Data Mining_ Concepts and Techniques - Jiawei Han [358]
Mining Spatial Data
Spatial data mining discovers patterns and knowledge from spatial data. Spatial data, in many cases, refer to geospace-related data stored in geospatial data repositories. The data can be in “vector” or “raster” formats, or in the form of imagery and geo-referenced multimedia. Recently, large geographic data warehouses have been constructed by integrating thematic and geographically referenced data from multiple sources. From these, we can construct spatial data cubes that contain spatial dimensions and measures, and support spatial OLAP for multidimensional spatial data analysis. Spatial data mining can be performed on spatial data warehouses, spatial databases, and other geospatial data repositories. Popular topics on geographic knowledge discovery and spatial data mining include mining spatial associations and co-location patterns, spatial clustering, spatial classification, spatial modeling, and spatial trend and outlier analysis.
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 phones, GPS devices, Internet-based map services, weather 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 objects) are especially important. For example, animal scientists attach telemetry 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 rich, complex, and ubiquitous. Examples of moving-object data mining include mining movement patterns of multiple moving objects (i.e., the discovery of relationships among multiple moving objects such as moving clusters, leaders and followers, merge, convoy, swarm, and pincer, as well as other collective movement patterns). Other examples of moving-object data mining include mining periodic patterns for one or a set of moving objects, and mining trajectory patterns, clusters, models, and outliers.
Mining Cyber-Physical System Data
A cyber-physical system (CPS) typically consists of a large number of interacting physical and information components. CPS systems may be interconnected so as to form large heterogeneous cyber-physical networks. Examples of cyber-physical networks include a patient care system that links a patient monitoring system with a network of patient/medical information and an emergency handling system; a transportation system that links a transportation monitoring network, consisting of many sensors and video cameras, with a traffic information and control system; and a battlefield commander system that links a sensor/reconnaissance network with a battlefield information analysis system. Clearly, cyber-physical systems and networks will be ubiquitous and form a critical component of modern information infrastructure.
Data generated in cyber-physical systems are dynamic, volatile, noisy, inconsistent, and interdependent, containing rich spatiotemporal information, and they are critically important for real-time decision making. In comparison with typical spatiotemporal data mining, mining cyber-physical data requires linking the current situation with a large information base, performing real-time calculations, and returning prompt responses. Research in the area includes rare-event detection and anomaly analysis in cyber-physical data streams, reliability and trustworthiness in cyber-physical data analysis,