Data Science and Cases in Sustainability
Description
This book discusses the fascinating world of data science and cases in sustainability focusing on the properties of two modern data science fields-data mining and big data analytics-along with their interdependencies, emphasizing sustainable applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). It explains the practical understanding of all the techniques and tools of data mining and big data analytics, along with their mathematical foundations and sustainable applications. Each of these two techniques plays an important part in revealing buried information from massive volume of data. The book emphasizes the interdisciplinary character of data science, relying on topics such as computer science, statistics, physics, economics and more to provide readers with a comprehensive understanding. It carefully takes the readers through the data science pipeline, including data collection, preparation, analysis, management, visualization and storage of high volumes of stationary and non-stationary data and its sustainable applications.
The second of the two volumes, the book digs into the methodologies and algorithms utilized at each level, including key learning mechanisms, association analysis, classification, clustering and outlier analysis. Furthermore, it provides insights on the growing discipline of deep learning and the utilization of distributed systems to handle large amounts of data. Case studies demonstrate the sustainable uses of data in a variety of fields from a big data perspective. Emphasizes the need of data science in handling this "big data" flood, it dives into the technological complexities of massive data storage and processing, emphasizing advances in artificial intelligence that enable successful analysis. Data mining and big data analysis approaches are thoroughly examined in the book, emphasizing their interplay and unique contributions to unravelling the secrets concealed inside massive data sets. The book provides a clear roadmap for navigating the complex world of big data analysis, providing readers with the information and tools they need to gain useful insights from the ever-increasing types of data and, ultimately, to shape the future through data-driven real time decisions.
More details
Person
Ashish Ghosh is the Director of International Institute of Information Technology Bhubaneswar, Odisha, India, on deputation from the Indian Statistical Institute (ISI) Kolkata, West Bengal, India. Starting out as an electronic engineer, he moved to computer science during his master's and finally to artificial intelligence (AI) during his PhD program. He completed his postdoctoral research in Japan and then joined the ISI Kolkata as a faculty member. He is currently Senior Professor (Higher Academic Grade) at the ISI Kolkata. He has been consistently contributing immensely to the field of AI, machine learning, image/video analysis and data science. With more than 300 research articles published on these fields, 12 books edited and an h-index of 53, he is a member of the group of the top 2% of scientists working in computer science. He is a member of the founding team that established the National Center for Soft Computing Research at the ISI Kolkata, in 2004, with funding from the Department of Science and Technology (DST), Government of India. He acted as the In-Charge of the Center that has now been recognized as an Associate Institute of ISI Kolkata. He also served as the Head of the well-known Machine Intelligence Unit, ISI Kolkata. He is currently leading the Data Science Research Consortium Project under a national mission of DST. He established and acted as the project director of the Technology Innovation Hub on Data Science, Big Data Analytics and Data Curation at the ISI Kolkata under the NMICPS mission of the Government of India with an initial funding of INR 100 Crores.
For his pioneering contribution in computational intelligence and image analysis, he received the Young Scientist Award from the Indian Science Congress Association (ISCA, 1992), the Young Scientist Medal from Indian National Science Academy (INSA, 1995) and the Young Associateship of Indian Academy of Sciences (IASc, 1997). He is a Fellow of the West Bengal Academy of Science and Technology (WAST, 2015), the National Academy of Sciences (NASI, 2019), the International Association for Pattern Recognition (IAPR, 2020), the Asia-Pacific Artificial Intelligence Association (AAIA, 2022) and the International Artificial Intelligence Industry Alliance (AIIA, 2023). For his excellent service to IEEE, he received the IEEE-GRSS Regional Leader Award, in 2019. He is a member of the Research Advisory Committee of many academic organizations, and SEBI (Mumbai, India), and was nominated to the Academic Council of Banasthali Vidyapith as an Eminent Educationist.
He is Associate Editor of several international journals, such as the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , IET Journal of Computer Vision , Springer Nature Computer Science , IET CAAI Transactions on Intelligence Technology , Sadhana , and the Journal of Banking and Financial Technology ; as well as Series Editor of the Communications in Computer and Information Science series (Springer Nature). He has visited various universities/academic institutes in connection with conducting collaborative research/projects and delivered lectures in different countries including China, Germany, Hong Kong, Italy, Japan, Poland, Portugal, South Korea, Thailand, The Netherlands, the UK and the USA. He has served as plenary/keynote speaker in many international conferences in India and abroad. As PI and Co-PI, he has completed 18 projects of national and international importance, funded by the Government of India, European Commission, Indo-Italy and Indo-US forum for science and technology.
Content
1. Knowledge Discovery from Databases.- 2. Attributes, Data Sets and Data Storage.- 3. Data Mining Systems.- 4. Data Mining Tasks.- 5. Associations and Correlations: Frequent Pattern Generation.- 6. Efficient and Scalable Frequent Itemset Generation.- 7. Incremental Rule Mining.- 8. From Association Mining to Correlation Analysis.- 9. Classification.- 10. Prediction.- 11. Cluster Analysis Techniques.- 12. Advanced Clustering Techniques.- 13. Anomaly Detection.- 14. Anomaly Detection Techniques.- 15. Distributed Data Mining.- 16. Advantages and Disadvantages of Data Mining.- 17. What Is Big Data?.- 18. Importance of Big Data.- 19. Big Data Platforms.-20. Machine Learning Strategies and Challenges for Big Data.- 21. Solving Big Problems with Big Data.- 22. Conclusions and Discussions.- 23. Epilogues.