Asian Journal of Information Technology 4 (5): 466-470, 2005 © Grace Publications, 2005 466 A Survey of Data Mining Query Language (DMQL) for Multimedia Databases Bilal A. H. Abul-Huda, Basel Bani Ismail, Osama Al-Horani and Ahmad El-Mustafah Department of Computer Information System, College of Information Technology and Computer Science, Yarmouk University, P.O. Box 4762, 21163 Irbid Jordan Abstract: Data Mining is the process of discovering interesting knowledge (patterns) from large amounts of data stored either in databases, data warehouses, or other information repositories. Multimedia data mining is the mining of high-level multimedia information and knowledge from large multimedia databases. Mining multimedia data is, however, at an experimental stage. Substantial progress in the field of data mining and data warehousing research has been witnessed in the last few years. Numerous research and commercial systems for data mining and data warehousing have been developed for mining knowledge in relational database and data warehouses (Fayyad et al., 1996) Despite the fact that Multimedia has been the major focus for many researchers around the world, data mining from multimedia databases is still in its infancy, multimedia mining still seem shy on results. Many techniques for representing, storing, indexing and retrieving multimedia data have been proposed. However, rare are the researches who ventured in the multimedia data mining field. The emerging data mining tools and systems lead to the demand of a powerful data mining query language. The concepts of such a language for relational databases are discussed in (Han et al., 1996) With the increasing popularity of multimedia databases, it is important to design a data mining query language for such databases. A multimedia data mining system prototype, MultiMediaMiner, has been designed and developed. It includes the construction of a multimedia data cube which facilitates multiple dimensional analysis of multimedia data, primarily based on visual content and the mining of multiple kinds of knowledge, including characterization (summarization), discrimination (comparison), classification, association and clustering, in image and video databases. Key words: Data mining, Data mining query language, Knowledge discovery, Data warehousing, Data cube, Multimedia database and Information retrieval Introduction Data Mining means the discovery of knowledge and useful information from the large amounts of data stored in databases. Data Mining, involves an integration of techniques from multiple disciplines such as: database technology, statistics, machine learning, neural networks, information retrieval. The desired feature of data mining systems is the ability to support ad hoc and interactive data mining in order to facilitate flexible and effective knowledge discovery. Data mining query language can be design to support such a feature. The importance of the design of a good data mining query language can also be seen from observing the history of relational database system. There is a lot of research that has been conducted on data mining in relational databases to mine a specific kind of knowledge. Also, there are some data mining experimental systems that have been developed for relational databases, such as DBMiner, Explora, MineSet, Quest, etc (Elfeky et al., 2000) It is reasonably easy to design a data mining language for data mining in relational databases. It is a great challenge to design language for knowledge mining in other kinds of databases, such as transactional databases, object-oriented databases, spatial databases, multimedia databases (Han et al., 1996). Multimedia data mining is a subfield of data mining that deals with the extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia databases. Multimedia data mining is not limited to images, video or sound, but encompasses text mining as well. There has been interesting research in text mining from text documents and Web or semi-structured data querying and mining. The availability of affordable imaging technology is leading to an explosion of data in the forms of image and video. Many relational databases are now including multimedia information, such as photos of customers, videos about real estate, etc. The proliferation of huge amounts of multimedia data is becoming prominent. Global information networks like the Internet, as well as specialized databases, are filled with a variety of multimedia, medical images, satellite pictures, etc., necessitating means to retrieve, classify and understand this data. With huge amounts of multimedia data collected by video cameras and audio recorders, satellite telemetry systems, remote sensing systems, surveillance cameras and other data collection tools, it is crucial to develop tools for discovery of interesting knowledge from large multimedia databases. Moreover, with the popularity of multimedia