A engenharia simultânea e os sistemas de gerenciamento de dados de produto(PDM), apesar de serem uma ajuda preciosa para que as empresas enfrentem as novas condições de sobrevivência no mercado atual, não são muito conhecidos. Além disso, existem poucos trabalhos científicos relacionados à implantação e ao auxílio deste tipo sistema no gerenciamento de dados em ambiente de engenharia simultânea. Neste trabalho procura-se levantar, segundo a bibliografia, as necessidades de gerenciamento de dados em ambientes de engenharia simultânea, comparar as necessidades de gerenciamento de dados encontradas na bibliografia com as necessidades de um caso de implantação real, levantar quais funcionalidades de sistemas PDM (product data management) suprem as necessidades encontradas, e caracterizar um projeto de implantação real de um sistema PDM em um ambiente de engenharia simultânea. / The concurrent engineering and the product data management systems(PDM), although being a precious aid to allow the companies to face the new survival conditions in the current market, are not very well-known. Besides that, few scientific works related to the implementation of this kind of system and their usage in the data management of data in a concurrent engineering environment are available. The objectives of this work are: to rise, according to the bibliography, the needs of data management in a concurrent engineering environment, to compare those needs with the ones of a real implementation case, to rise which functionality of PDM (Product Data Management) systems supply the founded needs, and to characterize a project of a real PDM system implementation in an environment of concurrent engineering.

Management of research data is globally being seen as part of good research practice. As a result of this, funders are increasingly insisting on proof of good research data management (RDM) practices when funding proposals are submitted. This study aimed at establishing the data management practices of emerging researchers at the Council for Scientific and Industrial Research (CSIR), South Africa. With no official RDM procedures currently being implemented at the CSIR, it was hoped that by gaining information about the RDM practices of emerging CSIR researchers, as well as insight into the RDM challenges experienced by them, this researcher would be able to put forward recommendations enabling the establishing of an RDM regime at the CSIR. The study aimed at answering several research questions. The main research question was: How can an organisation like the CSIR ensure that future researchers apply best practices when managing the CSIR’s research data? Five research sub-questions were identified: 1. What are the international RDM requirements, standards, best practices and expectations that are being developed? 2. What data practices need more formalised support: at CSIR, nationally, internationally? 3. What data are collected and held by emerging researchers in the CSIR? 4. What are the current RDM practices and themes among emerging researchers in the CSIR? 5. What are the RDM-related challenges, issues and concerns facing emerging researchers at the CSIR? A total of 48 emerging researchers from the Council for Scientific and Industrial Research (CSIR), South Africa completed an online survey investigating their RDM practices. RDM practices investigated included the use of data management plans, data storage and backup locations, creation of metadata, metadata standard adherence, and data sharing practices. Challenges faced when managing research data, as well as RDM needs and requirements, also formed part of the survey. Results of the online questionnaire revealed that the RDM practices of the group studied do not show to differ significantly from experienced CSIR researchers, or from researchers studied elsewhere on the globe. Findings enabled this researcher to put forward several recommendations which would assist in the implementing of a formalised RDM structure at the CSIR. Recommendations addressed, but were not limited to: formalization of RDM procedures, RDM marketing, and RDM training. / Dissertation (MIS)--University of Pretoria, 2016. / Information Science / MIS / Unrestricted

Abstract As the business complexity, number of customers continues to grow and customers evolve into multinational organisations that operate across borders, many companies are faced with great challenges in the way they manage their customer data. In today’s business, a single customer may have a relationship with several entities of an organisation, which means that the customer data is collected through different channels. One customer may be described in different ways by each entity, which makes it difficult to obtain a unified view of the customer. In companies where there are several sources of data and the data is distributed to several systems, data environments become heterogenic. In this state, customer data is often incomplete, inaccurate and inconsistent throughout the company. This thesis aims to study how organisations with heterogeneous customer data sources implement the Master Data Management (MDM) concept to achieve and maintain high customer data quality. The purpose is to provide recommendations for how to achieve successful customer data management using MDM based on existing literature related to the topic and an interview-based empirical study. Successful customer data management is more of an organisational issue than a technological one and requires a top-down approach in order to develop a common strategy for an organisation’s customer data management. Proper central assessment and maintenance processes that can be adjusted according to the entities’ needs must be in place. Responsibilities for the maintenance of customer data should be delegated to several levels of an organisation in order to better manage customer data.

Because of the importance of Research Data Management (RDM) in the life sciences, where vast amounts of research data in different complex formats are being produced, this study aimed to assess the state of RDM readiness in the life sciences at Wits to ascertain what support is needed with regards to RDM. In order to achieve the aim, the current RDM practices and needs of researchers, as well as the challenges they face, were investigated. The Jisc Research Data Lifecycle (Jisc, 2021a) was used to guide the literature review, frame data collection, analyse data and advise on some of the main findings and recommendations. A mixed methods approach and an explanatory sequential design were used to achieve the research objectives. For the quantitative phase of research, an online questionnaire was used to collect data. As the total target population (282) was not big, a census was conducted. The questionnaire was administered using SurveyMonkey software. During the qualitative part of the research, semi-structured interviews were used to explain the quantitative results. Five participants were purposively sampled to take part in interviews. The statistical package, MS Excel, was used to analyse quantitative data whilst qualitative data were analysed by thematic analysis. The study showed that life sciences researchers at Wits have adopted many RDM practices, and researchers are increasingly becoming aware of the importance of the openness of data. However, they are dealing with similar RDM issues as their peers worldwide. Results highlighted challenges of, amongst others, the lack of an RDM policy as well as the lack of, or unawareness of, appropriate RDM training and support at Wits. As formal implementation of RDM still needs to take place at Wits, it is recommended that Wits puts an RDM policy in place, followed by suitable RDM infrastructure and awareness making of current services.

Traditional manufacturing firms today are on the way to extend their product portfolio with services in order to broaden their offering and to strengthen the relation to their customers. New services are being developed, old services are substituted and replaced and existing services adapted, while the amount of services tends to increase in total. As the number of services increases, so does the administrative work and complexity that comes along with services. This thesis aims at investigating how to handle the rising amount of data internally created around services and which service data need to be managed to describe a service. The objective is to find a way that allows Volvo to focus on the development and adaptation of services and not being distracted with work on administration of data around services, but rather focus on value-creating work. Different types of metadata are identified that describe a service along its lifecycle, e.g. versions, lifecycle stages, dependencies to other services, etc. The way is summarized in a framework that illustrates which data are needed and how this information can be managed. Besides the literature study, a benchmarking study is conducted within three other, global operating companies that have made their way from a manufacturer to a service provider or are still on the way to become one. The objective is to analyze their way on handling service data and to compare those to the one of Volvo and use the gathered information to provide a recommendation that suits Volvo’s way of working. The benchmarking study aimed at investigating the current status and future objectives of the firms through interviews with service experts. The results show that there is a great variation among the firms, including Volvo. While the third benchmarking firm has a mature way of dealing with services in their administration, Volvo and the benchmarking firm 2 are at an early stage in the servitization process. However, benchmarking firm 1 has an intermediate state that is strongly supported by an existing ERP system that is capable of registering service data. The Volvo Group acts in a strong competitive market that demands fleet management services, maintenance agreements and repair contracts in addition to the product offering. A lean way of service data management with high efficiency allows Volvo to compete in this market successfully. Services can be developed quicker by reuse of existing modules, a tracking of changes allows users to see the evolution of a particular service and the impact of a change can be estimated with the right system that considers dependencies among services. A solution to improve in the service data management is presented in the developed framework of this thesis. It is one way to account for the growing number of data and services and to simplify the daily work with services with a software-based solution. It can help to approach the growing number of services and to structure them in a daily work environment as well as from a more holistic portfolio management perspective.

Published Article / Radio-frequency Identification (RFID) technology promises to revolutionize business processes. While RFID technology is improving rapidly, a reliable deployment of this technology is still a significant challenge impeding its widespread adoption. In this paper we provide a brief overview of some common fundamental characteristics of RFID data and devices, which pose significant challenges in the design of RFID middleware systems. In addition, the development of a multi-agent RFID middleware solution to address the RFID data and device management challenges is discussed.

International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A synopsis of the very recent evolution of telemetry chart recorders from “closed” chart paper output devices to powerful “open” Data Management Systems. A Data Management System (DMS) is defined as one which incorporates a video screen for waveform preview and monitoring, direct connection of hard or optical disk via SCSI for real-time data archiving, and DR11 digital interfacing. The DMS concept of providing real-time waveform monitoring independent of hard copy recording is discussed, as well as the capabilities of the hard copy recorder. The realities of budget shortfalls makes wholesale system upgrades to eliminate DAC’s entirely difficult at best. These concerns—and a potential remedy: a DMS which accepts any mix of analog and digital waveforms—are reviewed. Objectives: How DMS’s can be integrated with existing telemetry systems, encompass the functionality of conventional recorders and add new capabilities, with an emphasis on how data can be digitally pre-formatted in real-time, simplifying—or even eliminating—post-mission reduction and analysis. A demonstration of how a video display allows real-time trace viewing—a major weakness of conventional thermal array recorders.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The production of flight test plans have received attention from many research workers due to increasing complexity of testing facilities, the complex demands proposed by customers and the large volume of data required from test flights. The paper opens with a review of research work conducted by other authors who have contributed to ameliorating the preparation of flight test plans and processing the resulting data. This is followed by a description of a specific problem area; efficiently configuring the flight test data telemetry format (defined by the relevant standards while meeting user requirements of sampling rate and PCM word length). Following a description of a current semi-automated system, the authors propose an enhanced approach and demonstrate its efficiency through two case studies.

Doctor of Philosophy / Department of Diagnostic Medicine/Pathobiology / Robert L. Larson / Bradley J. White / Utilization of data analytics allows for rapid and real-time decision making in the food animal production industry. The objective of my research was to implement and utilize different data analytic strategies in multiple sectors of the beef cattle industry in order to determine management, health, and performance strategies. A retrospective analysis using reproductive and genomic records demonstrated that a bull will sire a larger number of calves in a multiple sire-pasture compared to other bulls in the same pasture. A further study was performed to determine if behavior differences existed among bulls in a multiple-sire pasture, and the ability of accelerometers to predict breeding behaviors. Machine learning techniques used classifiers on accelerometer data to predict behavior events lying, standing, walking, and mounting. The classifiers were able to accurately predict lying and standing, but walking and mounting resulted in a lower predictable accuracy due to the extremely low prevalence of these behaviors. Finally, a new form of meta-analysis to the veterinary literature, a mixed treatment comparison, was able to accurately identify differences in metaphylactic antimicrobials on outcomes of bovine respiratory disease morbidity, mortality, and retreatment morbidity. The meta-analysis was not successful in determining the effects of metaphylactic antimicrobials on performance outcomes.

Mining spatio-temporal reachable regions aims to find a set of road segments from massive trajectory data, that are reachable from a user-specified location and within a given temporal period. Accurately extracting such spatio-temporal reachable area is vital in many urban applications, e.g., (i) location-based recommendation, (ii) location-based advertising, and (iii) business coverage analysis. The traditional approach of answering such queries essentially performs a distance-based range query over the given road network, which have two main drawbacks: (i) it only works with the physical travel distances, where the users usually care more about dynamic traveling time, and (ii) it gives the same result regardless of the querying time, where the reachable area could vary significantly with different traffic conditions. Motivated by these observations, in this thesis, we propose a data- driven approach to formulate the problem as mining actual reachable region based on real historical trajectory dataset. The main challenge in our approach is the system efficiency, as verifying the reachability over the massive trajectories involves huge amount of disk I/Os. In this thesis, we develop two indexing structures: 1) spatio-temporal index (ST-Index) and 2) connection index (Con-Index) to reduce redundant trajectory data access operations. We also propose a novel query processing algorithm with: 1) maximum bounding region search, which directly extracts a small searching region from the index structure and 2) trace back search, which refines the search results from the previous step to find the final query result. Moreover, our system can also efficiently answer the spatio-temporal reachability query with multiple query locations by skipping the overlapped area search. We evaluate our system extensively using a large-scale real taxi trajectory data in Shenzhen, China, where results demonstrate that the proposed algorithms can reduce 50%-90% running time over baseline algorithms.