Matching tools

Pattern matching may sound dull or esoteric, but we all use an excellent, free pattern-matching tool every day—the human brain excels at just this sort of task. Babies are not taught how to recognize their mother, yet without instruction quickly learn to do so. By the time we are adults, we have become... 

These rotary tablet presses range from machines with 16-90 or more stations of matched tooling. Specific details for each manufacturer would be best obtained from the supplier s literature. 

Many of these tools have been developed through successful collaborations between researchers and industry. Clio, one of the first and most sophisticated schema matching tools, was a research prototype developed through a collaboration at IBM s Almaden Research Center and the University of Toronto [Miller et al. 2001], Clio can automatically generate a view to reformulate queries from one schema to another or transform data from one representation to another to facilitate data exchange. 

Like the previously discussed ontology matching tools, Clio proposes a semiautomatic approach and supports a visual matching representation similar to COMA++, CogZ, and OntoMapper (see Fig. 2.10). Users can draw arrows... 

Similar to MapForce, the Stylus Studio contains an XML matching tool that supports visual matching between XML, relational databases, and web service data.7 Users can drag and drop lines between source and target elements and matching... 

Finally, like the Clio project, Microsoft s BizTalk mapper8 has had both a research and commercial focus. BizTalk mapper provides similar functionality as MapForce and the matching tools in the Stylus Studio, however, work from Microsoft s Research has been incorporated to allow the matching tool to work more effectively for large schemas. 

Each of these tools uses similar visual interaction techniques as the ontology matching tools that we discussed in Sect. 3. However, there is more focus on data translation rule construction than with the ontology-related tools. In the next section, we discuss a different interaction approach, one based on creating matchings by harnessing the power of a community of users. 

The criteria for evaluation of matching tools needs to be specified. This should include usability features, technical details about what ontologies are supported, as well as criteria for evaluating the scalability of the approach. 

Another metric to measure the human effort is the human-spared resources (HSR) [Duchateau 2009]. It counts the number of designer interactions required to correct both precision and recall, i.e., to manually obtain a 100% f-measure, a quality metric that is discussed later. In other words, HSR takes into account not only the effort to validate or invalidate the discovered matches but also the effort to discover those missing. HSR is sufficiently generic, can be expresse in the range of [0,1] or in time units (e.g., seconds), and does not require any input other than the one for computing precision, recall, f-measure, or overall. The only limitation is that it does not take into account the fact that some matching tools may return the top-K matches instead of all of them. 

Precision] The precision calculates the proportion of relevant matches discovered by the matching tool with respect to all those discovered. Using the notation of Table 9.1, the precision is defined as... 

The matching benchmark XBenchMatch [Duchateau et al. 2007] and the ontology alignment API [Euzenat 2004] are based on the above metrics to evaluate the effectiveness of matching tools. They assume the availability of the expected set of matches through an expert user. Based on that set and the matches that the matching tool produces, the various values of the metrics are computed. 

A limitation of the above metrics is that they do not take into consideration any postmatch user effort, for instance, tasks that the user may need to do to guide the matching tool in the matching process, or any iterations the user may perform to verify partially generated results. 

This section describes the parameters related to similarity measures. Although they have a significant impact, parameters inside the similarity measures are often set to default values. Schema matching tools let users tune the thresholds, which is a traditional decision maker for deciding what happens to a pair of schema elements. Finally, we have detailed specific parameters that users have to understand before optimizing the matchers. In the next section, we reach one level up by studying the parameters related to the combination of similarity measures. 

Duchateau F, Bellahsene Z, Hunt E (2007) Xbenchmatch A benchmark for xml schema matching tools. In VLDB. VLDB Endowment, pp 1318-1321... 

We will now use the pattern matching tool dna-pattern (4,5) to locate... 

Program Suites Containing Pattern-Matching Tools... 

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