Nunziato Cassavia

Improving tourist experience by Big Data tools

Due to the emerging Big Data applications traditional data management techniques result inadequate in many real life scenarios. In particular, OLAP techniques require substantial changes in order to offer useful analysis due to huge amount of data to be analyzed and their velocity and variety. In this paper, we describe an approach for dynamic Big Data searching that based on data collected by a suitable storage system, enrich data in order to guide users through data exploration in a efficient and effective way.

Innovative power operating center management exploiting big data techniques

The problem of accurately predicting the energy production from renewable sources has recently received an increasing attention from both the industrial and the research communities. It presents several challenges, such as facing with the rate data are provided by sensors, the heterogeneity of the data collected, power plants efficiency, as well as uncontrollable factors, such as weather conditions and user consumption profiles. In this paper we describe Vi-POC (Virtual Power Operating Center), a project conceived to assist energy producers and decision makers in the energy market. In this paper we present the Vi-POC project and how we face with challenges posed by the specific application. The solutions we propose have roots both in big data management and in stream data mining.

Discovering User Behavioral Features to Enhance Information Search on Big Data

Due to the emerging Big Data paradigm, driven by the increasing availability of intelligent services easily accessible by a large number of users (e.g., social networks), traditional data management techniques are inadequate in many real-life scenarios. In particular, the availability of huge amounts of data pertaining to user social interactions, user preferences, and opinions calls for advanced analysis strategies to understand potentially interesting social dynamics. Furthermore, heterogeneity and high speed of user-generated data require suitable data storage and management tools to be designed from scratch. This article presents a framework tailored for analyzing user interactions with intelligent systems while seeking some domain-specific information (e.g., choosing a good restaurant in a visited area). The framework enhances a users quest for information by exploiting previous knowledge about their social environment, the extent of influence the users are potentially subject to, and the influence they may exert on other users. User influence spread across the network is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. Such features are the result of data exchange activity (called data posting) that enriches information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users. The approach is tested in an important application scenario such as tourist recommendation, but it can be profitably exploited in several other contexts, for example, viral marketing and food education.

A Big Data Approach For Querying Data in EHR Systems

Information management in healthcare is nowadays experiencing a great revolution. After the impressive progress in digitizing medical data by private organizations, also the federal government and other public stakeholders have also started to make use of healthcare data for data analysis purposes in order to extract actionable knowledge. In this paper, we propose an architecture for supporting interoperability in healthcare systems by exploiting Big Data techniques. In particular, we describe a proposal based on big data techniques to implement a nationwide system able to improve EHR data access efficiency and reduce costs.

Data Preparation for Tourist Data Big Data Warehousing

The pervasive diffusion of new generation devices like smart phones and tablets along with the widespread use of social networks causes the generation of massive data flows containing heterogeneous information generated at different rates and having different formats. These data are referred as \emph{Big Data} and require new storage and analysis approaches to be investigated for managing them. In this paper we will describe a system for dealing with massive tourism flows that we exploited for the analysis of tourist behavior in Italy. We defined a framework that exploits a NoSQL approach for data management and map reduce for improving the analysis of the data gathered from different sources

A Peer to Peer Approach to Efficient High Performance Computing

Nowadays, many applications call for collaborative solutions in order to accomplish complex projects requiring huge amounts of computing resources, e.g., physical science simulation. Many approaches have been proposed in order to design a task partitioning strategy able to assign pieces of execution to the appropriate workers. In this paper, we describe our peer to peer solution for solving complex works by using the idling computational resources of users connected to our network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. By doing this, users help each others by asking the network available computational resources when they face high computing demanding tasks. Differently from many proposal available for volunteer computing, users providing their resources are rewarded with tangible credits, i.e., they can redeem their credits by asking computation power to solve their own task or/and they can redeem them earning coins. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead).

A Framework Enhancing the User Search Activity Through Data Posting

Due to the increasing availability of huge amounts of data, traditional data management techniques result inadequate in many real life scenarios. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch. In this paper, we describe a framework tailored for analyzing user interactions with intelligent systems while seeking for some domain specific information (e.g., choosing a good restaurant in a visited area). The framework enhances user quest for information by performing a data exchange activity (called data posting) which enriches the information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users.

Surfing Big Data Warehouses for Effective Information Gathering

Due to the emerging Big Data paradigm traditional data management techniques result inadequate in many real life scenarios. In particular, OLAP techniques require substantial changes in order to offer useful analysis due to huge amount of data to be analyzed and their velocity and variety. In this paper, we describe an approach for dynamic Big Data searching that based on data collected by a suitable storage system, enriches data in order to guide users through data exploration in an efficient and effective way.

High Performance Computing by the Crowd

Computational techniques both from a software and hardware viewpoint are nowadays growing at impressive rates leading to the development of projects whose complexity could be quite challenging, e.g., bio-medical simulations. Tackling such high demand could be quite hard in many context due to technical and economic motivation. A good trade-off can be the use of collaborative approaches. In this paper, we address this problem in a peer to peer way. More in detail, we leverage the idling computational resources of users connected to a network. We designed a framework that allows users to share their CPU and memory in a secure and efficient way. Indeed, users help each others by asking the network computational resources when they face high computing demanding tasks. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead), we hypothesize a remarkable side effect at steady state: energy consumption reduction compared with traditional server farm or cloud based executions.

Distributed computing by leveraging and rewarding idling user resources from P2P networks

Abstract Currently, many emerging computer science applications call for collaborative solutions to complex projects that require huge amounts of computing resources to be completed, e.g., physical science simulation, big data analysis. Many approaches have been proposed for high performance computing designing a task partitioning strategy able to assign pieces of execution to the appropriate workers in order to parallelize task execution. In this paper, we describe the Coremuniti T M system, our peer to peer solution for solving complex works by using the idling computational resources of users connected to our network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. By doing this, users help each other by asking the network computational resources when they face high computing demanding tasks. In this respect, as users provide their computational power without providing specific human skill, our approach can be considered as a hybrid crowdsourcing. Differently from many proposals available for volunteer computing, users providing their resources are rewarded with tangible credits, i.e., they can redeem their credits by asking for computational power to solve their own task and/or by exchanging them for money. We conducted a comprehensive experimental assessment in an interesting scenario as 3D rendering, which allowed us to validate the scalability and effectiveness of our solution and its profitability for end-users. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead), we hypothesize a remarkable side effect at steady state: energy consumption reduction compared with traditional server farms or cloud based executions.