Competência em informação na construção da inteligência competitiva nas organizações: o caso da empresa Mizumo

Pós-graduação em Ciência da Informação - FFC

Tipos documentais e fluxos de informação como subsídios para o processo decisório em ambientes organizacionais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Toward the Optimization of an e-Tongue System Using Information Visualization: A Case Study with Perylene Tetracarboxylic Derivative Films in the Sensing Units

The wide variety of molecular architectures used in sensors and biosensors and the large amount of data generated with some principles of detection have motivated the use of computational methods, such as information visualization techniques, not only to handle the data but also to optimize sensing performance. In this study, we combine projection techniques with micro-Raman scattering and atomic force microscopy (AFM) to address critical issues related to practical applications of electronic tongues (e-tongues) based on impedance spectroscopy. Experimentally, we used sensing units made with thin films of a perylene derivative (AzoPTCD acronym), coating Pt interdigitated electrodes, to detect CuCl(2) (Cu(2+)), methylene blue (MB), and saccharose in aqueous solutions, which were selected due to their distinct molecular sizes and ionic character in solution. The AzoPTCD films were deposited from monolayers to 120 nm via Langmuir-Blodgett (LB) and physical vapor deposition (PVD) techniques. Because the main aspects investigated were how the interdigitated electrodes are coated by thin films (architecture on e-tongue) and the film thickness, we decided to employ the same material for all sensing units. The capacitance data were projected into a 2D plot using the force scheme method, from which we could infer that at low analyte concentrations the electrical response of the units was determined by the film thickness. Concentrations at 10 mu M or higher could be distinguished with thinner films tens of nanometers at most-which could withstand the impedance measurements, and without causing significant changes in the Raman signal for the AzoPTCD film-forming molecules. The sensitivity to the analytes appears to be related to adsorption on the film surface, as inferred from Raman spectroscopy data using MB as analyte and from the multidimensional projections. The analysis of the results presented may serve as a new route to select materials and molecular architectures for novel sensors and biosensors, in addition to suggesting ways to unravel the mechanisms behind the high sensitivity obtained in various sensors.

Intercambio de información entre sistemas SAP R/3-Knapp para optimizar el proceso de expedición de pedidos

[ES] El objetivo de este Trabajo es el de parametrizar, implementar las estructuras de datos y programar las aplicaciones necesarias que posibilitan el intercambio de información entre dos entornos software, SAP R/3 y Knapp, líderes en sus campos de actuación. El resultado de aplicar tales cambios permitirá a la organización no sólo centralizar la información en el ERP, sino que mejorará sus procesos de negocio y agilizará la toma de decisiones por parte de los responsables. Se realiza un estudio de la situación actual y, tras un análisis detallado, se propone una solución que permita alcanzar los objetivos propuestos. Una vez diseñada, presentada y aprobada la propuesta, se procede a la parametrización de SAP R/3, a la definición de los segmentos y tipos de IDOC y a la codificación de funciones y programas que permitan tratar la información enviada por Knapp. Finalizadas estas tareas, se elaboran juegos de datos de los procesos comerciales y se ejecutan en un entorno de test, en colaboración con los usuarios claves, para comprobar la bondad de la solución implementada. Se analizan los resultados y se corrigen posibles deficiencias. Finalmente se transporta al sistema productivo todos los cambios realizados y se verifica la correcta ejecución de los procesos de negocio de la organización.

Dalla normativa alle procedure perative:gestione dei dati ai fini della prevenzione e protezione in un'azienda multiservizi

La presente Tesi di Laurea Specialistica considera, partendo da un'analisi della normativa vigente e delle procedure aziendali interne, il Sistema di Gestione Integrato Qualità  Sicurezza Ambiente (SGI QSA) di HERA SpA con particolare attenzione alle tematiche relative alla Prevenzione e Protezione sul luogo di lavoro in riferimento al Testo Unico sulla sicurezza (D.Lgs 81/2008) . Nello specifico, l'elaborato si basa sull'esperienza maturata durante cinque mesi di stage effettuati presso l'ufficio "Servizio Prevenzione e Protezione" della Struttura Operativa Territoriale (SOT) Bologna. Durante la mia permanenza in HERA SpA, ho avuto modo di osservare e prendere parte alle attività  quotidianamente svolte sia in ufficio che presso gli impianti dislocati nel territorio della provincia di Bologna con particolare riguardo alla raccolta, gestione e fruibilità  dei dai inerenti la sicurezza dei luoghi di lavoro. Nell'ambito dello stage, ho avuto anche la possibilità , estremamente formativa, di prendere visione dei processi, delle tecnologie e delle modalità  operative sottostanti l'erogazione di servizi da parte di una Multiutility; acquisire consapevolezza e know how in merito alle energie messe in campo per effettuare attività  quali la raccolta e lo smaltimento di rifiuti piuttosto che rendere disponibile alle utenze la fornitura di acqua e gas. Ritengo che questo possa darmi un valore aggiunto sia da un punto di vista professionale che da un punto di vista umano. Scopo primario di questa trattazione è effettuare l'istantanea di un'azienda complessa e in rapida evoluzione come HERA a partire della Salute e Sicurezza dei Lavoratori con l'obiettivo di indicare le attività  eseguite durante lo stage e il contributo fornito allo sviluppo e al mantenimento del SGS (Sistema di Gestione per la Salute e la sicurezza). Per meglio evidenziare la diversa natura delle informazioni riportate, l'elaborato risulta diviso in due parti fondamentali: La I PARTE riguarda lo studio della normativa che regola il settore con particolare riferimento al TUSL Testo Unico per la Sicurezza sui Luoghi di Lavoro (norma vigente in Italia) e allo standard britannico OHSAS 18001 a cui possono fare riferimento le organizzazioni che intendono certificare il proprio sistema di gestione in materia di sicurezza. In seguito si andranno ad analizzare le norme ISO 9001e ISO14001 che riguardano rispettivamente la possibilità  di certificare il proprio sistema di gestione in merito a Qualità  del servizio e tutela dell'Ambiente. Infine saranno proposte alcune riflessioni riguardanti la necessità  di sviluppare un sistema di gestione integrato e certificato che permetta di avere una visione unitaria di Qualità  Sicurezza e Ambiente. Nella II PARTE si entrerà  nel merito delle attività  svolte dall'ufficio Prevenzione e Protezione: a partire dalle procedure aziendali che fungono da punto di contatto fra gli obblighi normativi e la necessità  di regolare l'operatività  dei lavoratori, saranno descritte le mansioni che mi sono state affidate e le attività  svolte durante lo stage.

DTCU: Alberi Decisionali per la Classificazione di Dati Incerti

Oggi piu' che mai e' fondamentale essere in grado di estrarre informazioni rilevanti e conoscenza dal grande numero di dati che ci possono arrivare da svariati contesti, come database collegati a satelliti e sensori automatici, repository generati dagli utenti e data warehouse di grandi compagnie. Una delle sfide attuali riguarda lo sviluppo di tecniche di data mining per la gestione dell’incertezza. L’obiettivo di questa tesi e' di estendere le attuali tecniche di gestione dell’incertezza, in particolare riguardanti la classificazione tramite alberi decisionali, in maniera tale da poter gestire incertezza anche sull’attributo di classe.

Progettazione di uno strumento di analisi dati per sistemi SCADA-ENERGY Management System

L’obiettivo di questa tesi è approfondire le competenze sulle funzionalità sviluppate nei sistemi SCADA/EMS presenti sul mercato, così da conoscerne le potenzialità offerte: tutte le conoscenze acquisite servono a progettare uno strumento di analisi dati flessibile e interattivo, con il quale è possibile svolgere analisi non proponibili con le altre soluzioni analizzate. La progettazione dello strumento di analisi dei dati è orientata a definire un modello multidimensionale per la rappresentazione delle informazioni: il percorso di progettazione richiede di individuare le informazioni d’interesse per l’utente, così da poterle reintrodurre in fase di progettazione della nuova base dati. L’infrastruttura finale di questa nuova funzionalità si concretizza in un data warehouse: tutte le informazioni di analisi sono memorizzare su una base dati diversa da quella di On.Energy, evitando di correlare le prestazione dei due diversi sottosistemi. L’utilizzo di un data warehouse pone le basi per realizzare analisi su lunghi periodi temporali: tutte le tipologie di interrogazione dati comprendono un enorme quantità d’informazioni, esattamente in linea con le caratteristiche delle interrogazioni OLAP

Analyzing spatio-temporal patterns of genuine cross-correlations

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

Adaptive sensing for target tracking applications

In a statistical inference scenario, the estimation of target signal or its parameters is done by processing data from informative measurements. The estimation performance can be enhanced if we choose the measurements based on some criteria that help to direct our sensing resources such that the measurements are more informative about the parameter we intend to estimate. While taking multiple measurements, the measurements can be chosen online so that more information could be extracted from the data in each measurement process. This approach fits well in Bayesian inference model often used to produce successive posterior distributions of the associated parameter. We explore the sensor array processing scenario for adaptive sensing of a target parameter. The measurement choice is described by a measurement matrix that multiplies the data vector normally associated with the array signal processing. The adaptive sensing of both static and dynamic system models is done by the online selection of proper measurement matrix over time. For the dynamic system model, the target is assumed to move with some distribution and the prior distribution at each time step is changed. The information gained through adaptive sensing of the moving target is lost due to the relative shift of the target. The adaptive sensing paradigm has many similarities with compressive sensing. We have attempted to reconcile the two approaches by modifying the observation model of adaptive sensing to match the compressive sensing model for the estimation of a sparse vector.

Validation of climate model-inferred regional temperature change for late-glacial Europe

Comparisons of climate model hindcasts with independent proxy data are essential for assessing model performance in non-analogue situations. However, standardized palaeoclimate data sets for assessing the spatial pattern of past climatic change across continents are lacking for some of the most dynamic episodes of Earth’s recent past. Here we present a new chironomid-based palaeotemperature dataset designed to assess climate model hindcasts of regional summer temperature change in Europe during the late-glacial and early Holocene. Latitudinal and longitudinal patterns of inferred temperature change are in excellent agreement with simulations by the ECHAM-4 model, implying that atmospheric general circulation models like ECHAM-4 can successfully predict regionally diverging temperature trends in Europe, even when conditions differ significantly from present. However, ECHAM-4 infers larger amplitudes of change and higher temperatures during warm phases than our palaeotemperature estimates, suggesting that this and similar models may overestimate past and potentially also future summer temperature changes in Europe.

The application of medical informatics to the veterinary management programs at companion animal practices in Alberta, Canada: a case study.

Companion animals closely share their domestic environment with people and have the potential to, act as sources of zoonotic diseases. They also have the potential to be sentinels of infectious and noninfectious, diseases. With the exception of rabies, there has been minimal ongoing surveillance of, companion animals in Canada. We developed customized data extraction software, the University of, Calgary Data Extraction Program (UCDEP), to automatically extract and warehouse the electronic, medical records (EMR) from participating private veterinary practices to make them available for, disease surveillance and knowledge creation for evidence-based practice. It was not possible to build, generic data extraction software; the UCDEP required customization to meet the specific software, capabilities of the veterinary practices. The UCDEP, tailored to the participating veterinary practices', management software, was capable of extracting data from the EMR with greater than 99%, completeness and accuracy. The experiences of the people developing and using the UCDEP and the, quality of the extracted data were evaluated. The electronic medical record data stored in the data, warehouse may be a valuable resource for surveillance and evidence-based medical research.

Mining free-text medical records for companion animal enteric syndrome surveillance.

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals.

Pan2Applic - a tool to convert and compile PANGAEA output files

The software Pan2Applic is a tool to convert files or folders of files (ascii/tab-separated data files with or without metaheader), downloaded from PANGAEA via the search engine or the data warehouse to formats as used by applications, e.g. for visualization or further processing. It may also be used to convert files or zip-archives as downloaded from CD-ROM data collections, published in the WDC-MARE Reports series. Pan2Applic is distributed as freeware for the operating systems Microsoft Windows, Apple OS X and Linux.

Automatic Understanding of ATC Speech: Study of Prospectives and Field Experiments for Several Controller Positions

Although there has been a lot of interest in recognizing and understanding air traffic control (ATC) speech, none of the published works have obtained detailed field data results. We have developed a system able to identify the language spoken and recognize and understand sentences in both Spanish and English. We also present field results for several in-tower controller positions. To the best of our knowledge, this is the first time that field ATC speech (not simulated) is captured, processed, and analyzed. The use of stochastic grammars allows variations in the standard phraseology that appear in field data. The robust understanding algorithm developed has 95% concept accuracy from ATC text input. It also allows changes in the presentation order of the concepts and the correction of errors created by the speech recognition engine improving it by 17% and 25%, respectively, absolute in the percentage of fully correctly understood sentences for English and Spanish in relation to the percentages of fully correctly recognized sentences. The analysis of errors due to the spontaneity of the speech and its comparison to read speech is also carried out. A 96% word accuracy for read speech is reduced to 86% word accuracy for field ATC data for Spanish for the "clearances" task confirming that field data is needed to estimate the performance of a system. A literature review and a critical discussion on the possibilities of speech recognition and understanding technology applied to ATC speech are also given.