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The following lists several areas for further exploration. They represent the types of activities you might want to conduct as experiments (or as a class assignment) to explore relational database technologies. 1. The query in Figure 10-1 exposes a design flaw in one of the tables. What is it Does the flaw violate any of the normal forms If so, which one 2. Explore the TABLE structure and change the SELECT DBXP stub to return information about the table and its fields. 3. Change the EXPLAIN SELECT DBXP command to produce an output similar to the MySQL EXPLAIN SELECT command. 4. Modify the build_query_tree function to identify and process the LIMIT clause. 5. How can the query tree query_node structure be changed to accommodate HAVING, GROUP BY, and ORDER clauses



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Buildfile: file:///ErrorHandlingExample.xml Target(s) specified: DoomedToFail DoomedToFail: GiveUp: [echo] I give up, I really do. CleanUp1: [echo] Doing CleanUp1... [call] ErrorHandlingExample.xml(20,7): [call] No message. BUILD FAILED - 1 non-fatal error(s), 0 warning(s) ErrorHandlingExample.xml(8,7): This target is not going to work Total time: 0 seconds. Output completed (1 sec consumed) - Normal Termination We can see in the output that following the second failure (in target CleanUp1) halts the script execution immediately; target CleanUp2 is never called. The net result of this is that there is some utility to the error handling in NAnt but not to the extent that it provides robust rollbacks, transaction reversals, and so on. We will see a suitable use of the onfailure property when we look at the testing part of the build script for the Transformer application in just a little while. So, after a pessimistic start to proceedings, let us move on to the real work.





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13 Nov 2014 ... Combined the OCR Professional Engine, you can easily create a document workflow of scanning documents and read text from images in your web application. ... Below we will show some code snippets of doing TWAIN scanning and client-side OCR in JavaScript using Dynamic Web TWAIN.

he query tree class shown in 10 forms the starting point for building the experimental query optimization and execution engine for DBXP. In this chapter, I ll show you how to add the optimizer to the query tree class. I ll begin by explaining the rationale for the heuristics (or rules) used in the optimizer, and then jump into writing the code. Because the code for some of the functions is quite lengthy, the code examples shown in this chapter are excerpts. If you are following along by coding the examples, I recommend you download the source code for this chapter instead of typing in the code from scratch.

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Opportunities for Refactoring 127 Refactoring Settings 128 Creating the Deploy File 128 Core Settings 129 selectversion 129 get 130 selectenvironments 130 createenvironments 130 position 130 configure 131 notify 131 fail 131 help 131 Examining the Output 132 Summary 133 Further Reading 133.

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The first query optimizers were designed for use in early database systems such as System R1 and INGRES.2 These optimizers were developed for a particular implementation of the relational model and have stood the test of time as illustrations for how to implement optimizers. Many of the commercially available database systems are based on these works. Since then, optimizers have been created for extensions of the relational model to include object-oriented and distributed database systems. One example is the Volcano optimizer, which uses a dynamic programming algorithm to generate query plans for cost-based optimization in object-oriented database systems. Another example is concerned with how to perform optimization in heterogeneous database systems (similar to distributed systems, but there is no commonly shared concept of organization). In these environments it is possible to use statistical methods for deriving optimization strategies. Another area in which the requirements for query optimization generate unique needs is that of memory-resident database systems. Memory-resident database systems are designed to contain the entire system and all of the data in the computer s secondary memory (i.e., disk). While most of these applications are in the area of embedded systems, some larger distributed systems that consist of a collection of systems use memory-resident databases to expedite information flow. Optimization in memory-resident database systems requires faster algorithms

1. P G. Selinger, M. M. Astraham, D. D. Chamberlin, R. A. Lories, and T. G. Price. 1979. Access Path Selection in . a Relational Database Management System. Proceedings of the ACM SIGMOD International Conference on the Management of Data, Aberdeen, Scotland: 23 34. Considered by some to be the Bible of Query Optimization. 2. M. Stonebraker, E. Wong, P Kreps. 1976. The Design and Implementation of INGRES. ACM Transactions on . Database Systems 1(3): 189 222.

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