Research Challenges for Data Mining in Science and Engineering∗

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