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The skill shortage is becoming an ever-increasing challenge for IT departments. Allocation of resources in the best possible way is even more important. The challenge is to improve the enterprise not only on the side of the organizational and process level, but to develop new strategies and approaches in human resource management. Only a symbiosis of the disciplines economics, psychology and information technology will enable relevant and indispensable employees and promote loyalty to the company. A frequent change of the work place, for a well-trained professional, is so long associated with normality until they find the best environment for fulfilling their needs and expectations. These expectations are no longer just on a financial level. This paper will analyse the previous work on these topics and demonstrate first conclusions regarding a way forward.
Interactions among people have substantially changed since the emergence of social networks, the expansion of the Internet and the proliferation of connected mobile devices, and so have the possibilities of collaborative learning, with the inclusion of new e-learning platforms. From this point, assessing human knowledge in these virtual environments is not a trivial task. This work presents a novel cloud-computing-based service which relies on advanced artificial intelligence mechanisms to infer knowledge and interest from users considering the aggregated data presented from/to these users in different social networks. This way it is possible to assess with a certain degree of confidence the user knowledge level in different topics as well as recommend additional specific education related to his/her former studies in order to get a better/desired job.
This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence systems is not sufficient to meet today's business needs. They normally are business domain-specific and have not been sufficiently process-aware to support the needs of process improvement-type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs, with no structure, and produced on a variety of heterogeneous environments. This paper tackles this variability by devising different Big Data based approaches that aim to gain visibility into process performance.