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• W200800914 abstract "Technology determines the types and amounts of information we can access. Currently, a large fraction of information originates in silicon. Cheap, fast chips and smart algorithms are helping digital data processing take over all sorts of information processing. Consequently, the volume of digital data surrounding us increases continuously. However, an information-centric society has additional requirements besides the availability and capability to process digital data. We should also be able to find the pieces of information relevant to a particular problem. Having the answer to a question but not being able to find it is equivalent to not having it at all. The increased volumes of information and the wide variety of data types make finding information a challenging task. Current searching methods and algorithms are based on assumptions about technology and goals that seemed reasonable before the widespread use of computers. However, these assumptions no longer hold in the context of information retrieval systems. “Good ideas do not always scale.” [Kay] This chapter presents a pattern that provides a proven solution for searching large volumes of information. The pattern originated in the information retrieval domain. However, information retrieval has expanded into other fields like office automation, genome databases, fingerprint identification, medical imaging, data mining, multimedia, etc. Since the pattern works with any kind of data, it is applicable in many other domains. You will see examples from text searching, telecommunications, stock prices, medical imaging and trademark symbols. The key idea of the pattern is to map from a large, complex problem space into a small, simple feature space. The mapping represents the creative part of the solution. Every type of application uses a different kind mapping. Mapping into the feature space is also the hard part of this pattern. Traditional searching algorithms are not viable for problems typical to the information retrieval domain. Since they were designed for exact matching, their use for similarity search is cumbersome. In contrast, feature extraction provides an elegant and efficient alternative. With information retrieval expanding into other fields, this pattern is applicable in a wide range of applications." @default.
• W200800914 created "2016-06-24" @default.
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• W200800914 date "1998-01-01" @default.
• W200800914 modified "2023-09-27" @default.
• W200800914 title "Feature Extraction—A Pattern for Information Retrieval" @default.
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