Introduction: Good system architecture will facilitate the data mining system to make best use of the software environment, accomplish data mining tasks in an efficient and timely manner, interoperate and exchange information with other information systems, be adaptable to users’ diverse requirements, and evolve with time.
A critical question in the design of a data mining (DM) system is how to integrate or couple the DM system with a database (DB) system and/or a data warehouse (DW) system. If a DM system works as a stand-alone system or is embedded in an application program, there are no DB or DWsystems with which it has to communicate. This simple scheme is called no coupling, where the main focus of the DM design rests on developing effective and efficient algorithms for mining the available data sets. However, when a DM systemworks in an environment that requires it to communicate with other information system components, such as DB and DWsystems, possible integration schemes includeno coupling, loose coupling, semitight coupling, and tight coupling. We examine each of these schemes, as follows:
No coupling: No coupling means that a DM system will not utilize any function of a DB or DW system. It may fetch data from a particular source (such as a file system), process data using some data mining algorithms, and then store the mining results in another file.
Such a system, though simple, suffers from several drawbacks. First, a DB system provides a great deal of flexibility and efficiency at storing, organizing, accessing, and processing data.Without using a DB/DWsystem, a DM system may spend a substantial amount of time finding, collecting, cleaning, and transforming data. In DB and/or DWsystems, data tend to be well organized, indexed, cleaned, integrated, or consolidated, so that finding the task-relevant, high-quality data becomes an easy task. Second, there are many tested, scalable algorithms and data structures implemented in DB and DWsystems. It is feasible to realize efficient, scalable implementations using such systems. Moreover, most data have been or will be stored in DB/DW systems. Without any coupling of such systems, a DM system will need to use other tools to extract data, making it difficult to integrate such a system into an information processing environment. Thus, no coupling represents a poor design.
Loose coupling: Loose coupling means that a DM system will use some facilities of a DB or DW system, fetching data from a data repository managed by these systems, performing data mining, and then storing the mining results either in a file or in a designated place in a database or data warehouse.
Loose coupling is better than no coupling because it can fetch any portion of data stored in databases or data warehouses by using query processing, indexing, and other system facilities. It incurs some advantages of the flexibility, efficiency, and other features provided by such systems. However, many loosely coupled mining systems are main memory-based. Because mining does not explore data structures and query optimization methods provided by DB or DW systems, it is difficult for loose coupling to achieve high scalability and good performance with large data sets. Semitight coupling: Semitight coupling means that besides linking a DM system to a DB/DW system, efficient implementations of a few essential data mining primitives (identified by the analysis of frequently encountered data mining functions) can be provided in the DB/DW system. These primitives can include sorting, indexing, aggregation, histogram analysis, multiway join, and pre computation of some essential statistical measures, such as sum, count, max, min, standard deviation, and so on. Moreover, some frequently used intermediate mining results can be precomputed and stored in the DB/DW system. Because these intermediate mining results are either precomputed or can be computed efficiently, this design will enhance the performance of a DM system.
Tight coupling: Tight coupling means that a DM system is smoothly integrated into the DB/DW system. The data mining subsystem is treated as one functional component of an information system. Data mining queries and functions are optimized based on mining query analysis, data structures, indexing schemes, and query processing methods of a DB or DW system. With further technology advances, DM, DB, and DW systems will evolve and integrate together as one information system with multiple functionalities. This will provide a uniform information processing environment.
This approach is highly desirable because it facilitates efficient implementations of data mining functions, high system performance, and an integrated information processing environment.With this analysis, it is easy to see that a data mining systemshould be coupled with a DB/DWsystem. Loose coupling, though not efficient, is better than no coupling because it uses both data and system facilities of a DB/DW system. Tight coupling is highly desirable, but its implementation is nontrivial and more research is needed in this area. Semitight coupling is a compromise between loose and tight coupling. It is important to identify commonly used data mining primitives and provide efficient implementations of such primitives in DB or DWsystems.
