Tesi di Dottorato

Now showing 1 - 10 of 12

Designing Cloud services for data processing and knowledge discovery
(2012-10-24) Marozzo, Fabrizio; Palopoli, Luigi; Talia, Domenico; Trunfio, Paolo
Metodi e algoritmi per il riconoscimento del livello di distrazione e affaticamento nella guida di autoveicoli
(2012-10-24) Cario, Gianni; Palopoli, Luigi; Casavola, Alessandro
New materials and technologies for compact antennas and circuits at millimeter frequencies
(2012-10-24) Borgia, Antonio; Palopoli, Luigi; Costanzo, Sandra
Autonomic computing-based wireless sensor networks
(2013-11-27) Galzarano, Stefano; Fortino, Giancarlo; Liotta, Antonio; Greco, Sergio
Wireless Sensor Networks (WSNs) have grown in popularity in the last years by proving to be a bene cial technology for a wide range of application do- mains, including but not limited to health-care, environment and infrastruc- ture monitoring, smart home automation, industrial control, intelligent agri- culture, and emergency management. However, developing applications on such systems requires many e orts due to the lack of proper software abstractions and the di culties in man- aging resource-constrained embedded environments. Moreover, these appli- cations have to meet a combination of con icting requirements. Achieving accuracy, e ciency, correctness, fault-tolerance, adaptability and reliability on WSN is a major issue because these features have to be provided beyond the design/implementation phase, notably at execution time. This thesis explores the viability and convenience of Autonomic Comput- ing in the context of WSNs by providing a novel paradigm to support the development of autonomic WSN applications as well as speci c self-adaptive protocols at networking levels. In particular, this thesis provides three main contributions. The rst is the design and realization of a novel framework for the development of e cient distributed signal processing applications on heterogeneous WSNs, called SPINE2. It provides a programming abstraction based on the task-oriented paradigm for abstracting away low-level details and has a platform-independent architecture enabling code reusability and portability, application interoperability and platform heterogeneity. The sec- ond contribution is the development of SPINE-* which is an enhancement of SPINE2 by means of an autonomic plane, a way for separating out the provision of self-* techniques from the WSN application logic. Such a separa- tion of concerns leads to an ease of deployment and run-time management of new applications. We nd that this enhancement brings not only considerable functional improvements but also measurable performance bene ts. Third, since we advocate that the agent-oriented paradigm is a well-suited approach in the context of autonomic computing, we propose MAPS, an agent-based programming framework for WSNs. Speci cally designed for supporting Java- iii based sensor platforms, MAPS allows the development of general-purpose mobile multi-agent applications by adopting a multi-plane state machine for- malism for de ning agents' behavior. Finally, the fourth contribution regards the design, analysis, and simulations of a self-adaptive AODV routing protocol enhancement, CG-AODV, and a novel contention-based MAC protocol, QL- MAC. CG-AODV adopts a \node concentration-driven gossiping" approach for limiting the ooding of control packets, whereas QL-MAC, based on a Q-learning approach, aims to nd an e cient radio wake-up/sleep scheduling strategy to reduce energy consumption on the basis of the actual network load of the neighborhood. Simulation results show that CG-AODV outper- forms AODV, whereas QL-MAC provides better performance over standard MAC protocols.
Discovering Exceptional Individuals and Properties in Data
(2014-03-07) Fassetti, Fabio; Angiulli, Fabrizio; Palopoli, Luigi
XML and web data management
(2014-03-07) Bettina Fazzinga, Bettina Fazzinga; Sergio. Flesca, Sergio. Flesca; Domenico Talia, Domenico Talia
Scheduling techniques in high-speed packet switches
(2014-03-06) Scicchitano,Alessandra; Bianco,Andrea; Molinaro,Antonella; Talia,Domenico
Super peer models for public resource computing
(2014-03-06) Cozza,Pasquale; Talia,Domenico
Advances in mining complex data: modeling and clustering
(2009) Ponti,Giovanni; Greco,Sergio; Palopoli,Luigi
In the last years, there has been a great production of data that come from di®erent application contexts. However, although technological progress pro- vides several facilities to digitally encode any type of event, it is important to de¯ne a suitable representation model which underlies the main character- istics of the data. This aspect is particularly relevant in ¯elds and contexts where data to be archived can not be represented in a ¯x structured scheme, or that can not be described by simple numerical values. We hereinafter refer to these data with the term complex data. Although it is important de¯ne ad-hoc representation models for complex data, it is also crucial to have analysis systems and data exploration tech- niques. Analysts and system users need new instruments that support them in the extraction of patterns and relations hidden in the data. The entire process that aims to extract useful information and knowledge starting from raw data takes the name of Knowledge Discovery in Databases (KDD). It starts from raw data and consists in a set of speci¯c phases that are able to transform and manage data to produce models and knowledge. There have been many knowledge extraction techniques for traditional structured data, but they are not suitable to handle complex data. Investigating and solving representation problems for complex data and de¯ning proper algorithms and techniques to extract models, patterns and new information from such data in an e®ective and e±cient way are the main challenges which this thesis aims to face. In particular, two main aspects related to complex data management have been investigated, that are the way in which complex data can be modeled (i.e., data modeling), and the way in which homogeneous groups within complex data can be identi¯ed (i.e., data clustering). The application contexts that have been objective of such studies are time series data, uncertain data, text data, and biomedical data. It is possible to illustrate research contributions of this thesis by dividing them into four main parts, each of which concerns with one speci¯c area and data type: vi Abstract Time Series | A time series representation model has been developed, which is conceived to support accurate and fast similarity detection. This model is called Derivative time series Segment Approximation (DSA), as it achieves a concise yet feature-rich time series representation by com- bining the notions of derivative estimation, segmentation and segment approximation. Uncertain Data | Research in uncertain data mining went into two di- rections. In a ¯rst phase, a new proposal for partitional clustering has been de¯ned by introducing the Uncertain K-medoids (UK-medoids) al- gorithm. This approach provides a more accurate way to handle uncertain objects in a clustering task, since a cluster representative is an uncertain object itself (and not a deterministic one). In addition, e±ciency issue has been addressed by de¯ning a distance function between uncertain objects that can be calculated o²ine once per dataset. In a second phase, research activities aimed to investigate issues related to hierarchical clustering of uncertain data. Therefore, an agglomera- tive centroid-based linkage hierarchical clustering framework for uncer- tain data (U-AHC) has been proposed. The key point lies in equipping such scheme with a more accurate distance measure for uncertain objects. Indeed, it has been resorted to information theory ¯eld to ¯nd a mea- sure able to compare probability distributions of uncertain objects used to model uncertainty. Text Data |Research results on text data can be summarized in two main contributions. The ¯rst one regards clustering of multi-topic documents, and a framework for hard clustering of documents according to their mix- tures of topics has been proposed. Documents are assumed to be modeled by a generative process, which provides a mixture of probability mass functions (pmfs) to model the topics that are discussed within any spe- ci¯c document. The framework combines the expressiveness of generative models for document representation with a properly chosen information- theoretic distance measure to group the documents. The second proposal concerns distributional clustering of XML documents, focusing on a the development of a distributed framework for e±ciently clustering XML documents. The distributed environment consists of a peer-to-peer network where each node in the network has access to a portion of the whole document collection and communicates with all the other nodes to perform a clustering task in a collaborative fashion. The proposed framework is based on modeling and clustering XML documents by structure and content. Indeed, XML documents are transformed into transactional data based on the notion of tree tuple. The framework is based on the well-known paradigm of centroid-based partitional clustering to conceive the distributed, transactional clustering algorithm. Biomedical Data | Research results on time series and uncertain data have been involved to support e®ective and e±cient biomedical data man- agement. The focus regarded both proteomics and genomics, investigat- Abstract vii ing Mass Spectrometry (MS) data and microarray data. In the speci¯c, a Mass Spectrometry Data Analysis (MaSDA) system has been de¯ned. The key idea consists in exploiting temporal information implicitly contained in MS data and model such data as time series. The major advantages of this solution are the dimensionality and the noise reduction. As re- gards micrarray data, U-AHC has been employed to perform clustering of microarray data with probe-level uncertainty. A strategy to model probe- level uncertainty has been de¯ned, together with a hierarchical clustering scheme for analyzing such data. This approach performs a gene-based clustering to discover clustering solutions that are well-suited to capture the underlying gene-based patterns of microarray data. The e®ectiveness and the e±ciency of the proposed techniques in clus- tering complex data are demonstrated by performing intense and exhaustive experiments, in which such proposals are extensively compared with the main state-of-the-art competitors.
On the Computational Complexity of solution concepts in compact coalitional games
(2014-03-05) Malizia,Enrico; Palopoli,Luigi; Scarcello,Francesco

Tesi di Dottorato

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