SCA (Service Component Architecture) is a collaborative effort by the leading vendors in the enterprise software space to define an architecture for building applications and systems using a Service-Oriented Architecture. SCA allows developers to define components that implement business logic, which offer their capabilities through services and consume functions offered by other components through references in a relatively abstract manner. This paper discusses how infrastructure and Quality of Service constraints and capabilities can be associated with SCA artifacts either as abstract desires or as concrete policies.
In academic settings over the last decade, there has been significant progress in time series classification. However, much of this work makes assumptions that are simply unrealistic for deployed industrial applications. Examples of these unrealistic assumptions include the following: assuming that data subsequences have a single fixed-length, are precisely extracted from the data, and are correctly labeled according to their membership in a set of equalsize classes. In real-world industrial settings, these patterns can be of different lengths, the class annotations may only belong to a general region of the data, may contain errors, and finally, the class distribution is typically highly skewed. Can we learn from such weakly labeled data? In this work, we introduce SDTS, a scalable algorithm that can learn in such challenging settings. We demonstrate the utility of our ideas by learning from diverse datasets with millions of datapoints. As we shall demonstrate, our domain-agnostic parameter-free algorithm can be competitive with domain-specific algorithms used in neuroscience and entomology, even when those algorithms have been tuned by domain experts to incorporate domain knowledge.
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