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primary goal of the Apache/Perl integration project was to bring the full power of the Perl programming language into the Apache server This resulted in the development of mod_perl, which you can compile and link together with Apache and Perl to provide an object-oriented Perl interface to the server s C language API This enables Perl programmers to write Apache modules in Perl An Apache-Perl module may step in during the handler, header parser, URI translation, authentication, authorization, access, type check, fix-up, logger, and cleanup stages of a request The mod_perl module is widely used with Apache to create dynamic contents mod_perl is a very efficient way to use Perl with Apache; you no longer have to run Perl-based CGI scripts, which are slow, resource consuming, and often not suitable for sites that receive a large number of simultaneous requests per second This chapter discusses how to compile, install, and configure Apache for mod_perl, how to run your old CGI scripts by using mod_perl, and how to develop mod_perl modules to take advantage of the Apache API

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Compiling and installing mod_perl Running CGI scripts via mod_perl Writing a mod_perl module Preloading modules Sharing memory Writing a database application in

Table F-2 (continued)

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5224 Experimental Results The aim of the following experiments is to gauge the implementation s performance when operating on worst-case and average-case scenarios The worst-case scenario is one in which all remaining attributes have to be examined because they provide almost identical gains Given a dataset with n conditional attributes, is evaluated n + (n 1) + + 2 + 1 = n (1 + n) times, a O(n2 ) time complexity Evaluating 2 involves processing each dataset item using check-discernibility clearly, O(l) with respect to the number of dataset items The average case is described as a dataset in which high-signal and noise/redundant attributes are homogeneously mixed in a dataset The best, nontrivial case is one where high signal attributes are the rst to be chosen by the algorithm For brevity and because of its simplicity, the last case is not addressed here.

Here is how you can download, compile and install mod_perl on your system: 1 Download the latest version of the mod_perl source into the /usr/local/src directory from the http://perl apacheorg/dist site or from an official mirror site

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Please make sure you read the installation notes and README files in the source distribution before proceeding with the compilation The steps discussed below might change with newer version of mod_perl and/or newer Apache

Attribute dhcpRanges dhcpReservations dhcpServers dhcpSites dhcpState dhcpSubnets dhcpType dhcpUniqueKey dhcpUpdateTime directReports displayName displayNamePrintable distinguishedName dITContentRules division dMDLocation dmdName dNReferenceUpdate dnsAllowDynamic dnsAllowXFR dNSHostName dnsNotifySecondaries dNSProperty dnsRecord dnsRoot dnsSecureSecondaries dNSTombstoned domainCAs domainCrossRef domainID domainIdentifier Syntax Print Case String Print Case String Print Case String Print Case String Print Case String Print Case String Integer Large Integer Large Integer Distinguished Name Unicode String Print Case String Distinguished Name Unicode String Unicode String Distinguished Name Unicode String Distinguished Name Boolean Boolean Unicode String Integer Octet String Octet String Unicode String Integer Boolean Distinguished Name Distinguished Name Distinguished Name Integer

2 As root, extract the mod_perl source distribution by using the tar xvzf mod_perl-versiontargz command This section assumes that you extracted the Apache source distribution into /usr/local/src 3 Change to the /usr/local/src/mod_perl-version directory and run:

Experimental results were obtained by running different versions of the improved RSAR on arti cially constructed datasets with varying dimensionality and number of samples to gauge ef ciency and complexity To smooth out timing errors, numerous runs were performed and averaged Approximate curves are shown interpolating the experimental data The worst-case runtimes are shown in Figure 54, where RSAR performed FS on pure binary noise (each attribute is one bit of noise) The rst graph shows the results of varying the number of attributes (with 20 dataset items); the second graph plots runtime over the number of dataset items (with 20 attributes) O(n2 ) and O(l) complexities are obvious As expected, QuickReduct V is the best choice for this type of problem Two different average-case experiments were conducted.

Perl MakefilePL APACHE_SRC=/apache-version \ DO_HTTPD=1 \ USE_APACHE=1 \ PERL_MARK_WHERE=1 \ EVERYTHING=1

Attribute domainPolicyObject domainPolicyReference domainReplica domainWidePolicy driverName driverVersion dSASignature dSCorePropagationData dSHeuristics dSUIAdminMaximum dSUIAdminNotication dSUIShellMaximum dynamicLDAPServer eFSPolicy employeeID employeeNumber employeeType enabled enabledConnection enrollmentProviders extendedAttributeInfo extendedCharsAllowed extendedClassInfo extensionName facximileTelephoneNumber fileExtPriority flags flatName forceLogoff foreignIdentifier friendlyNames

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