A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and nonspatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

Operator scheduling strategy for LBS based intelligent transportation system

With the development of location technologies, advanced LBSbased ITS increasingly requires the capability of database technologies to manage the continuously arrived vehicles' location, traffic jam and other interrelated information of large amounts of traffic in the following years. And some burst arrival stream data will challenge the realtime performance and the allocation of limited resource. However, choosing a desirable database operator scheduling strategy can significantly improve the performance of the system. The path capability strategy was chosen and improved as ITS' operator scheduling strategy to meet the realtime response and the minimal memory requirement of the system. 