After a risk has manifested itself and has led to an accident, valuable lessons can be learned to reduce the risk of a similar accident occurring again. This calls for accident analysis methods. In the past 20 years, a large number of accident analysis methods have been proposed and it is difficult to find the right method to apply in a specific circumstance. The authors conducted a review of the state of the art of accident analysis methods and models across domains. They classify the models using the well-known categorization into sequential, epidemiological, and systemic methods. The authors find that these classes have their own characteristics in terms of speed of application versus pay-off. For optimum risk reduction, methods that take organizational issues into account can add valuable information to the risk management process in an organization.
The volume, velocity, variety, veracity and value of data currently produced and consumed by different types of information systems turned big Data into a phenomena of study. For data variety, temporal data commonly represents a source of potential inconsistency. This paper reports on a research endeavor for treating the problem of how to minimize inconsistencies in temporal databases due to unavailability of big data. This problem often occurs in situations where a same query is executed on the same data set at different points in time. To address this issue, we propose query optimization strategies based on query transformation and rewriting rules, to amend data consistency in temporal databases. We validate these strategies proposed via case scenario in sensor data analysis, and via manual data input, both for local and distributed query environments.
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