Short note on Mining Spatiotemporal Data and Moving Objects.

 Spatiotemporal data are data that relate to both space and time. Spatiotemporal data mining refers to the process of discovering patterns and knowledge from spatiotemporal data. Typical examples of spatiotemporal data mining include discovering the evolutionary history of cities and lands, uncovering weather patterns, predicting earthquakes and hurricanes, and determining global warming trends. Spatiotemporal data mining has become increasingly important and has far-reaching implications, given the popularity of mobile services, and digital Earth, as well as satellite, RFID, sensor, wireless, and video technologies.

Among many kinds of spatiotemporal data, moving-object data (i.e., data about moving to attach equipment on wildlife to analyze ecological behavior, mobility managers embed GPS in cars to better monitor and guide vehicles, and meteorologists use weather satellites and radars to observe hurricanes. Massive-scale moving-object data are becoming sich Javary rich, complex, and ubiquitous. Examples of moving-object data mining include mining org laminate movement patterns of multiple moving objects (i.e., the discovery of relationships among s multiple moving objects such as moving clusters, leaders and followers, merge, convey, swarm, and pincer, as well as other collective movement patterns).