Abstract

Ahstract-Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to busi ness intelligence. These monitoring applications submit complex queries to track sequences of events that match a given pattern.As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of flat sequence queries only. To assure real-time responsiveness and scalability for pattern detection even on huge volume high-speed streams, efficient processing techniques must be designed. In this paper, we first analyze the prevailing nested pattern query processing strategy and identify several serious shortcomings. Not only are substantial subsequences first constructed just to be subse quently discarded, but also opportunities for shared execution of nested subexpressions are overlooked. As foundation, we introduce NEEL, a CEP query language for expressing nested CEP pattern queries composed of sequence, negation, AND and OR operators. To overcome deficiencies, we design rewriting rules for pushing negation into inner subexpressions. Next, we devise a normalization procedure that employs these rules for flattening a nested complex event expression. To conserve CPU and memory consumption, we propose several strategies for efficient shared processing of groups of normalized NEEL subexpressions. These strategies include prefix caching, suffix clustering and customized "bit-marking" execution strategies. We design an optimizer to partition the set of all CEP sub expressions in a NEEL normal form into groups, each of which can then be mapped to one of our shared execution operators. Lastly, we evaluate our technologies by conducting a performance study to assess the CPU processing time using real-world stock trades data. Our results confirm that our NEEL execution in many cases performs 100 fold faster than the traditional iterative nested execution strategy for real stock market query workloads.

Abstract-Sign Language, the natural communication medium for a deaf person, is difficult to learn for the general population. The prospective signer should learn specific hand gestures in coordination with head motion, facial expression and body posture. Since language learning can only advance with continuous practice and corrective feedback, we have developed an interactive system, called SignTutor, which automatically evaluates users' signing and gives multimodal feedbacks to guide them to improve their signing. SignTutor allows users to practice instructed signs and to receive feedback on their performance. The system automatically evaluates sign instances by multimodal analysis of the hand and head gestures. The time and gestural variations among different articulations of the signs are mitigated by the use of hidden Markov models. The multimodal user feedback consists of a text-based information on the sign, and a synthesized version of the sign on an avatar as a visual feedback.. We have observed that the system has a very satisfactory performance, especially in the signer -dependent mode, and that the user experience is very positive.

Abstract-Cloud computing has emerged as a new computing paradigm that impacts several different research fields, including software testing. Testing cloud applications has its own peculiarities that demand for novel testing methods and tools. On the other hand, cloud computing also facilitates and provides opportunities for the development of more effective and scalable software testing techniques. This paper reports on a systematic survey of published results attained by the synergy of these two research fields. We provide an overview regarding main contributions, trends, gaps, opportunities, challenges and possible research directions. We provide a review of software testing over the cloud literature and categorize the body of work in the field.

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