Data mining in deductive databases using query flocks |
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| Affiliation: | 1. Middle East Technical University, Department of Computer Engineering, Ankara, Turkey;2. University of Washington, The Information School, Mary Gates Hall, Room 416, Box 352840, Seattle, WA 98195, USA;1. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, Jilin, China;2. College of Geography and Environment, Shandong Normal University, Jinan, China;3. Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China;4. Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA;5. Biology Department, San Diego State University, San Diego, CA 92182, USA;6. University of the Chinese Academy of Sciences, Beijing 100049, China;7. College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China;8. Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Korea;1. School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou 730050, China;2. Gansu Engineering Research Center of Manufacturing Informationization, Lanzhou 730050, China;1. Department of Agricultural, Food and Environmental Engineering, Mendel University in Brno, Zemědělská 1, 613 00, Czech Republic;2. Department of Production Systems and Virtual Reality, Brno University of Technology, Czech Republic, Technická 2896/2, 616 69 Brno, Czech Republic;1. Department of Mathematics & Computing, Indian Institute of Technology Dhanbad, Dhanbad 826004, India;2. Department of Statistics, Pandit Deendayal Upadhyaya Adarsha Mahavidyalaya, Eraligool, Karimganj 788723, India;3. Division of Science and Technology, Beijing Normal University–Hong Kong Baptist University United International College, Zhuhai 519085, China;4. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt;1. Clinical Outcomes Solutions, Tucson, AZ, USA;2. Endpoint Outcomes, Boston, MA, USA;3. Myovant Sciences GmbH, Basel, Switzerland;4. Myovant Sciences, Inc., Brisbane, CA, USA;5. Endpoint Outcomes, Long Beach, CA, USA;6. Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA |
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| Abstract: | Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a ‘generate-and-test’ model for different kind of patterns, can also be applied to deductive databases. In this paper, query flocks technique is extended with view definitions including recursive views. Although in our system query flock technique can be applied to a data base schema including both the intensional data base (IDB) or rules and the extensible data base (EDB) or tabled relations, we have designed an architecture to compile query flocks from datalog into SQL in order to be able to use commercially available data base management systems (DBMS) as an underlying engine of our system. However, since recursive datalog views (IDB's) cannot be converted directly into SQL statements, they are materialized before the final compilation operation. On this architecture, optimizations suitable for the extended query flocks are also introduced. Using the prototype system, which is developed on a commercial database environment, advantages of the new architecture together with the optimizations, are also presented. |
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