In ubiquitous environments, context-aware applications need to monitor their execution context. They use middleware services such as context managers for this purpose. The space of monitorable entities is huge and each context-aware application has specic monitoring requirements which can change at runtime as a result of new opportunities or constraints due to context variations. The issues dealt with in this paper are 1) to guide context-aware application designers in the specication of the monitoring of distributed context sources, and 2) to allow the adaptation of context management capabilities by dynamically taking into account new context data collectors not foreseen during the development process. The solution we present, CA3M, follows the model-driven engineering approach for answering the previous questions: 1) designers specialised into context management specify context-awareness concerns into models that conform to a context-awareness meta-model, and 2) these context-awareness models are present at runtime and may be updated to cater with new application requirements. This paper presents the whole chain from the context-awareness model denition to the dynamic instantiation of context data collectors following modications of context-awareness models at runtime.

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International audienceOn-line data stream analysis is an important challenge today because of the always-increasing rates of the streams issued from multiple heterogeneous sources, in many application domains. To reduce the amount of the data stream, several sampling methods were designed by the data stream research community. We focus in this paper, on the chain sampling algorithm proposed by Babcock et al. The aim of this algorithm is to select randomly and at any time, a given fixed proportion from the most recent items of the stream contained in the last sliding window. This algorithm is well adapted to the stream context, as only one pass over the data is performed. Moreover it uses a small memory, as it does not store all the items of the current sliding window. We show in this paper that the chain sampling algorithm suffers from some collision or redundancy problems. The collision occurs when the same item is selected as a sample more than once during the execution of the algorithm. We propose two approaches to overcome this weakness and improve the chain sampling algorithm. The first one is called “inverting the selection for a high sampling rate” and the second one is inspired from the “divide to conquer strategy”. Different experimentations are performed to show the efficiency of these two improvements, in particular their impact on the execution time of the algorithm

International audienceAbstract:Data streams are large data sets generated continuously and at a fast tempo. Their arrival rate is large compared to the treatment and storage capacities. Thus, these streams cannot be entirely stored. That is why we need to treat them in a single pass, without storing them exhaustively. However, for a particular stream, it is not always possible to predict in advance all of the processing to be performed. It is therefore necessary to save some of this data for future treatments. These stored data then build “summaries”. Several ways exist for the construction of the summary, among them, the sampling algorithms. We propose in this paper an in-depth study of sampling methods used for the construction of data stream summaries. This paper includes two main parts. First, we introduce the basic concepts of data stream: Windowing models over data stream as well as data stream applications. Then we describe the different sampling algorithms used in stream environments. We particularly focus on their advantages and drawbacks. Finally, we compare the performance of the Simple Random Sampling to the chain sampling algorithm and we discuss the relevant research challenges for data stream sampling

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