Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica - Tesi di Dottorato
Permanent URI for this collectionhttps://lisa.unical.it/handle/10955/31
Questa collezione raccoglie le Tesi di Dottorato afferenti al Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica dell'Università della Calabria.
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Item Big Data Analysis: Methodologies, Frameworks and Real-World Applications(Università della Calabria, 2023-06-28) Branda, Francesco; Fortino, Giancarlo; Talia, DomenicoInthelastyears,thecapacitytoproduceandcollectdatahasincreasedexpo- nentially.Thehugeamountofdatagenerated,commonlyreferredtoasBigData, thespeedatwhichitisproduced,anditsheterogeneityintermsofformatrepresent a challengetocurrentstorage,processing,andanalysiscapabilities.Thisscenario requiresthedesignandimplementationofnewarchitecturesandanalyticalplatform solutionsthatmustprocessBigDatatoextractcomplexpredictiveanddescriptive models.Today,high-performancecomputing(HPC)infrastructuressuchashighly parallelclusters,supercomputers,andcloudscanbeusedforprocessingandanalyz- ingmassivesourcesofreal-worlddatainvariousfields,includinggenomicsequencing andmedicalresearch,frauddetection,andweatherforecasting.Followingthesepre- liminaryobservations,thegoalofthisthesisistwofold.First,themainchallengesto besolvedforimplementinginnovativedataanalysisapplicationsonHPCsystemsare investigated.Inparticular,themainkeyresearchtopicsaddressedinclude:(i)stud- iesofsoftwaresystemsforBigDatastoring,processing,andanalysis;(ii)methods, techniques,andprototypesdesignedandusedtoimplementBigDatasolutionson massivedatasourcesrequiringtheuseofhigh-performancecomputingsystems;and (iii)designandprogrammingissuesforBigDataanalysisinExascalesystems,which willrepresentthenextcomputingstep.Second,severalinnovativeapplicationsand usecasesofBigDataanalyticsthatcanbeimplementedinlarge-scaleparallelsys- temsareproposed.Theseresearchcontributionsprovidenewinsightsandsolutions forextractingusefulknowledgefromlargevolumesofdata,describingmethodsand mechanismstosupportusers,practitioners,andscientistsworkingintheareaofBig Datainthedesignandexecutionofdataanalysistechniquesindifferentapplication domains.Item Resource reservation protocol and predictive algorithms for QoS support in wireless environments(2008-01) Fazio, Peppino; Talia, Domenico; Marano, SalvatoreItem Scalable data analysis: methods, tools and applications(2017-07-26) Belcastro, Loris; Crupi, Felice; Talia, DomenicoItem Mobile Computing: energy-aware tecniques and location-based methodologies(2014-12-01) Falcone, Deborah; Talia, Domenico; Greco, SergioItem Designing Cloud services for data processing and knowledge discovery(2012-10-24) Marozzo, Fabrizio; Palopoli, Luigi; Talia, Domenico; Trunfio, PaoloItem A spatial data infrastructure(2012-10-24) D'Amore, Francesco; Palopoli, Luigi; Talia, Domenico; Cinnirella, SergioItem Declarative Semantics for Consistency Maintenance(2006) Caroprese, Luciano; Zumpano, Ester; Talia, DomenicoItem Ontology-Driven Modelling and analyzing of Business Process(2014-03-10) Gualtieri, Andrea; Saccà, Domenico; Talia, DomenicoItem Modelling complex data mining applications in a formal framework(2008) Locane, Antonio; Saccà, Domenico; Manco, Giuseppe; Talia, DomenicoItem Swarm-Based Algorithms for Decentralized Clustering and Resource Discovery in Grids(2012-11-09) Forestiero, Agostino; Spezzano, Giandomenico; Talia, DomenicoIn this thesis, some novel algorithms based on swarm intelligent paradigm are proposed. In particular, the swarm agents, was exploited to tackle the following issues: - P2P Clustering. A swarm-based algorithm is used to cluster distributed data in a peer-to-peer environment through a small worlds topology. Moreover, to perform spatial clustering in every peer, two novel algorithms are proposed. They are based on the stochastic search of the ocking algorithm and on the main principles of two popular clustering algorithms, DBSCAN and SNN. - Resource discovery in Grids. An approach based on ant systems is exploited to replicate and map Grid services information on Grid hosts according to the semantic classi cation of such services. To exploit this mapping, a semi-informed resource discovery protocol which makes use of the ants' work has been achieved. Asynchronous query messages (agents) issued by clients are driven towards "representative peers" which maintain information about a large number of resources having the required characteristics.