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
Rapid growth in the capture and generation of images and videos is driving the need for more efficient and effective systems for analyzing, searching, and retrieving this data. Specific challenges include supporting automatic content indexing at a large scale and accurately extracting a sufficiently large number of relevant semantic concepts to enable effective search. In this paper, we describe the development of a system for massive-scale visual semantic concept extraction and learning for images and video. The system models the visual semantic space using a hierarchical faceted classification scheme across objects, scenes, people, activities, and events and utilizes a novel machine learning approach that creates ensemble classifiers from automatically extracted visual features. The ensemble learning and extraction processes are easily parallelizable for distributed processing using Hadoop A and IBM InfoSphere A Streams, which enable efficient processing of large data sets. We report on various applications and quantitative and qualitative results for different image and video data sets.
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