Data Mining_ Concepts and Techniques - Jiawei Han [376]
■ Scalable and interactive data mining methods: In contrast with traditional data analysis methods, data mining must be able to handle huge amounts of data efficiently and, if possible, interactively. Because the amount of data being collected continues to increase rapidly, scalable algorithms for individual and integrated data mining functions become essential. One important direction toward improving the overall efficiency of the mining process while increasing user interaction is constraint-based mining. This provides users with added control by allowing the specification and use of constraints to guide data mining systems in their search for interesting patterns and knowledge.
■ Integration of data mining with search engines, database systems, data warehouse systems, and cloud computing systems: Search engines, database systems, data warehouse systems, and cloud computing systems are mainstream information processing and computing systems. It is important to ensure that data mining serves as an essential data analysis component that can be smoothly integrated into such an information processing environment. A data mining subsystem/service should be tightly coupled with such systems as a seamless, unified framework or as an invisible function. This will ensure data availability, data mining portability, scalability, high performance, and an integrated information processing environment for multi-dimensional data analysis and exploration.
■ Mining social and information networks: Mining social and information networks and link analysis are critical tasks because such networks are ubiquitous and complex. The development of scalable and effective knowledge discovery methods and applications for large numbers of network data is essential, as outlined in Section 13.1.2.
■ Mining spatiotemporal, moving-objects, and cyber-physical systems: Cyber-physical systems as well as spatiotemporal data are mounting rapidly due to the popular use of cellular phones, GPS, sensors, and other wireless equipment. As outlined in Section 13.1.3, there are many challenging research issues realizing real-time and effective knowledge discovery with such data.
■ Mining multimedia, text, and web data: As outlined in Section 13.1.3, mining such kinds of data is a recent focus in data mining research. Great progress has been made, yet there are still many open issues to be solved.
■ Mining biological and biomedical data: The unique combination of complexity, richness, size, and importance of biological and biomedical data warrants special attention in data mining. Mining DNA and protein sequences, mining high-dimensional microarray data, and biological pathway and network analysis are just a few topics in this field. Other areas of biological data mining research include mining biomedical literature, link analysis across heterogeneous biological data, and information integration of biological data by data mining.
■ Data mining with software engineering and system engineering: Software programs and large computer systems have become increasingly bulky in size sophisticated in complexity, and tend to originate from the integration of multiple components developed by different implementation teams. This trend has made it an increasingly challenging task to ensure software robustness and reliability. The analysis of the executions of a buggy software program is essentially a data mining process—tracing the data generated during program executions may disclose important patterns and outliers that could lead to the eventual automated discovery of software bugs. We expect