A Data Life Cycle Modeling Proposal by Means of Formal Methods

Data usually evolve according to specific processes, with the consequent possibility to identify a profile of evolution: the values it may assume, the frequencies at which it changes, the temporal variation in relation to other data, or other constraints that are directly connected to the reference domain. A violation of these conditions could be the signal of different menaces that threat the system, as well as: attempts of a tampering or a cyber attack, a failure in the system operation, a bug in the applications which manage the life cycle of data. To detect such violations is not straightforward as processes could be unknown or hard to extract. In this paper we propose an approach to model the data life cycle by observing the data evolution in its life cycle. Thus, we represent users able to alter data through timed automata. Through model checking, the obtained profile of evolution can be used to detect anomalies in relational database, data warehouse and big data.