The Experts below are selected from a list of 1377 Experts worldwide ranked by ideXlab platform
Francisca Pambel - One of the best experts on this subject based on the ideXlab platform.
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Privacy and security in the big Data Paradigm
Journal of Computer Information Systems, 2018Co-Authors: Zhaohao Sun, Kenneth David Strang, Francisca PambelAbstract:Privacy and security in the big Data age have drawn significant attention in the academia and industry. This article examines privacy and security in the big Data Paradigm through proposing a model...
Christoph Thuemmler - One of the best experts on this subject based on the ideXlab platform.
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An architecture for designing Future Internet (FI) applications in sensitive domains: Expressing the software to Data Paradigm by utilizing hybrid cloud technology
13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013, 2013Co-Authors: Stelios Sotiriadis, Paolo Zampognaro, Eleni Georga, Euripides G.m. Petrakis, Stefan Covaci, Christoph ThuemmlerAbstract:The emergency of cloud computing and Generic Enablers (GEs) as the building blocks of Future Internet (FI) applications highlights new requirements in the area of cloud services. Though, due to the current restrictions of various certification standards related with privacy and safety of health related Data, the utilization of cloud computing in such area has been in many instances unlawful. Here, we focus on demonstrating a “software to Data” provisioning solution to propose a mapping of FI application use case requirements to software specifications (using GEs). The aim is to establish a provider to consumer cloud setting wherein no sensitive Data will be exchanged but it will reside at the back-end site. We propose a prototype architecture that covers the cloud management layer and the operational features that manage Data and Internet of Things devices. To show a real life scenario, we present the use case of the diabetes care and a FI application that includes various GEs. TS - RIS
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Applying the Software-to-Data Paradigm in Next Generation E-Health Hybrid Clouds
2013 10th International Conference on Information Technology: New Generations, 2013Co-Authors: Christoph Thuemmler, Stefano de Panfilis, Armin Schneider, Julius Mueller, Stefan Covaci, Thomas Jell, Thomas Magedanz, Anastasius GavrasAbstract:Health care providers have over years continuously rejected Public Cloud technology for understandable concerns regarding privacy and confidentiality. The pick up rate on cloud computing in health care has been very poor reminding at the typical hype curve profile. Coincidentally Google Health has been discontinued and Microsoft Health Vault has switched focus and turned to the slightly "softer" ambient assisted living market. It seems that the idea of using Platforms as a Service and Software as a Service to manage and distribute patient Data in support of new distributed patient centric care models and services will require second thoughts and new ideas in order to receive another chance to penetrate global health care markets. The 2010 EC Cloud report briefly discussed the possibility of a reverse cloud approach aiming at sending software to the Data rather than the other way round [1]. This thought seems to be worthwhile exploring further, as it would also sit well with the fact that the overall amount of Data is currently growing much faster than the available bandwidth, a gap that continues to widen. Therefore it would be entirely reasonable to consider ideas to bring the software to the Data rather than the other way round. This approach would also address existing governance, QoS and security issues. Our paper proposes a taxonomy and an architecture for the implementation of the software-to-Data-Paradigm in health care scenarios. The model is based on the "FI Core Platform" an innovative concept currently under investigation under the European 7th Framework (FP7)[2].
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ITNG - Applying the Software-to-Data Paradigm in Next Generation E-Health Hybrid Clouds
2013 10th International Conference on Information Technology: New Generations, 2013Co-Authors: Christoph Thuemmler, Stefano de Panfilis, Armin Schneider, Julius Mueller, Stefan Covaci, Thomas Jell, Thomas Magedanz, Anastasius GavrasAbstract:Health care providers have over years continuously rejected Public Cloud technology for understandable concerns regarding privacy and confidentiality. The pick up rate on cloud computing in health care has been very poor reminding at the typical hype curve profile. Coincidentally Google Health has been discontinued and Microsoft Health Vault has switched focus and turned to the slightly "softer" ambient assisted living market. It seems that the idea of using Platforms as a Service and Software as a Service to manage and distribute patient Data in support of new distributed patient centric care models and services will require second thoughts and new ideas in order to receive another chance to penetrate global health care markets. The 2010 EC Cloud report briefly discussed the possibility of a reverse cloud approach aiming at sending software to the Data rather than the other way round [1]. This thought seems to be worthwhile exploring further, as it would also sit well with the fact that the overall amount of Data is currently growing much faster than the available bandwidth, a gap that continues to widen. Therefore it would be entirely reasonable to consider ideas to bring the software to the Data rather than the other way round. This approach would also address existing governance, QoS and security issues. Our paper proposes a taxonomy and an architecture for the implementation of the software-to-Data-Paradigm in health care scenarios. The model is based on the "FI Core Platform" an innovative concept currently under investigation under the European 7th Framework (FP7)[2].
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BIBE - An architecture for designing Future Internet (FI) applications in sensitive domains: Expressing the software to Data Paradigm by utilizing hybrid cloud technology
13th IEEE International Conference on BioInformatics and BioEngineering, 2013Co-Authors: Stelios Sotiriadis, Paolo Zampognaro, Eleni Georga, Euripides G.m. Petrakis, Stefan Covaci, Christoph ThuemmlerAbstract:The emergency of cloud computing and Generic Enablers (GEs) as the building blocks of Future Internet (FI) applications highlights new requirements in the area of cloud services. Though, due to the current restrictions of various certification standards related with privacy and safety of health related Data, the utilization of cloud computing in such area has been in many instances unlawful. Here, we focus on demonstrating a “software to Data” provisioning solution to propose a mapping of FI application use case requirements to software specifications (using GEs). The aim is to establish a provider to consumer cloud setting wherein no sensitive Data will be exchanged but it will reside at the back-end site. We propose a prototype architecture that covers the cloud management layer and the operational features that manage Data and Internet of Things devices. To show a real life scenario, we present the use case of the diabetes care and a FI application that includes various GEs.
Sören Auer - One of the best experts on this subject based on the ideXlab platform.
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Towards Linked Data based Enterprise Information Integration.
WaSABi'13 Proceedings of the 2013th International Conference on Semantic Web Enterprise Adoption and Best Practice Vol. 1106, 2013Co-Authors: Philipp Frischmuth, Kai Holzweißig, Sebastian Tramp, Jorg Unbehauen, Sören Auer, Carl-martin MarquardtAbstract:Data integration in large enterprises is a crucial but at the same time costly, long lasting and challenging problem. In the last decade, the prevalent Data integration approaches were primarily based on XML, Web Services and Service Oriented Architectures (SOA). We argue that classic SOA architectures may be well-suited for transaction processing, however more efficient technologies can be employed for enterprise Data integration. In particular, the use of the Linked Data Paradigm appears to be a very promising approach. In this article we explore challenges large enterprises are still facing with regard to Data integration. We discuss Linked Data approaches in these areas and present some examples of successful applications of the Linked Data principles in that context.
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linkedgeoData a core for a web of spatial open Data
Social Work, 2012Co-Authors: Claus Stadler, Jens Lehmann, Konrad Hoffner, Sören AuerAbstract:The Semantic Web eases Data and information integration tasks by providing an infrastructure based on RDF and ontologies. In this paper, we contribute to the development of a spatial Data Web by elaborating on how the collaboratively collected OpenStreetMap Data can be interactively transformed and represented adhering to the RDF Data model. This transformation will simplify information integration and aggregation tasks that require comprehensive background knowledge related to spatial features such as ways, structures, and landscapes. We describe how this Data is interlinked with other spatial Data sets, how it can be made accessible for machines according to the Linked Data Paradigm and for humans by means of several applications, including a faceted geo-browser. The spatial Data, vocabularies, interlinks and some of the applications are openly available in the LinkedGeoData project.
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diachronic linked Data towards long term preservation of structured interrelated information
Proceedings of the First International Workshop on Open Data, 2012Co-Authors: Sören Auer, Giorgos Flouris, Theodore Dalamagas, Helen Parkinson, Francois Bancilhon, Dimitris Sacharidis, Peter Buneman, Dimitris Kotzinos, Yannis Stavrakas, Vassilis ChristophidesAbstract:The Linked Data Paradigm is a promising technology for publishing, sharing, and connecting Data on the Web, which provides new perspectives for Data integration and interoperability. However, the proliferation of distributed, interconnected linked Data sources on the Web poses significant new challenges for consistently managing the vast number of potentially large Datasets and their interdependencies. In this article we focus on the key problem of preserving evolving structured interlinked Data. We argue that a number of issues, which hinder applications and users, are related to the temporal aspect that is intrinsic in Linked Data. We present three use cases to motivate our approach, we discuss problems that occur, and propose a direction for a solution.
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linkedgeoData adding a spatial dimension to the web of Data
International Semantic Web Conference, 2009Co-Authors: Sören Auer, Jens Lehmann, Sebastian HellmannAbstract:In order to employ the Web as a medium for Data and information integration, comprehensive Datasets and vocabularies are required as they enable the disambiguation and alignment of other Data and information. Many real-life information integration and aggregation tasks are impossible without comprehensive background knowledge related to spatial features of the ways, structures and landscapes surrounding us. In this paper we contribute to the generation of a spatial dimension for the Data Web by elaborating on how the collaboratively collected OpenStreetMap Data can be transformed and represented adhering to the RDF Data model. We describe how this Data can be interlinked with other spatial Data sets, how it can be made accessible for machines according to the linked Data Paradigm and for humans by means of a faceted geo-Data browser.
Zhaohao Sun - One of the best experts on this subject based on the ideXlab platform.
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Privacy and security in the big Data Paradigm
Journal of Computer Information Systems, 2018Co-Authors: Zhaohao Sun, Kenneth David Strang, Francisca PambelAbstract:Privacy and security in the big Data age have drawn significant attention in the academia and industry. This article examines privacy and security in the big Data Paradigm through proposing a model...
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Big Data Paradigm: What is the Status of Privacy and Security?
Annals of Data Science, 2017Co-Authors: Kenneth David Strang, Zhaohao SunAbstract:We extended the big Data body of knowledge by analyzing the longitudinal literature to highlight important research topics and identify critical gaps. We initially collected 79,012 articles from 1900 to 2016 related to big Data. We refined our sample to 13,029 articles allowing us to determine that the big Data Paradigm commenced in late 2011 and the research production exponentially rose starting in 2012, which approximated a Weibull distribution that captured 82% of the variance ( $$p
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big Data Paradigm what is the status of privacy and security
Annals of Data Science, 2017Co-Authors: Kenneth David Strang, Zhaohao SunAbstract:We extended the big Data body of knowledge by analyzing the longitudinal literature to highlight important research topics and identify critical gaps. We initially collected 79,012 articles from 1900 to 2016 related to big Data. We refined our sample to 13,029 articles allowing us to determine that the big Data Paradigm commenced in late 2011 and the research production exponentially rose starting in 2012, which approximated a Weibull distribution that captured 82% of the variance (\(p<.01\)). We developed a dominant topic list for the big Data body of knowledge that contained 49 keywords resulting in an inter-rater reliability of 93% (\(\hbox {r}^{2}=0.89\)). We found there were 13 dominant topics that captured 49% of the big Data production in journals during 2011–2016 but privacy and security related topics accounted for only 2% of those outcomes. We analyzed the content of 970 journal manuscripts produced during the first of 2016 to determine the current status of big Data research. The results revealed a vastly different current trend with too many literature reviews and conceptual papers that accounted for 41% of the current big Data knowledge production. Interestingly, we observed new big Data topics emerging from the healthcare and physical sciences disciplines.
Giancarlo Sperlí - One of the best experts on this subject based on the ideXlab platform.
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A survey of Big Data dimensions vs Social Networks analysis
Journal of Intelligent Information Systems, 2020Co-Authors: Michele Ianni, Elio Masciari, Giancarlo SperlíAbstract:The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous Data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated Data (text, video, image and audio), Data heterogeneity and high speed generation rate. More in detail, the analysis of user generated Data by popular social networks (i.e Facebook ( https://www.facebook.com/ ), Twitter ( https://www.twitter.com/ ), Instagram ( https://www.instagram.com/ ), LinkedIn ( https://www.linkedin.com/ )) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of Data w.r.t. the dimensions defined for Big Data Paradigm (the so called Big Data 6 V’s).
