How Can Transactional Semantics Enhance the Commit Rate of Context-aware Service Composition in Advanced Pervasive Systems?

Context-aware composition of services exhibiting transactional properties poses several challenges. A major challenge is the transactional behavior of candidate services which is subject to perpetual change while the composition is running. Compositions of services displaying transactional properties must be dynamically adapted at run time to cope with context fluctuations. By dynamic adaptation, we refer to the ability to alter the composition behavior in response to changes affecting its execution. We focus on changes impacting the successful commit rate of transactional service composition. This has led us to explore the trail of a flexible homeomorphism between alternative behaviors. We propose a behavioral adaptation approach that adjusts the behavior of transactional compositions of services in a proactive and transparent manner. This strategy is based on the Profiled Task Class concept. A service composition generator has also been developed for the performance evaluation of components implementing the behavioral adaptation strategy in order to identify its impact on the commit rate of CATS compositions.

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

CLASSY: A Conversational Aware Suggestion System

Over the last few years, pervasive systems have seen some interesting development. Nevertheless, human–human interaction can also take advantage of those systems by using their ability to perceive the surrounding environment. In this work, we have developed a pervasive system – named CLASSY – that is aware of the conversational context and suggests documents potentially useful to the users based on an Information Retrieval system, and proposed a new scoring approach that uses semantics and distance based on proximity data in order to classify the relationship between tokens.