@article{1432774,
      recid = {1432774},
      author = {Sharma, Neha, and Ghosh, Santanu, and Saha, Monodeep,},
      title = {Open data for sustainable community : glocalized  sustainable development goals /},
      publisher = {Springer Singapore Pte. Limited,},
      address = {Singapore :},
      pages = {1 online resource (xxiv, 299 pages) :},
      year = {2021},
      abstract = {This book is an attempt to bring value to the enterprise  pursuits in the areas of research and innovation around the  specific issues in terms of topic selection, open data  resources and researcher orientation. Over the last 300  years, industrial revolutions have had game-changing impact  on societies. Presently, by and large, we are at the  crossroads of the fourth industrial revolution, where  phygital systems are going to play a massive role, where  digital systems can simulate and go beyond the limitations  of the physical world, thereby enabling a new world order.  This transformation is cutting across every sphere known to  mankind. The world will become a globally localized  marketplace. In todays business world, sustainability is a  corporate agenda. Enterprises are also aiming to be  purpose-driven, adaptive and resilient to disruptions. The  contributions to community and environment are part of  their corporate branding. The book explores and presents a  part of the open data sets from government institutions to  achieve the sustainable goals at local level, in turn  contributing towards global mission. As the topic suggests,  the authors are looking at some of the specific issues in  the areas of environment, agriculture and health care  through the lens of data science. The authors believe that  the above three areas chosen have deep relevance in todays  world. The intent is to explore these issues from a data  and analytics perspective and identify cracks through which  deeper inroads can be made. Conscious efforts have been  taken to make use of all the major data science techniques  like prediction, classification, clustering, and  correlation. Given the above background, deeper waters will  be explored through the contents of this book.},
      url = {http://library.usi.edu/record/1432774},
      doi = {https://doi.org/10.1007/978-981-33-4312-2},
}