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advance (though not to strncmp), this complexity is unnecessary; we know the counts are right so checking for the \O wastes time Third, s t r c h r is also complex, since it must look for the character and also watch for the \O byte that terminates the message For a given call to isspam, the message is fixed, so time spent looking for the \O is wasted since we know where the message ends Finally, although strncmp, s t r c h r , and s t r l e n are all efficient in isolation, the overhead of calling these functions is comparable to the cost of the calculation they will perform It's more efficient to do all the work in a special, carefully written version of s t r s t r and avoid calling other functions altogether These sorts of problems are a common source of performance trouble-a routine or interface works well for the typical case, but performs poorly in an unusual case that happens to be central to the program at issue The existing s t r s t r was fine when both the pattern and the string were short and changed each call, but when the string is long and fixed, the overhead is prohibitive With this in mind, s t r s t r was rewritten to walk the pattern and message strings together looking for matches, without calling subroutines The resulting implementation has predictable behavior: it is slightly slower in some cases, but much faster in the spam filter and, most important, is never terrible To verify the new implementation's correctness and performance, a performance test suite was built This suite included not nly simple examples like searching for a word in a sentence, but also pathological cases such as looking for a pattern of a single x in a string of a thousand e's and a pattern of a thousand x's in a string of a single e, both of which can be handled badly by naive implementations Such extreme cases are a key part of performance evaluation The library was updated with the new s t r s t r and the sparn filter ran about 30% faster, a good payoff for rewriting a single routine Unfortunately, it was still too slow When solving problems, it's important to ask the right question Up to now, we've been asking for the fastest way to search for a textual pattern in a string But the real problem is to search for a large, fixed set of textual patterns in a long, variable string Put that way, s t r s t r is not so obviously the right solution The most effective way to make a program faster is to use a better algorithm With a clearer idea of the problem, it's time to think about what algorithm would work best The basic loop, f o r ( i = 0 ; i < npat; i++) i f (strstr(mesg, p a t [ i ] ) != NULL) return 1; scans down the message npat independent times; assuming it doesn't find any matches, it examines each byte of the message npat times, for a total of s t r l en (mesg) m p a t comparisons.

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PROMPT: Algorithm and tool for automated ontology merging nd alignment. In Proceedings 17th National Conference On Arti cial Intelligence (AAAI2000) , Austin, Texas, USA. Noy NF, Musen MA. 2003. The PROMPT suite: Interactive tools for ontology merging and mapping. International Journal of Human-Computer Studies 59(6): 983 1024. Omelayenko B. 2002. RDFT: A mapping meta-ontology for business integration. In Proceedings of the Workshop on Knowledge Transformation for the Semantic Web (KTSW 2002) at the 15th European Conference on Arti cial Intelligence, Lyon, France, pp 76 83. Rahm E, Bernstein PA. 2001. A survey of approaches to automatic schema matching. VLDB Journal: Very Large Data Bases 10(4):334 350. Scharffe F, de Bruijn J. 2005. A language to specify mappings between ontologies. In Proceedings of the Internet Based Systems IEEE Conference (SITIS05), Yandoue, Cameroon. Schnurr HP, Angele J. 2005. Do not use this gear with a switching lever! automotive industry experience with semantic guides. In 4th International Semantic Web Conference (ISWC2005), pp 1029 1040. Shvaiko P, Euzenat J. 2005. A survey of schema-based matching approaches. Journal on Data Semantics 4:146 171. Uschold M. 2000. Creating, integrating, and maintaining local and global ontologies. In Proceedings of the First Workshop on Ontology Learning (OL-2000) in conjunction with the 14th European Conference on Arti cial Intelligence (ECAI2000), Berlin, Germany. Visser PRS, Cui Z. 1998. On accepting heterogeneous ontologies in distributed architectures. In Proceedings of the ECAI98 workshop on applications of ontologies and problem-solving methods, Brighton, UK. In Visual Studio .NET Using Barcode printer for ASP .Related: Java Code 39 Generation , QR Code Generation VB.NET , Code 128 Generating C#

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