Giorgio Mario Grasso

Mining Uplink-Downlink User Association in Wireless Heterogeneous Networks

In the current era of big data, wide varieties of high volumes of valuable data of different veracities can be generated or collected at a high velocity. One of the popular sources of these big data is the wireless networks. Nowadays, the use of smartphones has significantly increased the traffic load in these cellular networks. Consequently, system models that are practical in real-life scenario with the significant for increasing traffic load in cellular networks have drawn attentions of researchers. Studies have been conducted to solve the related interesting research problem of user association in this complex system model. Some of these studies formulated this research problem as a many-to-one matching game, in which users and base stations evaluate each other based on well-defined utilities. In this paper, we examine how the traditional data mining techniques—in particular, the frequent pattern mining techniques—help to solve this research problem. Specifically, we examine the mining of uplink-downlink user association data in wireless heterogeneous networks.

Particle physics and polyedra proximity calculation for hazard simulations in large-scale industrial plants

This paper describes a system developed for the simulation of flames inside an open-source 3D computer graphic software, Blender, with the aim of analyzing in virtual reality scenarios of hazards in large-scale industrial plants. The advantages of Blender are of rendering at high resolution the very complex structure of large industrial plants, and of embedding a physical engine based on smoothed particle hydrodynamics. This particle system is used to evolve a simulated fire. The interaction of this fire with the components of the plant is computed using polyhedron separation distance, adopting a Voronoi-based strategy that optimizes the number of feature distance computations. Results on a real oil and gas refining industry are presented.

Conceptual Integrity without Concepts.

It is commonly asserted that one of the most crucial factors in software design is the adoption of consistent and appropriate concepts. This widespread assumption, underlying several researches in software engineering, tacitly postulates the very existence of concepts. What, then, if concepts do not exist? This is the question asked in this paper, where the articulation of possible answers is attempted. As weird as it might sound, doubts on the existence of concepts have been recently cast by few distinguished philosophers of mind, with compelling arguments. One such argument is the heterogeneity of cognitive assets that can be referred to a single concept, and we show that it might be the case for design concepts in the example of Blender, a 3D computer graphics software.

Querying Encrypted OLAP Data

This paper provides an overview of some state-ofthe- art proposals in the context of querying encrypted OLAP data, along with critical discussion on open challenges and future research directions in the investigated topics.

Clustering-Based Spatio-Temporal Analysis of Big Atmospheric Data

This paper proposes a comprehensive approach for supporting clustering-based spatio-temporal analysis of big atmospheric data via specializing on the interesting applicative setting represented by Greenhouse Gas Emissions (GGEs), a relevant instance of Big Data that empathize the Variety aspect of the well-known 3V Big Data axioms. In particular, in our research we consider GGEs from three EU countries, namely UK, France and Italy. The deriving Big Data Mining model turns to be useful for decision support processes in both the governmental and industrial contexts.

Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis

Following our previous research results, in this paper we provide two authoritative application scenarios that build on top of OLAP*, a middleware for parallel processing of OLAP queries that truly realizes effective and efficiently OLAP over Big Data. We have provided two authoritative case studies, namely parallel OLAP data cube processing and virtual OLAP data cube design, for which we also propose a comprehensive performance evaluation and analysis. Derived analysis clearly confirms the benefits of our proposed framework.

Advanced pattern recognition from complex environments: a classification-based approach

This paper describes an algorithm for building 3D maps of objects detected in the visual scene acquired in an indoor environment. One feature of the described algorithm is that it works with a standard webcam equipped with a simple devices which automatically estimates the camera orientation and its distance from the floor. Another feature is that the algorithm has a low computational complexity. The proposed algorithm first extracts from the acquired images the regions of interest (ROI) which may contain an object. The ROI’s 3D position is then estimated and a map of the environment is generated. ROI extraction is realized with an Haar-like approach. ROIs are represented with edge-based features. The edge representation is filtered with a novel fuzzy-based technique which removes edges introduced by noise. Object classification is performed with a pseudo2D-HMM algorithm. We prove the reliability of our method by discussing some critical applications in the context of human–robot interaction and robot–robot interaction. Finally, we complete our contributions via describing a case study in the robotic field and providing comprehensive experimental results showing the benefits deriving from our approach.

An Innovative Framework for Supporting Frequent Pattern Mining Problems in IoT Environments.

In the current era of big data, high volumes of a wide variety of data of different veracity can be easily generated or collected at a high velocity from rich sources of data include devices from the Internet of Things (IoT). Embedded in these big data are useful information and valuable knowledge. Hence, frequent pattern mining and its related research problem of association rule mining, which aim to discover implicit, previously unknown and potentially useful information and knowledge—in the form of sets of frequently co-occurring items or rules revealing relationships between these frequent sets—from these big data have drawn attention of many researchers. For instance, since introduction of the research problems of association rule mining or frequent pattern mining, numerous information system and engineering approaches have been developed. These include the development of serial algorithms, distributed and parallel algorithms, as well as MapReduce-based big data mining algorithms. These algorithms can be run in local computers, distributed and parallel environments, as well as clusters, grids and clouds. In this paper, we describe some of these algorithms and discuss how to mine frequent patterns or association rules in fogs—i.e., edges of the computing network.

Genetic Estimation of Iterated Function Systems for Accurate Fractal Modeling in Pattern Recognition Tools

In this paper, we describe an algorithm to estimate the parameters of Iterated Function System (IFS) fractal models. We use IFS to model Speech and Electroencephalographic signals and compare the results. The IFS parameters estimation is performed by means of a genetic optimization approach. We show that the estimation algorithm has a very good convergence to the global minimum. This can be successfully exploited by pattern recognition tools. However, the set-up of the genetic algorithm should be properly tuned. In this paper, besides the optimal set-up description, we describe also the best tradeoff between performance and computational complexity. To simplify the optimization problem some constraints are introduced. A comparison with suboptimal algorithms is reported. The performance of IFS modeling of the considered signals are in accordance with known measures of the fractal dimension.

XML-VM: An XML-Based Grid Computing Middleware

This paper describes a novel distributing computing middleware named XML-VM. Its architecture is inspired by the ‘Grid Computing’ paradigm. The proposed system improves many characteristics of previous Grid systems, in particular the description of the distributed computation, the distribution of the code and the execution times. XML is a markup language commonly used to interchange arbitrary data over the Internet. The idea behind this work is to use XML to describe algorithms; XML documents are distributed by means of XML-RPC, interpreted and executed using virtual machines. XML-VM is an assembly-like language, coded in XML. Parsing of XML-VM programs is performed with a fast SAX parser for JAVA. XML-VM interpreter is coded in JAVA. Several algorithms are written in XML-VM and executed in a distributed environment. Representative experimental results are reported.