Towards Big data Governance in Cybersecurity

Big data refers to large complex structured or unstructured data sets. Big data technologies enable organisations to generate, collect, manage, analyse, and visualise big data sets, and provide insights to inform diagnosis, prediction, or other decision-making tasks. One of the critical concerns in handling big data is the adoption of appropriate big data governance frame- works to: 1) curate big data in a required manner to support quality data access for effective machine learning, and 2) ensure the framework regulates the storage and processing of the data from providers and users in a trustworthy way within the related regulatory frame- works (both legally and ethically). This paper proposes a framework of big data governance that guides organisations to make better data-informed business decisions within the related regularity framework, with close attention paid to data security, privacy and accessibility. In order to demonstrate this process, the work also presents an example implementation of the framework based on the case study of big data governance in cyber- security. This framework has the potential to guide the management of big data in different organisations for information sharing and cooperative decision-making

How useful are volunteers for visual biodiversity surveys? An evaluation of skill level and group size during a conservation expedition

The ability of volunteers to undertake different tasks and accurately collect data is critical for the success of many conservation projects. In this study, a simulated herpetofauna visual encounter survey was used to compare the detection and distance estimation accuracy of volunteers and more experienced observers. Experience had a positive effect on individual detection accuracy. However, lower detection performance of less experienced volunteers was not found in the group data, with larger groups being more successful overall, suggesting that working in groups facilitates detection accuracy of those with less experience. This study supports the idea that by optimizing survey protocols according to the available resources (time and volunteer numbers), the sampling efficiency of monitoring programs can be improved and that non-expert volunteers can provide valuable contributions to visual encounter-based biodiversity surveys. Recommendations are made for the improvement of survey methodology involving non-expert volunteers

Monitoring Approach of Cyber-physical Systems by Quality Measures

International audienceModern cities, industrial plants, cars, trucks, and vessels, among others, make extensive use of cyber-physical systems and sensors. These systems are very critical and contribute to assist decision making. Large data streams are thus produced and analyzed to extract information that allows building knowledge through a set of principles called wisdom. However, because of multiple imperfections, as well as intrinsic, contextual, and extrinsic conditions that alter data, the quality of the generated streams must be evaluated, to determine how relevant they are for decision support. This paper presents a methodology to monitor cyber-physical systems by quality estimation, which defines suitable evaluation characteristics for pertinent analysis. Quality assessment is defined for data imperfections, information dimensions, knowledge factors, and wisdom aspects. The case study of a cyber-physical network of a liquid container training platform is presented in detail, to show how the approach can be applied. Obtained measures are multidimensional, heterogeneous, and variable

Towards Multi-level Trust-Driven Data Integration in Multi-cloud Environments

International audienc