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Distributed Cyber-physical systems (DCPS) represent a new field in automatic control and recently they are increasingly used in life critical system, where the probability of tragic failure has to be kept below very low levels. In other side, fault tolerance has been used to face failures and achieve best performance of systems. Thus, it becomes critical to develop models that are tolerant toward the failure of system. This will enable guarantee of DCPS that will continue to run even through failure status. In this paper we take self-stabilizing as an example of fault tolerance in distributed cyber physical systems, where self-stabilization provides non-masking approach to fault tolerance. Then the Event-B approach is presented as formal method for system-level modelling and analysis. It is proposed using the Rodin modelling tool for Event-B that integrates modelling and proving.

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Nowadays, we are living in the midst of a data explosion and seeing a massive growth in databases so with the wide availability of huge amounts of data; necessarily we are become in need for turning this data into useful information and knowledge, where Data mining uncovers interesting patterns and relationships hidden in a large volume of raw data and big data is a new term used to identify the datasets that are of large size and have grater complexity. The knowledge gained from data can be used for applications such as market analysis, customer retention and production control. Data mining is a massive computing task that deals with huge amount of stored data in a centralized or distributed system to extract useful information or knowledge. In this paper, we will discuss Distributed Data Mining systems, approaches, Techniques and algorithms to deal with distributed data to discover knowledge from distributed data in an effective and efficient way.

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