By Li Yan, Zongmin Ma
Databases are designed to help facts garage, processing, and retrieval actions concerning info administration. using databases in quite a few functions has led to a major wealth of information, which populates many varieties of databases round the world.Advanced Database question structures: strategies, purposes and applied sciences makes a speciality of applied sciences and methodologies of database queries, XML and metadata queries, and purposes of database question structures, aiming at supplying a unmarried account of applied sciences and practices in complex database question structures. This e-book offers the cutting-edge details for teachers, researchers and practitioners who're attracted to the research, use, layout and improvement of complex and rising database queries with final goal of creating knowledge for exploiting the possibilities of the knowledge and information society.
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Additional resources for Advanced Database Query Systems: Techniques, Applications and Technologies
If all these general queries succeed, a query that fails produces a genuine null. Furthermore, assume q′ and q′′ are both relaxations of the failed query q, but q′′ is more general than q′. , the best the system could do to satisfy the initial query q). If both fail, then the erroneous presupposition indicated by q′′ is stronger. This leads to the conclusion that only Minimal Generalizations that Succeed (MGSs) and maXimal Generalizations that Fail (XGFs) are significant. Indeed, XGFs provide explanation for the failure and some assistance for relaxing the query into a non-failing query, whereas MGSs produce alternative answers to the failing query.
Note that most existing decision tree algorithms choose the partition that maximizes information gain. Information gain is the reduction of entropy due to a partition and is represented in the following formula, IGain(t, t1, t2 ) = E (t ) − N1 N E (t1 ) − 2 E (t2 ) N N (11) Thus a partition with a high information gain will generate a tree with a low entropy. And, this tree will have short root-to-leaf paths as well. Since the cost of visiting intermediate nodes equals the product of path lengths and fan-out in Definition 2, if we assume the average fan-out is about the same for all trees, then the cost of visiting intermediate nodes is proportional to the length of root-to-leaf paths.
1007/ BF00116251 Quinlan, J. R. (1993). 5: Programs for machine learning. San Francisco: Morgan Kaufmann Publishers Inc. , & Pavlaki, V. (2005). Towards a context-aware relational model. Proceedings of the International Workshop on Context Representation and Reasoning, Paris, (pp. 101-106). , Huang, T. , & Merhotra, S. (1997). Content-based image retrieval with relevance feedback in MARS. Proceedings of the IEEE International Conference on Image Processing, (pp. 815-818). , & Zhai, C. (2005). Context-sensitive information retrieval using implicit feedback.