Gerenciamento de Big Data em plantas industriais

Nowadays the companies generate great amount of data from different sources, however some of them produce more data than they can analyze. Big Data is a set of data that grows very fast, collected several times during a short period of time. This work focus on the importance of the correct management of Big Data in an industrial plant. Through a case study based on a company that belongs to the pulp and paper area, the problems resolutions are going to be presented with the usage of appropriate data management. In the final chapters, the results achieved by the company are discussed, showing how the correct choice of data to be monitored and analyzed brought benefits to the company, also best practices will be recommended for the Big Data management

Simulation of Ground-Water Flow in the Cedar River Alluvial Aquifer Flow System, Cedar Rapids, Iowa Scientific

The Cedar River alluvial aquifer is the primary source of municipal water in the Cedar Rapids, Iowa, area. Since 1992, the U.S. Geological Survey, in cooperation with the City of Cedar Rapids, has investigated the hydrogeology and water quality of the Cedar River alluvial aquifer. This report describes a detailed analysis of the ground-water flow system in the alluvial aquifer, particularly near well field areas. The ground-water flow system in the Cedar Rapids area consists of two main components, the unconsolidated Quaternary deposits and the underlying carbonate bedrock that has a variable fracture density. Quaternary deposits consist of eolian sand, loess, alluvium, and glacial till. Devonian and Silurian bedrock aquifers overlie the Maquoketa Shale (Formation) of Ordovician age, a regional confining unit. Ground-water and surface-water data were collected during the study to better define the hydrogeology of the Cedar River alluvial aquifer and Devonian and Silurian aquifers. Stream stage and discharge, ground-water levels, and estimates of aquifer hydraulic properties were used to develop a conceptual ground-water flow model and to construct and calibrate a model of the flow system. This model was used to quantify the movement of water between the various components of the alluvial aquifer flow system and provide an improved understanding of the hydrology of the alluvial aquifer.

Eight Polymorphic Microsatellite Loci Developed and Characterized From Townsend’s Big-Eared Bat, Corynorhinus Townsendii

Two of the five subspecies of the western big-eared bat, Corynorhinus townsendii, are listed as federally endangered with the remaining three being of conservation concern. Knowing the degree of connectivity among populations would aid in the establishment of sound conservation and management plans for this taxon. For this purpose, we have developed and characterized eight polymorphic microsatellite markers.

Development and Characterization of 15 Polymorphic Microsatellite Loci Isolated From Rafinesque’s Big-Eared Bat, Corynorhinus rafinesquii

We developed and characterized 15 microsatellite markers for Rafinesque’s big-eared bat, Corynorhinus rafinesquii. In a population from Tennessee, the number of alleles per locus ranged from three to 13 and observed heterozygosities were 0.35 to 0.97 per locus. These loci will provide appropriate variability for estimation of population connectivity, demographic parameters, and genetic diversity for this species of concern.

Intraspecific Comparison of Population Structure, Genetic Diversity, and Dispersal Among Three Subspecies of Townsend’s Big-Eared Bats, Corynorhinus townsendii townsendii, C. t. pallescens, and the Endangered C. t. birginianus

Townsend’s big-eared bat, Corynorhinus townsendii, is distributed broadly across western North America and in two isolated, endangered populations in central and eastern United States. There are five subspecies of C. townsendii; C. t. pallescens, C. t. australis, C. t. townsendii, C. t. ingens, and C. t. virginianus with varying degrees of concern over the conservation status of each. The aim of this study was to use mitochondrial and microsatellite DNA data to examine genetic diversity, population differentiation, and dispersal of three C. townsendii subspecies. C. t. virginianus is found in isolated populations in the eastern United States and was listed as endangered under the Endangered Species Act in 1979. Concern also exists about declining populations of two western subspecies, C. t. pallescens and C. t. townsendii. Using a comparative approach, estimates of the genetic diversity within populations of the endangered subspecies, C. t. virginianus, were found to be significantly lower than within populations of the two western subspecies. Further, both classes of molecular markers revealed significant differentiation among regional populations of C. t. virginianus with most genetic diversity distributed among populations. Genetic diversity was not significantly different between C. t. townsendii and C. t. pallescens. Some populations of C. t. townsendii are not genetically differentiated from populations of C. t. pallescens in areas of sympatry. For the western subspecies gene flow appears to occur primarily through male dispersal. Finally, geographic regions representing significantly differentiated and genetically unique populations of C. townsendii virginianus are recognized as distinct evolutionary significant units.

Nebraska NAMA Big XII Champs!

The University of Nebraska-Lincoln National Agri-Marketing Association (NAMA) Marketing Team brought home the Big XII Championship as a national finalist at the 2009 NAMA Annual Conference held April 14-17 in Atlanta, Georgia.

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

Gerenciamento de Big Data em plantas industriais

Nowadays the companies generate great amount of data from different sources, however some of them produce more data than they can analyze. Big Data is a set of data that grows very fast, collected several times during a short period of time. This work focus on the importance of the correct management of Big Data in an industrial plant. Through a case study based on a company that belongs to the pulp and paper area, the problems resolutions are going to be presented with the usage of appropriate data management. In the final chapters, the results achieved by the company are discussed, showing how the correct choice of data to be monitored and analyzed brought benefits to the company, also best practices will be recommended for the Big Data management

Too Big to Ignore: Global Partnership on Small-Scale Fisheries Research - the Latin America and Caribbean network

Too Big to Ignore (TBTI; www.toobigtoignore.net) is a research network and knowledge mobilization partnership established to elevate the profile of small-scale fisheries (SSF), to argue against their marginalization in national and international policies, and to develop research and governance capacity to address global fisheries challenges. Network participants and partners are conducting global and comparative analyses, as well as in-depth studies of SSF in the context of local complexity and dynamics, along with a thorough examination of governance challenges, to encourage careful consideration of this sector in local, regional and global policy arenas. Comprising 15 partners and 62 researchers from 27 countries, TBTI conducts activities in five regions of the world. In Latin America and the Caribbean (LAC) region, we are taking a participative approach to investigate and promote stewardship and self-governance in SSF, seeking best practices and success stories that could be replicated elsewhere. As well, the region will focus to promote sustainable livelihoods of coastal communities. Key activities include workshops and stakeholder meetings, facilitation of policy dialogue and networking, as well as assessing local capacity needs and training. Currently, LAC members are putting together publications that examine key issues concerning SSF in the region and best practices, with a first focus on ecosystem stewardship. Other planned deliverables include comparative analysis, a regional profile on the top research issues on SSF, and a synthesis of SSF knowledge in LAC

A Fuzzy Ontology-Driven Approach to Semantic Interoperability in e-Government Big Data

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society.

Software per l'analisi di Big Data su più livelli e sua applicazione a tracce di Google

Con l'avanzare della tecnologia, i Big Data hanno assunto un ruolo importante. In questo lavoro è stato implementato, in linguaggio Java, un software volto alla analisi dei Big Data mediante R e Hadoop/MapReduce. Il software è stato utilizzato per analizzare le tracce rilasciate da Google, riguardanti il funzionamento dei suoi data center.

Analisi e realizzazione di una piattaforma basate sul Cloud Computing per il trattamento dei Big Data.

L'elaborato introduce i concetti di Big Data, Cloud Computing e le tipologie di paradigmi basati sul calcolo parallelo. Trasformando questi concetti in pratica tramite un caso di studio sui Big Data. Nell'elaborato si spiega l'architettura proposta per l'elaborazione di report in formato pdf. Analaizando in fine i risultati ottenuti.

Big data: il valore dei dati e applicazioni all'analisi strategica

Il presente lavoro di tesi tende a un duplice scopo: il primo è quello di fornire una accurata analisi tecnica, applicativa e culturale riguardante il vasto mondo dei big data e il secondo quello di trovare connessioni con l’analisi strategica verificando se e in quale modo i big data possano risultare una risorsa distintiva in campo aziendale. Nello specifico il primo capitolo presenta i big data nelle sue caratteristiche più importanti cercando di approfondire gli aspetti tecnici del fenomeno, le fonti di produzione dei dati, le metodologie principali di analisi e l’impatto sulla società. Il secondo capitolo descrive svariate applicazioni dei big data in campo aziendale concentrandosi sul rapporto tra questi e l’analisi strategica, non trascurando temi come il vantaggio competitivo e la business intelligence. Infine il terzo capitolo analizza la condizione attuale, il punto di vista italiano ed eventuali sviluppi futuri del fenomeno.

Cenni al modello del Big Bang, inflazione e materia oscura

La cosmologia ha come scopo lo studio di origine ed evoluzione dell’Universo, per averne una comprensione generale attraverso le leggi fisiche conosciute. Il modello del Big Bang, di cui si dà la descrizione teorica nel capitolo 1, è una delle formulazioni più recenti di teoria che mira a spiegare l’inizio e la successiva evoluzione dell’Universo, fino ad avanzare anche ipotesi sul suo destino futuro; è inoltre il modello generalmente accettato dalla comunità scientifica in quanto, di tutti i modelli proposti nel corso degli anni, è quello con più riscontri sperimentali e che meglio si trova in accordo con le osservazioni. I principali successi del modello vengono trattati nel capitolo 2. In particolare: si tratterà di redshift e legge di Hubble, collegati all’espansione dell’Universo, importante prova dell’attendibilità del modello del Big Bang; della radiazione cosmica di fondo, scoperta negli anni ’60 quasi per caso e poi ricondotta a ipotesi teoriche già avanzate anni prima nelle prime formulazioni del modello; della nucleosintesi primordiale, che spiega l’abbondanza attuale dei principali costituenti dell’Universo, H ed He, cosa che altrimenti non sarebbe spiegabile se si considerasse soltanto la formazione di tali elementi nelle stelle. Ovviamente anche questo modello, come si vede nel capitolo 3, non manca di problemi, a cui si è trovata una risoluzione parziale o comunque non definitiva; in questa sede tuttavia, per ragioni di spazio, tali problemi e la loro soluzione verranno soltanto accennati. Si accennerà al problema dell’Universo piatto e al problema dell’orizzonte, e a come essi possano essere spiegati introducendo il concetto di inflazione. Infine, nel capitolo 4, si accenna anche alla materia oscura, soprattutto per quanto riguarda le evidenze sperimentali che ne hanno permesso la scoperta e le prove successive che ne confermano l’esistenza. Tuttora ne ignoriamo natura e composizione, visto che a differenza della materia ordinaria non emette radiazione elettromagnetica, ma i suoi effetti gravitazionali sono evidenti ed è estremamente difficile che la sua esistenza possa essere messa in discussione.

Cenni al modello del big bang, inflazione e materia oscura

Il presente lavoro intende offrire un riassunto dello “stato dell’arte” della Cosmologia: dopo averne ripercorso per sommi capi la storia degli ultimi cent’anni (cap.1), verrà illustrato il Modello Cosmologico Standard “ΛCDM” (cap.2), che è quello generalmente accettato dalla comunità scientifica allo stato attuale delle conoscenze; infine, si farà un breve “excursus” sulle questioni “aperte” e sui modelli cosmologici alternativi (cap.3).