Um modelo de EIS - enterprise information system - que identifica características para comportamentos proativos na recuperação de informações

Este documento constitui uma dissertação de mestrado, requisito parcial para a obtenção do grau de Mestre em Administração pela Universidade Federal do Rio Grande do Sul. O tema da pesquisa é o relacionamento existente entre as características técnicas de um projeto de sistema de informação e apoio à decisão e os comportamentos dos usuários no seu uso. O objetivo é desenvolver e apresentar um modelo conceitual de EIS (“Enterprise Information Systems”), a partir da literatura, das tendências tecnológicas e de estudos de caso, que identifique características para comportamentos proativos dos usuários na recuperação de informações. Adotou-se o conceito de comportamento proativo na recuperação de informações como a combinação das categorias exploração de dados e busca focada. Entre os principais resultados, pode-se destacar a definição de categorias relacionadas com as características dos sistemas - flexibilidade, integração e apresentação - e de categorias relacionadas com os comportamentos dos usuários na recuperação de informações - exploração de dados e busca focada, bem como a apresentação de um modelo conceitual para sistemas EIS. Pode-se destacar também a exploração de novas técnicas para análise qualitativa de dados, realizada com o objetivo de buscar uma maior preservação do contexto nos estudos de caso.

Uma arquitetura baseada em agentes para configuração dinâmica de aplicações inteligentes em ambiente foundation fieldbus

The industrial automation is directly linked to the development of information tecnology. Better hardware solutions, as well as improvements in software development methodologies make possible the rapid growth of the productive process control. In this thesis, we propose an architecture that will allow the joining of two technologies in hardware (industrial network) and software field (multiagent systems). The objective of this proposal is to join those technologies in a multiagent architecture to allow control strategies implementations in to field devices. With this, we intend develop an agents architecture to detect and solve problems which may occur in the industrial network environment. Our work ally machine learning with industrial context, become proposed multiagent architecture adaptable to unfamiliar or unexpected production environment. We used neural networks and presented an allocation strategies of these networks in industrial network field devices. With this we intend to improve decision support at plant level and allow operations human intervention independent

Sistemas computacionais espaço-temporais para toma de decisão em questões ambientais relacionadas à indústria de petróleo e gás

The area between São Bento do Norte and Macau cities, located in the northern coast of the Rio Grande do Norte State is submitted to intense and constant processes of littoral and aeolian transport, causing erosion, alterations in the sediments balance and modifications in the shoreline. Beyond these natural factors, the human interference is huge in the surroundings, composed by sensitive places, due to the existence of the Guamaré Petroliferous Pole, RN, the greater terrestrial oil producing in Brazil, besides the activities of the salt companies and shrimp farms. This socioeconomic-environmental context justifies the elaboration of strategies of environmental monitoring of that coastal area. In the environmental monitoring of coastal strips, submitted to human impacts, the use of multi-sources and multitemporal data integrated through a Spatio- Temporal Database that allows the multiuser friendly access. The objective was to use the potential of the computational systems as important tools the managers of environmental monitoring. The stored data in the form of a virtual library aid in making decisions from the related results and presented in different formats. This procedure enlarges the use of the data in the preventive attendance, in the planning of future actions and in the definition of new lines of researches on the area, in a multiscale approach. Another activity of this Thesis consisted on the development of a computational system to automate the process to elaborate Oil-Spill Environmental Sensitivity Maps, based on the temporal variations that some coastal ecosystems present in the sensibility to the oil. The maps generated in this way, based on the methodology proposed by the Ministério do Meio Ambiente, supply more updated information about the behavior of the ecosystem, as a support to the operations in case of oil spill. Some parameters, such as the hydrodynamic data, the declivity of the beach face, types of resources in risk (environmental, economical, human or cultural) and use and occupation of the area are some of the essential basic information in the elaboration of the sensitivity maps, which suffer temporal alterations.In this way, the two computational systems developed are considered support systems to the decision, because they provide operational subsidies to the environmental monitoring of the coastal areas, considering the transformations in the behavior of coastal elements resulting from temporal changes related the human and/or natural interference of the environment

Sistema integrado para tomada de decisão espacial em situações de derramamento de óleo no litoral norte do Estado do RN

The northern coast of Rio Grande do Norte State (RN) shows areas of Potiguar basin with high activity in petroleum industry. With the goal of avoiding and reducing the accident risks with oil it is necessary to understand the natural vulnerability, mapping natural resources and monitoring the oil spill. The use of computational tools for environmental monitoring makes possible better analyses and decisions in political management of environmental preservation. This work shows a methodology for monitoring of environment impacts, with purpose of avoiding and preserving the sensible areas in oil contact. That methodology consists in developing and embedding an integrated computational system. Such system is composed by a Spatial Decision Support System (SDSS). The SDSS shows a computational infrastructure composed by Web System of Geo-Environmental and Geographic Information - SWIGG , the System of Environmental Sensibility Maps for Oil Spill AutoMSA , and the Basic System of Environmental Hydrodynamic ( SisBAHIA a System of Modeling and Numerical Simulating SMNS). In a scenario of oil spill occurred coastwise of Rio Grande do Norte State s northern coast, the integration of such systems will give support to decision agents for managing of environmental impacts. Such support is supplied through a system of supporting to spatial decisions

Um sistema de apoio a decisão utilizado no planejamento e controle de produção de uma empresa aeronaútica: Reinaldo de Almeida. -

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estratégia de otimização para a melhoria da interpretabilidade de redes bayesianas: aplicações em sistemas elétricos de potência

A investigação de métodos, técnicas e ferramentas que possam apoiar os processos decisórios em sistemas elétricos de potência, em seus vários setores, é um tema que tem despertado grande interesse. Esse suporte à decisão pode ser efetivado mediante o emprego de vários tipos de técnicas, com destaque para aquelas baseadas em inteligência computacional, face à grande aderência das mesmas a domínios com incerteza. Nesta tese, são utilizadas as redes Bayesianas para a extração de modelos de conhecimento a partir dos dados oriundos de sistemas elétricos de potência. Além disso, em virtude das demandas destes sistemas e de algumas limitações impostas às inferências em redes bayesianas, é desenvolvido um método original, utilizando algoritmos genéticos, capaz de estender o poder de compreensibilidade dos padrões descobertos por essas redes, por meio de um conjunto de procedimentos de inferência em redes bayesianas para a descoberta de cenários que propiciem a obtenção de um valor meta, considerando a incorporação do conhecimento a priori do especialista, a identificação das variáveis mais influentes para obtenção desses cenários e a busca de cenários ótimos que estabeleçam valores, definidos e ponderados pelo usuário/especialista, para mais de uma variável meta.

A support system that delineates location-choice sets for firms seeking office space

Factors influencing the location decisions of offices include traffic, accessibility, employment conditions, economic prospects and land-use policies. Hence tools for supporting real-estate managers and urban planners in such multidimensional decisions may be useful. Accordingly, the objective of this study is to develop a GIS-based tool to support firms who seek office accommodation within a given regional or national study area. The tool relies on a matching approach, in which a firm's characteristics (demand) on the one hand, and environmental conditions and available office spaces (supply) on the other, are analyzed separately in a first step, after which a match is sought. That is, a suitability score is obtained for every firm and for every available office space by applying some value judgments (satisfaction, utility etc.). The latter are powered by a focus on location aspects and expert knowledge about the location decisions of firms/organizations with respect to office accommodation as acquired from a group of real-estate advisers; it is stored in decision tables, and they constitute the core of the model. Apart from the delineation of choice sets for any firm seeking a location, the tool supports two additional types of queries. Firstly, it supports the more generic problem of optimally allocating firms to a set of vacant locations. Secondly, the tool allows users to find firms which meet the characteristics of any given location. Moreover, as a GIS-based tool, its results can be visualized using GIS features which, in turn, facilitate several types of analyses.

Development of quantitative methods for sustainability assessment in process industry

The increasing aversion to technological risks of the society requires the development of inherently safer and environmentally friendlier processes, besides assuring the economic competitiveness of the industrial activities. The different forms of impact (e.g. environmental, economic and societal) are frequently characterized by conflicting reduction strategies and must be holistically taken into account in order to identify the optimal solutions in process design. Though the literature reports an extensive discussion of strategies and specific principles, quantitative assessment tools are required to identify the marginal improvements in alternative design options, to allow the trade-off among contradictory aspects and to prevent the “risk shift”. In the present work a set of integrated quantitative tools for design assessment (i.e. design support system) was developed. The tools were specifically dedicated to the implementation of sustainability and inherent safety in process and plant design activities, with respect to chemical and industrial processes in which substances dangerous for humans and environment are used or stored. The tools were mainly devoted to the application in the stages of “conceptual” and “basic design”, when the project is still open to changes (due to the large number of degrees of freedom) which may comprise of strategies to improve sustainability and inherent safety. The set of developed tools includes different phases of the design activities, all through the lifecycle of a project (inventories, process flow diagrams, preliminary plant lay-out plans). The development of such tools gives a substantial contribution to fill the present gap in the availability of sound supports for implementing safety and sustainability in early phases of process design. The proposed decision support system was based on the development of a set of leading key performance indicators (KPIs), which ensure the assessment of economic, societal and environmental impacts of a process (i.e. sustainability profile). The KPIs were based on impact models (also complex), but are easy and swift in the practical application. Their full evaluation is possible also starting from the limited data available during early process design. Innovative reference criteria were developed to compare and aggregate the KPIs on the basis of the actual sitespecific impact burden and the sustainability policy. Particular attention was devoted to the development of reliable criteria and tools for the assessment of inherent safety in different stages of the project lifecycle. The assessment follows an innovative approach in the analysis of inherent safety, based on both the calculation of the expected consequences of potential accidents and the evaluation of the hazards related to equipment. The methodology overrides several problems present in the previous methods proposed for quantitative inherent safety assessment (use of arbitrary indexes, subjective judgement, build-in assumptions, etc.). A specific procedure was defined for the assessment of the hazards related to the formations of undesired substances in chemical systems undergoing “out of control” conditions. In the assessment of layout plans, “ad hoc” tools were developed to account for the hazard of domino escalations and the safety economics. The effectiveness and value of the tools were demonstrated by the application to a large number of case studies concerning different kinds of design activities (choice of materials, design of the process, of the plant, of the layout) and different types of processes/plants (chemical industry, storage facilities, waste disposal). An experimental survey (analysis of the thermal stability of isomers of nitrobenzaldehyde) provided the input data necessary to demonstrate the method for inherent safety assessment of materials.

Analysis and developments of uncertainty processors for real time flood forecasting

The hydrologic risk (and the hydro-geologic one, closely related to it) is, and has always been, a very relevant issue, due to the severe consequences that may be provoked by a flooding or by waters in general in terms of human and economic losses. Floods are natural phenomena, often catastrophic, and cannot be avoided, but their damages can be reduced if they are predicted sufficiently in advance. For this reason, the flood forecasting plays an essential role in the hydro-geological and hydrological risk prevention. Thanks to the development of sophisticated meteorological, hydrologic and hydraulic models, in recent decades the flood forecasting has made a significant progress, nonetheless, models are imperfect, which means that we are still left with a residual uncertainty on what will actually happen. In this thesis, this type of uncertainty is what will be discussed and analyzed. In operational problems, it is possible to affirm that the ultimate aim of forecasting systems is not to reproduce the river behavior, but this is only a means through which reducing the uncertainty associated to what will happen as a consequence of a precipitation event. In other words, the main objective is to assess whether or not preventive interventions should be adopted and which operational strategy may represent the best option. The main problem for a decision maker is to interpret model results and translate them into an effective intervention strategy. To make this possible, it is necessary to clearly define what is meant by uncertainty, since in the literature confusion is often made on this issue. Therefore, the first objective of this thesis is to clarify this concept, starting with a key question: should be the choice of the intervention strategy to adopt based on the evaluation of the model prediction based on its ability to represent the reality or on the evaluation of what actually will happen on the basis of the information given by the model forecast? Once the previous idea is made unambiguous, the other main concern of this work is to develope a tool that can provide an effective decision support, making possible doing objective and realistic risk evaluations. In particular, such tool should be able to provide an uncertainty assessment as accurate as possible. This means primarily three things: it must be able to correctly combine all the available deterministic forecasts, it must assess the probability distribution of the predicted quantity and it must quantify the flooding probability. Furthermore, given that the time to implement prevention strategies is often limited, the flooding probability will have to be linked to the time of occurrence. For this reason, it is necessary to quantify the flooding probability within a horizon time related to that required to implement the intervention strategy and it is also necessary to assess the probability of the flooding time.

Automatic induction of lexical features

This thesis concerns artificially intelligent natural language processing systems that are capable of learning the properties of lexical items (properties like verbal valency or inflectional class membership) autonomously while they are fulfilling their tasks for which they have been deployed in the first place. Many of these tasks require a deep analysis of language input, which can be characterized as a mapping of utterances in a given input C to a set S of linguistically motivated structures with the help of linguistic information encoded in a grammar G and a lexicon L: G + L + C → S (1) The idea that underlies intelligent lexical acquisition systems is to modify this schematic formula in such a way that the system is able to exploit the information encoded in S to create a new, improved version of the lexicon: G + L + S → L' (2) Moreover, the thesis claims that a system can only be considered intelligent if it does not just make maximum usage of the learning opportunities in C, but if it is also able to revise falsely acquired lexical knowledge. So, one of the central elements in this work is the formulation of a couple of criteria for intelligent lexical acquisition systems subsumed under one paradigm: the Learn-Alpha design rule. The thesis describes the design and quality of a prototype for such a system, whose acquisition components have been developed from scratch and built on top of one of the state-of-the-art Head-driven Phrase Structure Grammar (HPSG) processing systems. The quality of this prototype is investigated in a series of experiments, in which the system is fed with extracts of a large English corpus. While the idea of using machine-readable language input to automatically acquire lexical knowledge is not new, we are not aware of a system that fulfills Learn-Alpha and is able to deal with large corpora. To instance four major challenges of constructing such a system, it should be mentioned that a) the high number of possible structural descriptions caused by highly underspeci ed lexical entries demands for a parser with a very effective ambiguity management system, b) the automatic construction of concise lexical entries out of a bulk of observed lexical facts requires a special technique of data alignment, c) the reliability of these entries depends on the system's decision on whether it has seen 'enough' input and d) general properties of language might render some lexical features indeterminable if the system tries to acquire them with a too high precision. The cornerstone of this dissertation is the motivation and development of a general theory of automatic lexical acquisition that is applicable to every language and independent of any particular theory of grammar or lexicon. This work is divided into five chapters. The introductory chapter first contrasts three different and mutually incompatible approaches to (artificial) lexical acquisition: cue-based queries, head-lexicalized probabilistic context free grammars and learning by unification. Then the postulation of the Learn-Alpha design rule is presented. The second chapter outlines the theory that underlies Learn-Alpha and exposes all the related notions and concepts required for a proper understanding of artificial lexical acquisition. Chapter 3 develops the prototyped acquisition method, called ANALYZE-LEARN-REDUCE, a framework which implements Learn-Alpha. The fourth chapter presents the design and results of a bootstrapping experiment conducted on this prototype: lexeme detection, learning of verbal valency, categorization into nominal count/mass classes, selection of prepositions and sentential complements, among others. The thesis concludes with a review of the conclusions and motivation for further improvements as well as proposals for future research on the automatic induction of lexical features.

Sustainable improvement of water networks : to achieve best decisions for rehabilitation plans using advanced GIS and statistical techniques

Many of developing countries are facing crisis in water management due to increasing of population, water scarcity, water contaminations and effects of world economic crisis. Water distribution systems in developing countries are facing many challenges of efficient repair and rehabilitation since the information of water network is very limited, which makes the rehabilitation assessment plans very difficult. Sufficient information with high technology in developed countries makes the assessment for rehabilitation easy. Developing countries have many difficulties to assess the water network causing system failure, deterioration of mains and bad water quality in the network due to pipe corrosion and deterioration. The limited information brought into focus the urgent need to develop economical assessment for rehabilitation of water distribution systems adapted to water utilities. Gaza Strip is subject to a first case study, suffering from severe shortage in the water supply and environmental problems and contamination of underground water resources. This research focuses on improvement of water supply network to reduce the water losses in water network based on limited database using techniques of ArcGIS and commercial water network software (WaterCAD). A new approach for rehabilitation water pipes has been presented in Gaza city case study. Integrated rehabilitation assessment model has been developed for rehabilitation water pipes including three components; hydraulic assessment model, Physical assessment model and Structural assessment model. WaterCAD model has been developed with integrated in ArcGIS to produce the hydraulic assessment model for water network. The model have been designed based on pipe condition assessment with 100 score points as a maximum points for pipe condition. As results from this model, we can indicate that 40% of water pipeline have score points less than 50 points and about 10% of total pipes length have less than 30 score points. By using this model, the rehabilitation plans for each region in Gaza city can be achieved based on available budget and condition of pipes. The second case study is Kuala Lumpur Case from semi-developed countries, which has been used to develop an approach to improve the water network under crucial conditions using, advanced statistical and GIS techniques. Kuala Lumpur (KL) has water losses about 40% and high failure rate, which make severe problem. This case can represent cases in South Asia countries. Kuala Lumpur faced big challenges to reduce the water losses in water network during last 5 years. One of these challenges is high deterioration of asbestos cement (AC) pipes. They need to replace more than 6500 km of AC pipes, which need a huge budget to be achieved. Asbestos cement is subject to deterioration due to various chemical processes that either leach out the cement material or penetrate the concrete to form products that weaken the cement matrix. This case presents an approach for geo-statistical model for modelling pipe failures in a water distribution network. Database of Syabas Company (Kuala Lumpur water company) has been used in developing the model. The statistical models have been calibrated, verified and used to predict failures for both networks and individual pipes. The mathematical formulation developed for failure frequency in Kuala Lumpur was based on different pipeline characteristics, reflecting several factors such as pipe diameter, length, pressure and failure history. Generalized linear model have been applied to predict pipe failures based on District Meter Zone (DMZ) and individual pipe levels. Based on Kuala Lumpur case study, several outputs and implications have been achieved. Correlations between spatial and temporal intervals of pipe failures also have been done using ArcGIS software. Water Pipe Assessment Model (WPAM) has been developed using the analysis of historical pipe failure in Kuala Lumpur which prioritizing the pipe rehabilitation candidates based on ranking system. Frankfurt Water Network in Germany is the third main case study. This case makes an overview for Survival analysis and neural network methods used in water network. Rehabilitation strategies of water pipes have been developed for Frankfurt water network in cooperation with Mainova (Frankfurt Water Company). This thesis also presents a methodology of technical condition assessment of plastic pipes based on simple analysis. This thesis aims to make contribution to improve the prediction of pipe failures in water networks using Geographic Information System (GIS) and Decision Support System (DSS). The output from the technical condition assessment model can be used to estimate future budget needs for rehabilitation and to define pipes with high priority for replacement based on poor condition. rn

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Control concept for autonomous changeable material flow systems

Today’s material flow systems for mass customization or dynamic productions are usually realized with manual transportation systems. However new concepts in the domain of material flow and device control like function-oriented modularization and intelligent multi-agent-systems offer the possibility to employ changeable and automated material flow systems in dynamic production structures. These systems need the ability to react on unplanned and unexpected events autonomously.

Tools for better SLM knowledge management and informed decision-making in addressing land degradation at different scales: the WOCAT–LADA–DESIRE methodology

Desertification research conventionally focuses on the problem – that is, degradation – while neglecting the appraisal of successful conservation practices. Based on the premise that Sustainable Land Management (SLM) experiences are not sufficiently or comprehensively documented, evaluated, and shared, the World Overview of Conservation Approaches and Technologies (WOCAT) initiative (www.wocat.net), in collaboration with FAO’s Land Degradation Assessment in Drylands (LADA) project (www.fao.org/nr/lada/) and the EU’s DESIRE project (http://www.desire-project.eu/), has developed standardised tools and methods for compiling and evaluating the biophysical and socio-economic knowledge available about SLM. The tools allow SLM specialists to share their knowledge and assess the impact of SLM at the local, national, and global levels. As a whole, the WOCAT–LADA–DESIRE methodology comprises tools for documenting, self-evaluating, and assessing the impact of SLM practices, as well as for knowledge sharing and decision support in the field, at the planning level, and in scaling up identified good practices. SLM depends on flexibility and responsiveness to changing complex ecological and socioeconomic causes of land degradation. The WOCAT tools are designed to reflect and capture this capacity of SLM. In order to take account of new challenges and meet emerging needs of WOCAT users, the tools are constantly further developed and adapted. Recent enhancements include tools for improved data analysis (impact and cost/benefit), cross-scale mapping, climate change adaptation and disaster risk management, and easier reporting on SLM best practices to UNCCD and other national and international partners. Moreover, WOCAT has begun to give land users a voice by backing conventional documentation with video clips straight from the field. To promote the scaling up of SLM, WOCAT works with key institutions and partners at the local and national level, for example advisory services and implementation projects. Keywords: Sustainable Land Management (SLM), knowledge management, decision-making, WOCAT–LADA–DESIRE methodology.

The Houston Academy of Medicine--Texas Medical Center Library management information system.

A management information system (MIS) provides a means for collecting, reporting, and analyzing data from all segments of an organization. Such systems are common in business but rare in libraries. The Houston Academy of Medicine-Texas Medical Center Library developed an MIS that operates on a system of networked IBM PCs and Paradox, a commercial database software package. The data collected in the system include monthly reports, client profile information, and data collected at the time of service requests. The MIS assists with enforcement of library policies, ensures that correct information is recorded, and provides reports for library managers. It also can be used to help answer a variety of ad hoc questions. Future plans call for the development of an MIS that could be adapted to other libraries' needs, and a decision-support interface that would facilitate access to the data contained in the MIS databases.