A Study of Data Mining In Science and Engineering |

ABSTRACT:

With the rapid development of computer and informationtechnology in the last several decades, an enormous amount of data in scienceand engineering has been and will continuously be generated in massive scale,either being stored in gigantic storage devices or following into and out ofthe system in the form of data streams. Moreover, such data has been madewidely available, e.g., via the Internet. Such tremendous amount of data, inthe order of tera- to peta-bytes, has fundamentally changed science andengineering, transforming many disciplines from data-poor to increasingly data-rich,and calling for new, data-intensive methods to conduct research in science andengineering. In this paper, we discuss the research challenges inscience and engineering, from the data mining perspective, with a focus on thefollowing issues: (1) informationnetwork analysis, (2) discovery,usage, and understanding of patterns and knowledge, (3) stream data mining, (4) mining moving object data, RFID data, anddata from sensor networks, (5) spatiotemporaland multimedia data mining, (6) miningtext, Web, and other unstructured data, (7) data cube-oriented multidimensional online analytical mining,(8) visual data mining, and (9)data mining by integration ofsophisticated scientific and engineering domain knowledge.