Abstract
Nowadays, companies and official bodies are using the data as a principal asset to take strategic decisions. The advances in big data processing, storage and analysis techniques have allowed to manage the continuous increase in the volume of data. This increase in the volume of data together with its high variability and the large number of sources lead to a constant growing of the complexity of the data management environment. Data governance is the key for simplifying that complexity: it is the element that controls the decision making and responsibilities for all the processes related to data management. This paper discusses an approach to data governance based on ontological reasoning to reduce data management complexity. The proposed data governance system is built over an autonomous system based on distributed components. It implements semantic techniques and automatic ontology-based reasoning. The different components use a Shared Knowledge Plane to interact. Its fundamental piece is an ontology that represents all the data management processes included in data governance. A prototype of such a system has been implemented and tested for Telefonica's global video service. The results obtained show the feasibility of using this type of technology to reduce the complexity of managing big data environments.
Highlights
An increase in the exchange of data volume in networks has been recorded daily, the use of social networks or IoT are two simple examples about it
We describe an ontology to cover the whole vision of the data governance framework, including all the data processes and their relations
User activity files are generated again. This activity is done in two phases: First, a generating signal is created through the execution of a SWRL rule over the knowledge base
Summary
An increase in the exchange of data volume in networks has been recorded daily, the use of social networks or IoT are two simple examples about it. The stakeholders with the most visibility in the data environment are data scientists and data architects The former use analysis software based on artificial intelligence, machine learning and statistics algorithms with which to prepare the data for the generation of prescriptive and predictive models that help in decision-making. The latter oversee the design and guarantee the availability of systems and architectures (both hardware and software) for handling large and different varieties of data. Presented; Section III is devoted to give a detailed description of our model, including Shared Knowledge Plane Description and, in Section IV a case study on the implemented model in security management is presented, in Section V conclusion and future works are discussed
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