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A cost-based optimizer can go further Suppose the system catalog contains three additional pieces of information: (1) that there are 100 rows in Table1, (2) that there are two pages in Table1, and (3) that the value 55 appears 60 times in the index for column2 Those facts change everything The equals operation will match on 60% of the rows, so it's not a narrow search And the hole table can be scanned using two page reads, whereas an index lookup would take three page reads (one to lookup in the index, two more to fetch the data later) Therefore the cost-based optimizer makes a different plan: find matching rows using a table scan Notice that the cost-based optimizer is using volatile data (the row and column values that have been inserted) and an override (that the contents are more important than the fixed assumptions) In other words, a cost-based optimizer is a rule-based optimizer that has additional, volatile information available to it so that it can override the fixed assumptions that would otherwise govern its decisions The terminology causes an impression that one optimizer type is based on rules while the other is based on cost That's unfortunate because both optimizer types use rules and both optimizer types have the goal of calculating cost The reality is that cost-based is an extension of rule-based, and a better term would have been something like "rule-based++" Most vendors claim that their DBMSs have cost-based optimizers, as you can see from Table 17-1 The claims don't mean much by themselves What's important is whether the optimizer estimates cost correctly and how it acts on the estimate In this chapter, we'll look at the actions DBMSs take to fulfill their claims Table 17-1 Cost-Based Optimizers "Explains" the Access "Updates" Statistics for the Plan Optimizer EXPLAIN RUNSTATS SET EXPLAIN UPDATE STATISTICS EXECUTE QEP optimizedb utility SELECT PLAN SET STATISTICS EXPLAIN UPDATE STATISTICS EXPLAIN ANALYZE TABLE EXPLAIN PLAN FOR ANALYZE SET SHOWPLAN ON UPDATE STATISTICS. asprise ocr c#Jan 9, 2018 · Recognize page content of a PDF as text using Tesseract and Ghostscript - OmarMuscatello/pdf-ocr. c# ocr nuget C# OCR SDK for High Performance OCR and OCR PDF Applications
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Finally, the chapter included a C# 40 introduced feature dynamic programming using .Related: C# QR Code Generating , Print Intelligent Mail C# , Interleaved 2 of 5 Generating .NET WinForms Our goal in implementing list in this fashion is to avoid creating a servant for each object Creating all those servants might be a waste of time and memory resources, mainly because it is unlikely that the client that invoked list will also invoke operations on every Thermometer that list returns Unfortunately, the fact that we must narrow each newly reated object reference to the CCS::Thermometer interface to assign it to our sequence might mean that a servant for each object gets created anyway This is because _narrow must verify that the object actually supports the interface being narrowed to Most ORBs perform narrowing. 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Skowron, and P. Synak. Market data analysis: A rough set approach. ICSResearch Reports 6/94. Warsaw University of Technology. 1994. 25. T. Beaubouef, F. E. Petry, and G. Arora. Information measures for rough and fuzzy sets and application to uncertainty in relational databases. In [258] 1999. 26. R. Bellman. Adaptive Control Processes: A Guided Tour. Princeton: Princeton University Press. 1961. 27. M. J. Beynon. An investigation of -reduct selection within the variable precision rough sets model. In Proceedings of 2nd International Conference on Rough Sets and. Encode Barcode In VS .NET Using Barcode generation .Related: PDF417 Generating ASP.NET , VB.NET EAN-8 Generating , Generate Code 39 Word set of Gabor lters spans a unique feature space for . quick response code for .net using barcode generating for web.net control to generate, create qr codes .Related: primary Gc classifications, Similarities and Comprehension, to some extent, both require the use of the narrow Gf (Continues). GTIN - 12 barcode library with .Related: ITF-14 Generator Word , Interleaved 2 of 5 Generation C# , Excel Codabar Generator dynamic reducts, 100 102 evaluation, 106 111 PASH 106 Preset, 106 QuickReduct algorithm, 87 88 proof of monotonicity, 90 relative dependency, 102 103 RSAR, 86 algae population estimation, 126 128 applications, 113 medical image classi cation, 113 117 optimizations, 91 95 text categorization, 117 126 RSAR-SAT, 279 283 tolerance approach, 103, 275 approximations, 104 similarity, 103 tolerance QuickReduct, 104 105 variable precision rough set theory, 98 100 Rough set theory, 25, 133 applications, 128 132 bioinformatics and medicine, 129 130 fault diagnosis, 130 nancial investment, 129 music and acoustics, 131 132 pattern classi cation, 131 prediction of business failure, 128 approximations, 28 boundary region, 28 core, 31 degree of dependency, 29 discernibility function, 32, 280 281 discernibility matrix, 31, 280 281 feature selection, see Rough set-based feature selection indiscernibility, 27 introduction, 25 motivations, 4 5 negative region, 28 positive region, 28. of heat along an infinitely narrow pipe) Initially . to different temperatures, which will remain fixed throughout the . 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Table 8-4: Feature changes in ADO.NET Feature multiple tables ADO multiple .Related: dynamic reducts, 100 102 evaluation, 106 111 PASH, 106 Preset, 106 QuickReduct algorithm, 87 88 proof of monotonicity, 90 relative dependency, 102 103 RSAR, 86 algae population estimation, 126 128 applications, 113 medical image classi cation, 113 117 optimizations, 91 95 text categorization, 117 126 RSAR-SAT, 279 283 tolerance approach, 103, 275 approximations, 104 similarity, 103 tolerance QuickReduct, 104 105 variable precision rough set theory, 98 100 Rough set theory, 25, 133 applications, 128 132 bioinformatics and medicine, 129 130 fault diagnosis, 130 nancial investment, 129 music and acoustics, 131 132 pattern classi cation, 131 prediction of business failure, 128 approximations, 28 boundary region, 28 core, 31 degree of dependency, 29 discernibility function, 32, 280 281 discernibility matrix, 31, 280 281 feature selection, see Rough set-based feature selection indiscernibility 27 introduction, 25 motivations, 4 5 negative region, 28 positive region, 28 Encode Barcode In Java Using Barcode creator for Java . 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The N (x, y) locations (and the spatial frequency and spatial extent parameters of the N Gabor wavelets used at these locations) are chosen by a GA, with a tness function that takes into account the ability of each Gabor feature detector to istinguish one face from all the other faces in the database. barcode library in .net using barcode generation for .net control to generate, create qr bidimensional .Related: UCC-128 In VS NET Using Barcode printer for ASP In Visual Studio NET Using Barcode drawer for . abouthtml file. Encode USS Code 39 In Java Using Barcode generation for Java Control to generate, create Code-39 image in Java applications. Every feature and .Related: Create ISBN Java , UPC-A Generator C# , Print EAN-13 ASP.NET Aspose.Pdf. C#. VB.NET. Aspose.Slides. C#. VB.NET. Aspose.Pdf.Kit. C#. VB.NET. Aspose.Tasks. C#. VB.NET. Aspose.BarCode. C#. VB.NET. Aspose.Diagram. .Related: simple ocr c# OCR in PDF Using Tesseract Open - Source Engine | Syncfusion Blogs
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