Abstract.Esterel is an imperative synchronous language designed for the specification and the development of reactive systems. Recent studies pointed out that its use for the development of avionics software can yield great benefits but that the lack of support for separate compilation in the current toolset may be an obstacle to the development of large systems. This paper presents the Cronos framework which provides such support for some specific cases of Esterel programs.

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MapReduce has excellent scalability and fault-tolerance mechanism. It fits well with the cheap commodity hardware. Today, using MapReduce to answer data analytical query is an attractive topic. In this work, we introduce Multiple Group-by query processing. Our processing of this query is based on MapReduce model, a new parallel computing model coming from Cloud Computing. A pre-processing phase is performed for fitting MapReduce's data accessing and improving data accessibility. We give different MapReduce job definitions in order to process data set partitioned in different partitioning methods. We evaluate our query's processing on top of a cluster of Grid'5000. We also address performance issues since they are very important in software industry to integrate a new technology. We analyze the measured results and discover several factors which impact the response time. At the end of this work, we propose a new data structure which allows more flexible job-scheduling.

MapReduce model is a new parallel programming model initially developed for large-scale web content processing. Data analysis meets the issue of how to do calculation over extremely large dataset. The arrival of MapReduce provides a chance to utilize commodity hardware for massively parallel data analysis applications. The translation and optimization from relational algebra operators to MapReduce programs is still an open and dynamic research field. In this paper, we focus on a special type of data analysis query, namely, multiple group by query. We first study the communication cost of MapReduce model, then we give an initial implementation of multiple group by query. We then propose an optimized version which addresses and improves the communication cost issues. Our optimized version shows a better accelerating ability and a better scalability than the other version.

Machine learning algorithms have been widely adopted in recent years due to their efficiency and versatility across many fields. However, the complexity of predictive models has led to a lack of interpretability in automatic decision-making. Recent works have improved general interpretability by estimating the contributions of input features to the prediction of a pre-trained model. Despite these advancements, practitioners still seek to gain causal insights into the underlying data-generating mechanisms. To this end, some works have attempted to integrate causal knowledge into interpretability, as non-causal techniques can lead to paradoxical explanations. These efforts have provided answers to various queries, but relying on a single pre-trained model may result in quantification problems. In this paper, we argue that each causal query requires its own reasoning; thus, a single predictive model is not suited for all questions. Instead, we propose a new framework that prioritizes the query of interest and then derives a query-driven methodology accordingly to the structure of the causal model. It results in a tailored predictive model adapted to the query and an adapted interpretability technique. Specifically, it provides a numerical estimate of causal effects, which allows for accurate answers to explanatory questions when the causal structure is known.