980 resultados para order data management


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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

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The need for a convergence between semi-structured data management and Information Retrieval techniques is manifest to the scientific community. In order to fulfil this growing request, W3C has recently proposed XQuery Full Text, an IR-oriented extension of XQuery. However, the issue of query optimization requires the study of important properties like query equivalence and containment; to this aim, a formal representation of document and queries is needed. The goal of this thesis is to establish such formal background. We define a data model for XML documents and propose an algebra able to represent most of XQuery Full-Text expressions. We show how an XQuery Full-Text expression can be translated into an algebraic expression and how an algebraic expression can be optimized.

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Smart Environments are currently considered a key factor to connect the physical world with the information world. A Smart Environment can be defined as the combination of a physical environment, an infrastructure for data management (called Smart Space), a collection of embedded systems gathering heterogeneous data from the environment and a connectivity solution to convey these data to the Smart Space. With this vision, any application which takes advantages from the environment could be devised, without the need to directly access to it, since all information are stored in the Smart Space in a interoperable format. Moreover, according to this vision, for each entity populating the physical environment, i.e. users, objects, devices, environments, the following questions can be arise: “Who?”, i.e. which are the entities that should be identified? “Where?” i.e. where are such entities located in physical space? and “What?” i.e. which attributes and properties of the entities should be stored in the Smart Space in machine understandable format, in the sense that its meaning has to be explicitly defined and all the data should be linked together in order to be automatically retrieved by interoperable applications. Starting from this the location detection is a necessary step in the creation of Smart Environments. If the addressed entity is a user and the environment a generic environment, a meaningful way to assign the position, is through a Pedestrian Tracking System. In this work two solution for these type of system are proposed and compared. One of the two solution has been studied and developed in all its aspects during the doctoral period. The work also investigates the problem to create and manage the Smart Environment. The proposed solution is to create, by means of natural interactions, links between objects and between objects and their environment, through the use of specific devices, i.e. Smart Objects

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L’esperimento CMS a LHC ha raccolto ingenti moli di dati durante Run-1, e sta sfruttando il periodo di shutdown (LS1) per evolvere il proprio sistema di calcolo. Tra i possibili miglioramenti al sistema, emergono ampi margini di ottimizzazione nell’uso dello storage ai centri di calcolo di livello Tier-2, che rappresentano - in Worldwide LHC Computing Grid (WLCG)- il fulcro delle risorse dedicate all’analisi distribuita su Grid. In questa tesi viene affrontato uno studio della popolarità dei dati di CMS nell’analisi distribuita su Grid ai Tier-2. Obiettivo del lavoro è dotare il sistema di calcolo di CMS di un sistema per valutare sistematicamente l’ammontare di spazio disco scritto ma non acceduto ai centri Tier-2, contribuendo alla costruzione di un sistema evoluto di data management dinamico che sappia adattarsi elasticamente alle diversi condizioni operative - rimuovendo repliche dei dati non necessarie o aggiungendo repliche dei dati più “popolari” - e dunque, in ultima analisi, che possa aumentare l’“analysis throughput” complessivo. Il Capitolo 1 fornisce una panoramica dell’esperimento CMS a LHC. Il Capitolo 2 descrive il CMS Computing Model nelle sue generalità, focalizzando la sua attenzione principalmente sul data management e sulle infrastrutture ad esso connesse. Il Capitolo 3 descrive il CMS Popularity Service, fornendo una visione d’insieme sui servizi di data popularity già presenti in CMS prima dell’inizio di questo lavoro. Il Capitolo 4 descrive l’architettura del toolkit sviluppato per questa tesi, ponendo le basi per il Capitolo successivo. Il Capitolo 5 presenta e discute gli studi di data popularity condotti sui dati raccolti attraverso l’infrastruttura precedentemente sviluppata. L’appendice A raccoglie due esempi di codice creato per gestire il toolkit attra- verso cui si raccolgono ed elaborano i dati.

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Jahnke and Asher explore workflows and methodologies at a variety of academic data curation sites, and Keralis delves into the academic milieu of library and information schools that offer instruction in data curation. Their conclusions point to the urgent need for a reliable and increasingly sophisticated professional cohort to support data-intensive research in our colleges, universities, and research centers.

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OBJECTIVE: To describe the electronic medical databases used in antiretroviral therapy (ART) programmes in lower-income countries and assess the measures such programmes employ to maintain and improve data quality and reduce the loss of patients to follow-up. METHODS: In 15 countries of Africa, South America and Asia, a survey was conducted from December 2006 to February 2007 on the use of electronic medical record systems in ART programmes. Patients enrolled in the sites at the time of the survey but not seen during the previous 12 months were considered lost to follow-up. The quality of the data was assessed by computing the percentage of missing key variables (age, sex, clinical stage of HIV infection, CD4+ lymphocyte count and year of ART initiation). Associations between site characteristics (such as number of staff members dedicated to data management), measures to reduce loss to follow-up (such as the presence of staff dedicated to tracing patients) and data quality and loss to follow-up were analysed using multivariate logit models. FINDINGS: Twenty-one sites that together provided ART to 50 060 patients were included (median number of patients per site: 1000; interquartile range, IQR: 72-19 320). Eighteen sites (86%) used an electronic database for medical record-keeping; 15 (83%) such sites relied on software intended for personal or small business use. The median percentage of missing data for key variables per site was 10.9% (IQR: 2.0-18.9%) and declined with training in data management (odds ratio, OR: 0.58; 95% confidence interval, CI: 0.37-0.90) and weekly hours spent by a clerk on the database per 100 patients on ART (OR: 0.95; 95% CI: 0.90-0.99). About 10 weekly hours per 100 patients on ART were required to reduce missing data for key variables to below 10%. The median percentage of patients lost to follow-up 1 year after starting ART was 8.5% (IQR: 4.2-19.7%). Strategies to reduce loss to follow-up included outreach teams, community-based organizations and checking death registry data. Implementation of all three strategies substantially reduced losses to follow-up (OR: 0.17; 95% CI: 0.15-0.20). CONCLUSION: The quality of the data collected and the retention of patients in ART treatment programmes are unsatisfactory for many sites involved in the scale-up of ART in resource-limited settings, mainly because of insufficient staff trained to manage data and trace patients lost to follow-up.

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INTRODUCTION: Despite the key role of hemodynamic goals, there are few data addressing the question as to which hemodynamic variables are associated with outcome or should be targeted in cardiogenic shock patients. The aim of this study was to investigate the association between hemodynamic variables and cardiogenic shock mortality. METHODS: Medical records and the patient data management system of a multidisciplinary intensive care unit (ICU) were reviewed for patients admitted because of cardiogenic shock. In all patients, the hourly variable time integral of hemodynamic variables during the first 24 hours after ICU admission was calculated. If hemodynamic variables were associated with 28-day mortality, the hourly variable time integral of drops below clinically relevant threshold levels was computed. Regression models and receiver operator characteristic analyses were calculated. All statistical models were adjusted for age, admission year, mean catecholamine doses and the Simplified Acute Physiology Score II (excluding hemodynamic counts) in order to account for the influence of age, changes in therapies during the observation period, the severity of cardiovascular failure and the severity of the underlying disease on 28-day mortality. RESULTS: One-hundred and nineteen patients were included. Cardiac index (CI) (P = 0.01) and cardiac power index (CPI) (P = 0.03) were the only hemodynamic variables separately associated with mortality. The hourly time integral of CI drops <3, 2.75 (both P = 0.02) and 2.5 (P = 0.03) L/min/m2 was associated with death but not that of CI drops <2 L/min/m2 or lower thresholds (all P > 0.05). The hourly time integral of CPI drops <0.5-0.8 W/m2 (all P = 0.04) was associated with 28-day mortality but not that of CPI drops <0.4 W/m2 or lower thresholds (all P > 0.05). CONCLUSIONS: During the first 24 hours after intensive care unit admission, CI and CPI are the most important hemodynamic variables separately associated with 28-day mortality in patients with cardiogenic shock. A CI of 3 L/min/m2 and a CPI of 0.8 W/m2 were most predictive of 28-day mortality. Since our results must be considered hypothesis-generating, randomized controlled trials are required to evaluate whether targeting these levels as early resuscitation endpoints can improve mortality in cardiogenic shock.

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The paper showcases the field- and lab-documentation system developed for Kinneret Regional Project, an international archaeological expedition to the Northwestern shore of the Sea of Galilee (Israel) under the auspices of the University of Bern, the University of Helsinki, Leiden University and Wofford College. The core of the data management system is a fully relational, server-based database framework, which also includes time-based and static GIS services, stratigraphic analysis tools and fully indexed document/digital image archives. Data collection in the field is based on mobile, hand-held devices equipped with a custom-tailored stand-alone application. Comprehensive three-dimensional documentation of all finds and findings is achieved by means of total stations and/or high-precision GPS devices. All archaeological information retrieved in the field – including tachymetric data – is synched with the core system on the fly and thus immediately available for further processing in the field lab (within the local network) or for post-excavation analysis at remote institutions (via the WWW). Besides a short demonstration of the main functionalities, the paper also presents some of the key technologies used and illustrates usability aspects of the system’s individual components.

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The Center for Orbit Determination in Europe (CODE) is contributing as a global Analysis center to the International GNSS Service (IGS) since many years. The processing of GPS and GLONASS data is well established in CODE’s ultra-rapid, rapid, and final product lines. With the introduction of new signals for the established and new GNSS, new challenges and opportunities are arising for the GNSS data management and processing. The IGS started the Multi-GNSS-EXperiment (MGEX) in 2012 in order to gain first experience with the new data formats and to develop new strategies for making optimal use of these additional measurements. CODE has started to contribute to IGS MGEX with a consistent, rigorously combined triple-system orbit solution (GPS, GLONASS, and Galileo). SLR residuals for the computed Galileo satellite orbits are of the order of 10 cm. Furthermore CODE established a GPS and Galileo clock solution. A quality assessment shows that these experimental orbit and clock products allow even a Galileo-only precise point positioning (PPP) with accuracies on the decimeter- (static PPP) to meter-level (kinematic PPP) for selected stations.

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Early Employee Assistance Programs (EAPs) had their origin in humanitarian motives, and there was little concern for their cost/benefit ratios; however, as some programs began accumulating data and analyzing it over time, even with single variables such as absenteeism, it became apparent that the humanitarian reasons for a program could be reinforced by cost savings particularly when the existence of the program was subject to justification.^ Today there is general agreement that cost/benefit analyses of EAPs are desirable, but the specific models for such analyses, particularly those making use of sophisticated but simple computer based data management systems, are few.^ The purpose of this research and development project was to develop a method, a design, and a prototype for gathering managing and presenting information about EAPS. This scheme provides information retrieval and analyses relevant to such aspects of EAP operations as: (1) EAP personnel activities, (2) Supervisory training effectiveness, (3) Client population demographics, (4) Assessment and Referral Effectiveness, (5) Treatment network efficacy, (6) Economic worth of the EAP.^ This scheme has been implemented and made operational at The University of Texas Employee Assistance Programs for more than three years.^ Application of the scheme in the various programs has defined certain variables which remained necessary in all programs. Depending on the degree of aggressiveness for data acquisition maintained by program personnel, other program specific variables are also defined. ^

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Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.