A landmark work on big data by two brother academicians
Website news (Correspondent: Cai Liefei) About 80% of all our learning, working and living activities are related to locations in space, the spatial data generated therein has opened the door to the big data era. The problem faced by researchers over the world is how to realize the value of big data. The treatise by Li Deren et al. may have provided a solution.
Theory and Practice of Spatial data mining is a collaboration between Li Deren, Li Deyi (both academicians at Chinese Academy of Sciences) and Prof. Wang Shuliang. Published by Chinese Science Press, it has recently become one of the winners in the fifth Excellent Publication of China Award. This award from Chinese Association of Publishers is given once every two years, and is considered one of the three greatest awards in the field along with Five Ones Program Award, and Government Award of Chinese Publication.
Academician Li Deren is a scientist of surveying and remote sensing. His brother, Academician Li Deyi is a renowned expert on artificial intelligence. Prof. Wang Shuliang is a student of them both. Awed by the potential of spatial data mining, the authors have led their team to do groundbreaking research, publishing the first and second editions of the treatise. The first and second editions received the support of National Science and Technology Publication Fund and National Publication Fund respectively, and are named as Key Book Publications of the Twelfth Five-year Plan.
The treatise proposed the data field, cloud model, rough geoscience space perspective on spatial data mining, examined the mechanism and data sources of spatial data, and obtained an iterative method with variable weights for the clarification of spatial observation data. Based on the rules of association, distribution, summarization and aggregation in the geographical information system space, the method enables the discovery of spatial knowledge from images that can guide image classification, characteristic extraction, and expression identification, as well as datamining from the temporal spatial distribution of video data. Practical results have been obtained using these theories, including the team’s own prototype spatial data mining system GISDBMiner and RSImageMiner.
We have been informed that this edition of the treatise has deepened the examination of the issues, and incorporated the latest advances. Despite its smaller word count, the actual amount of knowledge has increased. The authors have spent years working on its manuscript, and are welcome to criticism and suggestions.
According to book editors at Science Press, the academicians have effectively utilized geoscience and artificial intelligence in spatial data mining, creating a work of cross-disciplinary worth. The book is a landmark of spatial data mining, rich and deep in content, meticulous in its research method, and inspiring in the authors’ humility.