Component-aware Orchestration of Cloud-based Enterprise Applications, from TOSCA to Docker and Kubernetes

Enterprise IT is currently facing the challenge of coordinating the management of complex, multi-component applications across heterogeneous cloud platforms. Containers and container orchestrators provide a valuable solution to deploy multi-component applications over cloud platforms, by coupling the lifecycle of each application component to that of its hosting container. We hereby propose a solution for going beyond such a coupling, based on the OASIS standard TOSCA and on Docker. We indeed propose a novel approach for deploying multi-component applications on top of existing container orchestrators, which allows to manage each component independently from the container used to run it. We also present prototype tools implementing our approach, and we show how we effectively exploited them to carry out a concrete case study

eStorys: A visual storyboard system supporting back-channel communication for emergencies

This is the post-print version of the final paper published in Journal of Visual Languages & Computing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.In this paper we present a new web mashup system for helping people and professionals to retrieve information about emergencies and disasters. Today, the use of the web during emergencies, is confirmed by the employment of systems like Flickr, Twitter or Facebook as demonstrated in the cases of Hurricane Katrina, the July 7, 2005 London bombings, and the April 16, 2007 shootings at Virginia Polytechnic University. Many pieces of information are currently available on the web that can be useful for emergency purposes and range from messages on forums and blogs to georeferenced photos. We present here a system that, by mixing information available on the web, is able to help both people and emergency professionals in rapidly obtaining data on emergency situations by using multiple web channels. In this paper we introduce a visual system, providing a combination of tools that demonstrated to be effective in such emergency situations, such as spatio/temporal search features, recommendation and filtering tools, and storyboards. We demonstrated the efficacy of our system by means of an analytic evaluation (comparing it with others available on the web), an usability evaluation made by expert users (students adequately trained) and an experimental evaluation with 34 participants.Spanish Ministry of Science and Innovation and Universidad Carlos III de Madrid and Banco Santander

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

Empowering Knowledge Bases: a Machine Learning Perspective

The construction of Knowledge Bases requires quite often the intervention of knowledge engineering and domain experts, resulting in a time consuming task. Alternative approaches have been developed for building knowledge bases from existing sources of information such as web pages and crowdsourcing; seminal examples are NELL, DBPedia, YAGO and several others. With the goal of building very large sources of knowledge, as recently for the case of Knowledge Graphs, even more complex integration processes have been set up, involving multiple sources of information, human expert intervention, crowdsourcing. Despite signi - cant e orts for making Knowledge Graphs as comprehensive and reliable as possible, they tend to su er of incompleteness and noise, due to the complex building process. Nevertheless, even for highly human curated knowledge bases, cases of incompleteness can be found, for instance with disjointness axioms missing quite often. Machine learning methods have been proposed with the purpose of re ning, enriching, completing and possibly raising potential issues in existing knowledge bases while showing the ability to cope with noise. The talk will concentrate on classes of mostly symbol-based machine learning methods, speci cally focusing on concept learning, rule learning and disjointness axioms learning problems, showing how the developed methods can be exploited for enriching existing knowledge bases. During the talk it will be highlighted as, a key element of the illustrated solutions, is represented by the integration of: background knowledge, deductive reasoning and the evidence coming from the mass of the data. The last part of the talk will be devoted to the presentation of an approach for injecting background knowledge into numeric-based embedding models to be used for predictive tasks on Knowledge Graphs

Digital entrepreneurship in a resource-scarce context: A focus on entrepreneurial digital competencies

Purpose – Thepurpose of this paper is to criticallyexplorehow context asan antecedent to entrepreneurial digital competencies (EDCs) influences digital entrepreneurship in a resource-scarce environment. Design/methodology/approach – The data comprises semi-structured interviews with 16 digital entrepreneurs, as owner-managers of small digital businesses in Cameroon. Findings – The results reveal the ways in which EDCs shape the entry (or start-up) choices and post-entry strategic decisions of digital entrepreneurs in response to context-specific opportunities and challenges associated with digital entrepreneurship. Research limitations/implications – The data comes from one African country and 16 digital businesses thus the research setting limits the generalisability of the results. Practical implications – This paper highlights important implications for encouraging digital entrepreneurship by focussing on institutional, technology and local dimensions of context and measures to develop the entrepreneurial and digital competencies. This includes policy interventions to develop the information and communication technology (ICT) infrastructure, transport and local distribution infrastructure, and training opportunities to develop the EDCs of digital entrepreneurs. Originality/value – Whereas the capabilities to adopt and use ICTs and the internet by small businesses have been examined, this is among the first theoretically sensitised study linking context, EDCs and digital entrepreneurship

Mergers & acquisitions research: A bibliometric study of top strategy and international business journals, 1980–2010

Mergers and acquisitions (M&As) are important modes through which firms carry out their domestic and international strategies and have been noted as the CEOs favorite strategy. As a significant field of study, M&Aresearch has accumulated substantial knowledge. This bibliometric study examines the extant strategy and international business literature on M&As. Methodologically, we examined a sample of 334 articles published in sixteen leading management/business journals, during a 31 year period — from 1980 to 2010. The results provide a global perspective of the field, identifying the works that have had the greater impact, the intellectual interconnections among authors and works, the main research traditions, or themes, delved upon on M&Arelated research. Structural and longitudinal analyses reveal the changes in the intellectual structure of the field over time. A discussion on the accumulated knowledge and future research avenues concludes this paper.info:eu-repo/semantics/publishedVersio

SIGHTED: A Framework for Semantic Integration of Heterogeneous Sensor Data on the Internet of Things

AbstractSensors are embedded nowadays in a growing number of everyday life objects. Smartphones, wearables, and sensor networks together play an important role in bridging the gap between physical and cyber worlds, a fundamental aspect of the Internet of Things vision. The ability to reuse sensor data integrated from multiple heterogeneous sources is a step towards building innovative applications and services. In this paper SIGHTED, a sensor data integration framework, is proposed exploiting semantic web technologies and linked data principles. It provides a layered structure as a guideline for integrating sensor data from various sources supporting accessibility and usability. DotThing, a demo platform, is implemented as an instantiation of SIGHTED framework and evaluated. Smartphones and sensor nodes are connected to DotThing showing the ability to query and reuse integrated sensor data from multiple sources to create more flexible horizontal applications. DotThing implementation also demonstrates the need for adding a semantic layer to existing IoT cloud-based platforms, like Xively, that generally lack such layer resulting in proprietary vertical solutions with limited data integration and discovery capabilities. DotThing makes use of vocabularies from existing ontologies on the linked data cloud providing a unified model to annotate data and link it to existing resources on the web