mobile Networks toward 5GBig Data-Driven (BDD) mobile network optimization-
8.Sindhu P Menon, Nagaratna P Hegde2015A Survey of Tools and Applications in Big Data--
9.Saurabh Arora and Inderveer Chana2014A Survey of Clustering Techniques for Big Data AnalysisDMM streamReal-time and streaming data
10.Chun-Wei Tsai, Bo-Chi Huang, and Ming-Chao Chiang2014A Novel Spiral Optimization for ClusteringDistributed spiral algorithmData mining
11.R. Madhuri, M. Ramakrishna Murty, J.V.R. Murthy, P.V.G.D. Prasad Reddy, and Suresh C. Satapathy2014Cluster Analysis on Different Data Sets Using K-Modes and K-Prototype AlgorithmsK-Modes and K-Prototype AlgorithmsNumerical data, categorial data and mixed data
12.Mahya Ameryan, Mohammad Reza Akbarzadeh Totonchi, Seyyed Javad Seyyed Mahdavi2014Clustering Based on Cuckoo Optimization AlgorithmCuckoo search algorithmIris dataset, Wine dataset, Cancer dataset, Vowel dataset
13.N.N.R. Ranga Suria, M. Narasimha Murty and G. Athithan2014A ranking-based algorithm for detection of outliers in categorical dataA novel algorithm for mining categorical outliers through rankingBenchmark datasets
14.Younghoon Kim, Kyuseok Shim, Min-Soeng Kim, June Sup Lee2013DBCURE-MR: An efficient density-based clustering algorithm for large data using MapReduceAn efficient density-based clustering algorithmDisjoint set data structure
15.Qing He, Xin Jin, Changying Du, Fuzhen Zhuang, Zhongzhi Shi2012Clustering in extreme learning machine feature spaceExtreme learning machineSynthetic control chart time series data set, Libras movement data set, NIST topic detection and tracking corpus,

Complete Chapter List

Search this Book:
Reset