Daniele Cenni

Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices

In many health care situations, powerful mobile tools may help to make decisions and provide support for continuous education and training. They can be useful in emergency conditions and for the supervised application of protocols and procedures. To this end, content models and formats with semantic and intelligence have more flexibility to provide medical personnel (both in off-line and on-line conditions) with more powerful tools than those currently on the market. In this paper, we are presenting Mobile Medicine solution, which exploits a collection of semantic computing technologies together with intelligent content model and tools to provide innovative services for medical personnel. Most of the activities of semantic computing are performed on the back office on a cloud computing architecture for: clustering, recommendations, intelligent content production and adaptation. The mobile devices have been endowed with a content organizer to collect local data, provide local suggestions, while supporting taxonomical searches and local queries on PDA and iPhone. The proposed solution is under usage at the main hospital in Florence. The smart content has been produced by medical personnel, with the adoption of the new ADF-Design authoring tool, which produces content in MPEG-21 format. The mobile content distribution service is integrated with a collaborative networking portal, for discussion on procedures and content, thus suggestions are provided on both PC and Mobiles (PDA and iPhone).

Micro Grids for Scalable Media Computing and Intelligence in Distributed Scenarios

Micro grid technology is playing a central role in the semantic evolution of small- and medium-sized services. This article presents the main requirements and architecture of micro grids for media and semantic computing.

Analysis and assessment of a knowledge based smart city architecture providing service APIs

Abstract The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.

A Knowledge Base Driven Solution for Smart Cloud Management

The complexity of Cloud infrastructures is increasing every year, requiring new concepts and tools to face off topics such as process configuration and reconfiguration, automatic scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolution, while enabling the reasoning on cloud structures and implementing strategies of efficient smart cloud management and intelligence. The solution proposed is composed of Smart Cloud Engine, SCE, the Knowledge Base, KB, and the Supervisor and Monitoring module for data acquisition. It can be easily integrated with any cloud configuration manager, cloud orchestra or, and monitoring tool, since the connections with these tools are performed by using REST calls and XML files.

Managing cloud via Smart Cloud Engine and Knowledge Base

Abstract Complexity of cloud infrastructures needs models and tools for process management, configuration, scaling, elastic computing and cloud resource health control. This paper presents a Smart Cloud Engine and solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolutions, and enabling the reasoning on structures by implementing strategies of efficient smart cloud management and intelligence. The solution proposed provides formal verification and intelligence tools for cloud control. It can be easily integrated with a large range of cloud configuration manager, cloud orchestrator, and monitoring tools, since the connections with these tools are performed by using REST calls and XML files. The proposed solution has been validated in the context of large ICARO Cloud project and in the cloud facility of a national cloud service provider. Some data resulting from the validation phases have been reported and are referring to the dynamic management of real ECLAP social network http://www.eclap.eu .

Km4City Smart City API: An Integrated Support for Mobility Services

The main technical issues regarding smart city solutions are related todata gathering, aggregation, reasoning, access, and service delivering via Smart City APIs (Application Program Interfaces). Aggregated and re-conciliated data (open and private, static and real time) should be exploitable by reasoning/smart algorithms for enabling sophisticated service delivering. Different kinds of Smart City APIs enable Smart City Services and Applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators. To this end, a comparison of the state of the art solutions for data aggregation was performed, by putting in evidence the needs of semantic interoperable aggregated data, to provide smart services. This paper presents the work performed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with services. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation and service production. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Finally, the API consumption related data in the recent period are presented.

Smart Cloud Engine and Solution Based on Knowledge Base

Abstract Complexity of cloud infrastructures needs models and tools for process management, configuration, scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolution, and enabling the reasoning on structures by implementing strategies of efficient smart cloud management and intelligence. The solution proposed provides formal verification tools and intelligence for cloud control. It can be easily integrated with any cloud configuration manager, cloud orchestrator, and monitoring tool, since the connections with these tools are performed by using REST calls and XML files. It has been validated in the large ICARO Cloud project with a national cloud service provider.

P2P Architecture for Automated B2B Cross Media Content Distribution

The requests of efficient solutions for automated distribution of digital content are constantly increasing especially from business users. The solution proposed allows setting up scalable, fault tolerant and fast distribution architectures of cross media content through the use of P2P networks. The solution is based on an evolution of the BitTorrent protocol and architecture, to make it suitable for content distribution among Business actors and for business to consumer distribution. This solution improves the BitTorrent technology and integrates it with that of the AXMEDIS infrastructure GRID, obtaining a solution that can be used to implement a large set of different strategies about content distribution, seeding, publishing, promoting, etc. The work has been developed for the AXMEDIS Integrated Project 1ST FP6 (Automating Production of Cross Media Content for Multi-channel Distribution) partially funded by the European Commission, and thus for setting up the related P2P network.

ICARO Cloud Simulator exploiting knowledge base

Abstract Allocation changes on cloud are complex and time consuming tasks, on cloning, scaling, etc. A solution to cope with these aspects is to perform a simulation. Cloud simulators have been proposed to assess conditions adopting specific models for energy, cloud capacity, allocations, networking, security, etc. In this paper, ICARO Cloud Simulator is proposed. It has been specifically designed for simulating the workload on the basis of real virtual machine workloads and for simulating complex business configurations and behaviours for wide temporal windows. This approach can be useful to predict and simulate the allocation of virtual machines on hosts and, thus, data centers on the basis of real business configuration behaviour for days, weeks, months, etc. (for example, to predict workloads). The proposed research has been developed in the context of the ICARO Cloud research and development project.

User Engagement Engine for Smart City Strategies

The new challenges in the smart city context are mainly related to the stimulation of the city users towards taking more sustainable behaviors, in mobility and energy. The state of the art in this case is mainly focused on classical smart city solution for informing the city users and or for engaging them with specific wired rules toward virtuous models. And not using flexible languages and predictive models, pushing them towards a larger range of virtuous habits. On this regards, the main problems are the computation of user behavior via data analytic (semantic computing, machine learning), as well as the formalization of strategies via simple and well formalized language for producing engagements to the city users, which can be understood by city operators. In this paper, a solution for city users engagement is studied and implemented for Sii-Mobility Smart city national project in Italy has been presented. The solution has been implemented thanks to the exploitation of Km4City model and semantic computing. The paper also presents the validation of results about the effective usage of the solution by providing some statistical evidence about the efficient assessment of user behavior and of engagement rules acceptance rate.