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Produktbild: Principles of Big Graph: In-depth Insight

Principles of Big Graph: In-depth Insight

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Beschreibung

Details

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.01.2023

Herausgeber

Ripon Patgiri + weitere

Verlag

Elsevier Science & Technology

Seitenzahl

458

Maße (L/B)

22,9/15,2 cm

Gewicht

860 g

Sprache

Englisch

ISBN

978-0-323-89810-2

Beschreibung

Portrait

Dr. Ripon Patgiri is an Assistant Professor at the Department of Computer Science & Engineering, National Institute of Technology Silchar, since 2013. His research interests include bloom filters, storage systems, security, and cryptography computing. He has published numerous papers in reputed journals, conferences, and books. Also, he has been awarded with several international patents. He is a senior member of IEEE. He was the General Chair of ICACNI 2018 and BigDML 2019. He is the Organizing Chair of FRSM 2020 and ADCOM 2020. Also, he is the Program Chair of CoMSO 2020, CoMSO 2021, and CoMSO 2022. He is also an editor of several multi-authored books. Moreover, he has received two research project fundings from SERB and DST, India.

Ganesh Chandra Deka is currently Deputy Director (Training) at Directorate General of Training, Ministry of Skill Development and Entrepreneurship, Government of India, New Delhi-110001, India. His research interests include e-Governance, Big Data Analytics, NoSQL Databases and Vocational Education and Training.

He has 2 books on Cloud Computing published by LAP Lambert, Germany. He is the Co-author for 4 text books on Fundamentals of Computer Science (3 books published by Moni Manik Prakashan, Guwahati, Assam, India and 1 IGI Global, USA). As of now he has edited 14 books (6 IGI Global, USA, 5 CRC Press, USA, 2 Elsevier & 1 Springer) on Big data, NoSQL and Cloud Computing and authored 10 Book Chapters.

He has published around 47 research papers in various IEEE conferences. He has organized 08 IEEE International Conferences as Technical Chair in India. He is the Member of the editorial board and reviewer for various Journals and International conferences. Member of IEEE, the Institution of Electronics and Telecommunication Engineers, India and Associate Member, the Institution of Engineers, India

Assistant Professor Anupam Biswas works in Computer Science and Engineering at the National Institute of Technology Silchar, Silchar, Assam, India.

Details

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.01.2023

Herausgeber

Verlag

Elsevier Science & Technology

Seitenzahl

458

Maße (L/B)

22,9/15,2 cm

Gewicht

860 g

Sprache

Englisch

ISBN

978-0-323-89810-2

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Zeitfracht Medien GmbH
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produktsicherheit@zeitfracht.de

Herstelleradresse

Elsevier Science & Technology
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tradeorders@elsevier.com

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  • Produktbild: Principles of Big Graph: In-depth Insight
  • Preface
    Ripon Patgiri, Ganesh ChandraDeka and Anupam Biswas
    1. CESDAM: Centered subgraph data matrix for large graph representation
    Anupam Biswas and Bhaskar Biswas
    2. Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications
    Samiya Khan, Xiufeng Liu, Syed Arshad Ali and Mansaf Alam
    3. An empirical investigation on BigGraph using deep learning
    Lilapati Waikhom and Ripon Patgiri
    4. Analyzing correlation between quality and accuracy of graph clustering
    Soumita Das and Anupam Biswas
    5. geneBF: Filtering protein-coded gene graph data using bloom filter
    Sabuzima Nayak and Ripon Patgiri
    6. Processing large graphs with an alternative representation
    Ravi Kishore Devarapalli and Anupam Biswas
    7. MapReduce based convolutional graph neural networks: A comprehensive review
    U. Kartheek Chandra Patnaik and Ripon Patgiri
    8. Fast exact triangle counting in large graphs using SIMD acceleration
    Kaushik Ravichandran, Akshara Subramaniasivam, Aishwarya PS and Kumar NS
    9. A comprehensive investigation on attack graphs
    M Franckie Singha and Ripon Patgiri
    10. Qubit representation of a binary tree and its operations in quantum computation
    Arnab Roy, Joseph L Pachuau and Anish Kumar Saha
    11. Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data
    Saurabh Kumar Srivastava, Ankit Vidyarthi and Sandeep Kumar Singh
    12. Big graph based online learning through social networks
    Rahul Chandra Kushwaha
    13. Community detection in large-scale real-world networks
    Dhananjay Kumar Singh and Prasenjit Choudhury
    14. Power rank: An interactive web page ranking algorithm
    Ankit Vidyarthi and Pawan Singh
    15. GA based energy efficient modelling of a wireless sensor network
    Anish Kumar Saha, Joseph L Pachuau, Arnab Roy and C. T. Bhunia
    16. The major challenges of big graph and their solutions: A review
    Fitsum Gebreegziabher and Ripon Patgiri
    17. An investigation on socio-cyber crime graph
    V S NageswaraRao Kadiyala and Ripon Patgiri