@article{AlvarezRodriguezColomoPalaciosStantchev2015, author = {{\´A}lvarez-Rodr{\´i}guez, Jose Mar{\´i}a and Colomo-Palacios, Ricardo and Stantchev, Vladimir}, title = {Skillrank: towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks}, series = {Scientific Programming}, journal = {Scientific Programming}, url = {http://nbn-resolving.de/urn:nbn:de:0298-opus4-7264}, pages = {14}, year = {2015}, abstract = {The present paper introduces a hybrid technique to measure the expertise of users by analyzing their profiles and activities in social networks. Currently, both job seekers and talent hunters are looking for new and innovative techniques to filter jobs and candidates where candidates are trying to improve and make their profiles more attractive. In this sense, the Skillrank approach is based on the conjunction of existing and well-known information and expertise retrieval techniques that perfectly fit the existing web and social media environment to deliver an intelligent component to integrate the user context in the analysis of skills confidence. A major outcome of this approach is that it actually takes advantage of existing data and information available on the web to perform both a ranked list of experts in a field and a confidence value for every professional skill. Thus, expertise and experts can be detected, verified, and ranked using a suited trust metric. An experiment to validate the Skillrank technique based on precision and recall metrics is also presented using two different datasets: (1) ad hoc created using real data from a professional social network and (2) real data extracted from the LinkedIn API.}, language = {en} } @article{SchermulyDraheimGlasbergetal.2015, author = {Schermuly, Carsten C. and Draheim, Michael and Glasberg, Ronald and Stantchev, Vladimir and Tamm, Gerrit and Hartmann, Michael and Hessel, Franz}, title = {Human resource crises in German hospitals: an explorative study}, series = {Human Resources for Health}, volume = {40}, journal = {Human Resources for Health}, number = {13}, url = {http://nbn-resolving.de/urn:nbn:de:0298-opus4-7288}, pages = {10}, year = {2015}, abstract = {The complexity of providing medical care in a high-tech environment with a highly specialized, limited labour force makes hospitals more crisis-prone than other industries. An effective defence against crises is only possible if the organizational resilience and the capacity to handle crises become part of the hospitals' organizational culture. To become more resilient to crises, a raised awareness — especially in the area of human resource (HR) — is necessary. The aim of this paper is to contribute to the process robustness against crises through the identification and evaluation of relevant HR crises and their causations in hospitals. Qualitative and quantitative methods were combined to identify and evaluate crises in hospitals in the HR sector. A structured workshop with experts was conducted to identify HR crises and their descriptions, as well as causes and consequences for patients and hospitals. To evaluate the findings, an online survey was carried out to rate the occurrence (past, future) and dangerousness of each crisis. Six HR crises were identified in this study: staff shortages, acute loss of personnel following a pandemic, damage to reputation, insufficient communication during restructuring, bullying, and misuse of drugs. The highest occurrence probability in the future was seen in staff shortages, followed by acute loss of personnel following a pandemic. Staff shortages, damage to reputation, and acute loss of personnel following a pandemic were seen as the most dangerous crises. The study concludes that coping with HR crises in hospitals is existential for hospitals and requires increased awareness. The six HR crises identified occurred regularly in German hospitals in the past, and their occurrence probability for the future was rated as high.}, language = {en} } @inproceedings{Stantchev2015, author = {Stantchev, Vladimir}, title = {Towards Ambient IT in Healthcare}, year = {2015}, abstract = {Keine Angabe No details}, language = {en} } @inproceedings{Stantchev2015, author = {Stantchev, Vladimir}, title = {The Perspectives for IT in Healthcare: the Case of Spain and Germany}, year = {2015}, abstract = {Keine Angabe No details}, language = {en} } @inproceedings{Stantchev2015, author = {Stantchev, Vladimir}, title = {PrevenTAB: Towards Ambient IT in Healthcare}, year = {2015}, abstract = {Keine Angabe No details}, language = {en} } @inproceedings{Stantchev2015, author = {Stantchev, Vladimir}, title = {Requirements Engineering and Risk Management Solutions}, year = {2015}, abstract = {Keine Angabe No details}, language = {en} } @article{StantchevPrietoGonzalezTamm2015, author = {Stantchev, Vladimir and Prieto-Gonz{\´a}lez, Lisardo and Tamm, Gerrit}, title = {Cloud computing service for knowledge assessment and studies recommendation in crowdsourcing and collaborative learning environments based on social network analysis}, series = {Computers in Human Behavior}, volume = {51}, journal = {Computers in Human Behavior}, number = {B}, pages = {762 -- 770}, year = {2015}, abstract = {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.}, language = {en} } @article{VeraBaqueroColomoPalaciosStantchevetal.2015, author = {Vera-Baquero, Alejandro and Colomo-Palacios, Ricardo and Stantchev, Vladimir and Molloy, Owen}, title = {Leveraging big-data for business process analytics}, series = {The Learning Organization}, volume = {22}, journal = {The Learning Organization}, number = {4}, pages = {215 -- 228}, year = {2015}, abstract = {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.}, language = {en} } @inproceedings{Stantchev2015, author = {Stantchev, Vladimir}, title = {Mathematical Models for Data Analysis in Econometrics}, year = {2015}, abstract = {Keine Angabe No details}, language = {en} } @inproceedings{Stantchev2015, author = {Stantchev, Vladimir}, title = {Mathematical Models for Data Analysis in Healthcare}, year = {2015}, abstract = {Keine Angabe No details}, language = {en} }