The Web Services standard is becoming the lingua franca for loosely coupled distributed applications. As the number of nodes and the complexity of these applications grow over the coming years, it will become more challenging for developers to understand, debug, and optimize them. In this paper, we describe Web Services Navigator, a visualization tool that fosters better understanding of serviceoriented architecture (SOA) applications. We draw on our experience with real SOA applications to show how this tool has been applied to practical problems ranging from business logic misunderstandings to performance bottlenecks to syntax and semantic errors. Web Services Navigator helps to solve these problems by visualizing how applications really execute, enabling business owners, application designers, project managers, programmers, and operations staff to understand how their applications actually behave. We sketch the architecture of Web Services Navigator, outline how it reconstructs application execution from event logs, and describe how users interactively explore their applications using its five linked views. INTRODUCTIONSignificant portions of the productivity gains enjoyed by businesses over the past decades are attributable to the adoption of new information technology (IT). At some point the economic balance shifts; businesses start putting more emphasis on reducing the cost of supporting existing IT functions than on adding new function. Today, many businesses are striving to improve the overall cost-effectiveness of their IT investments by reviewing business needs and cutting costs. These efforts typically include leveraging existing assets, consolidating redundancies, and laying a foundation for future growth. This trend is fueling the move from tightly coupled componentized systems to loosely coupled service-based systems, such as those based on service-oriented architectures (SOAs) employing standards-based interfaces. 1,2 To illustrate the differences between componentized systems and service-based systems, we make an analogy with the air transportation industry. This industry moves passengers arriving by means of ground transportation into airplanes, flies them to a Ó
With the explosion of available data mining algorithms, a method for helping user to select the most appropriate algorithm or combination of algorithms to solve a given problem and reducing users’ cognitive overload due to the overloaded data mining algorithms is becoming increasingly important. This chapter presents a meta-learning approach to support users automatically selecting most suitable algorithms during data mining model building process. The authors discuss the meta-learning method in detail and present some empirical results that show the improvement that can be achieved with the hybrid model by combining meta-learning method and Rough Set feature reduction. The redundant properties of the dataset can be found. Thus, the ranking process can be sped up and accuracy can be increased by using the reduct of the properties of the dataset. With the reduced searching space, users’ cognitive load is reduced.
The explosion in the amount of available data on any given subject has led researchers to the area of knowledge discovery and data mining. The main motivation of these research areas is that humans are not capable of analyzing the current size of the available data either manually or with basic statistical methods. As a result, the technological challenge of performing everything automatically has dominated the interests of researchers and developers. Thus, data mining was established as a methodology for extracting potentially useful information from very large amounts of data. To deal with different complicated data, scientists have developed numerous data mining algorithms.
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