Sustainable forest management using decision theaters : rethinking participatory planning

Involving stakeholders in the decision-making process can be very complex and time consuming. Decision theater (DT), which enables the combination of visualization and decision modeling capabilities together with human capacity of insight and interaction, is proposed for addressing this challenging problem in the forest sector. A generic framework for designing DTs to support participatory planning in the forest sector is proposed. To enable DT implementation and support decision-making in the DT in the province of Québec, Canada, the conceptual design of a decision-support system called Forest Community-DSS (FC-DSS) has been developed. Implementing FC-DSS along with other technologies in a DT environment can contribute to engage the stakeholders in the decision-making process by increasing participation frequency, collecting more inputs from the stakeholders, supporting the development and evaluation of alternative options and the selection of preferred alternatives. A DT-based collaboration approach would contribute to address the multiple issues of the stakeholders involved in participatory planning in Québec. Other Canadian provinces and other countries facing similar issues can benefit from the proposed approach

A Historical Observation of the Intellectual and Institutional Structures of the Field

In this paper, we examine the evolution of the institutional and intellectual structures of the IS field. We argue that, though the field’s institutional structures—academic programs, journals, conferences, and professional associations—have developed admirably, the state of the field’s intellectual structure is less clear. We employ a co-citation lens to analyze the development and evolution of subfields across three periods. We rely on Culnan’s (1987) second co-citation study as a point of departure for our analysis. We then extend her work through two additional studies that individually assess the state of subfield development at distinct periods during the field’s history. Over the three periods, we note that the field has experienced change in subfield diversity and cohesion. Culnan’s study exhibits low levels of cohesion and diversity among topics. Our first study shows continued isolation but growth in subfield diversity. This period is indicative of a fragmented adhocracy. Our second study suggests increasing levels of integration despite only a slight reduction in subfield diversity. While we largely only describe the field’s evolution, any assessment of whether this evolution represents a positive or negative trajectory for the field will be subject to interpretation and debate

A Spatial Decision Support System for Oil Spill Response and Recovery

abstract: Coastal areas are susceptible to man-made disasters, such as oil spills, which not only have a dreadful impact on the lives of coastal communities and businesses but also have lasting and hazardous consequences. The United States coastal areas, especially the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations that resulted in major economic and ecological losses. These disasters affected the oil, housing, forestry, tourism, and fishing industries with overall costs exceeding billions of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been done with respect to oil spill simulation techniques, spatial optimization models, and innovative strategies to deal with spill response and planning efforts. However, most of the research done in those areas is done independently of each other, leaving a conceptual void between them. In the following work, this thesis presents a Spatial Decision Support System (SDSS), which efficiently integrates the independent facets of spill modeling techniques and spatial optimization to enable officials to investigate and explore the various options to clean up an offshore oil spill to make a more informed decision. This thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to spatially optimize the response efforts. The results of this combination are visualized in the SDSS, featuring geographical maps, so the boat ramps from which the response should be launched can be easily identified along with the amount of oil that hits the shore thereby visualizing the intensity of the impact of the spill in the coastal areas for various cleanup targets.Dissertation/ThesisMasters Thesis Computer Science 201

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem

Developing A New Decision Support System for University Student Recruitment

This paper investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The paper describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs’ analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The paper investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a DW structure and knowledge management tools within the decision making framework. 2000 records have been used to build and test the data mining techniques within the KDD process. The records were drawn from the Arab Academy for Science and Technology and Maritime Transport (AASTMT) students’ database (DB). Moreover, the paper has analyzed the key characteristics of DW and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a DW for the Egyptian Universities that handle their admission and registration related archival data. The decision makers’ potential benefits of the DW within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MS-SQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUSTAN Graphics (5.0) by CLUSTAN software will also be used as a clustering package. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems

Factors That Influence The Adoption Of Geographic Information Systems In A Professional Work Environment: A Study Of The Property Assessment Profession

The adoption of Geographic Information Systems (GIS) Technology has been emerging in many professional work environments — including the property assessment discipline. Although many uses of GIS have been thoroughly documented throughout the literature in a variety of disciplines, there has been little research on the perceived factors that influence its adoption in professional work settings. The purpose of this research is to assess factors that influence the adoption of geographic information systems technology in a professional work environment. The work environment being studied is the property assessment profession. An online survey was sent out to property assessment professionals from around the United States and other countries that have access to International Association of Assessing Officers (IAAO) correspondence which collected data on constructs of perceived ease of use, perceived usefulness, efficiency, attitude, social influence, and intent to use GIS technology. A structural equation model was constructed based on an extension of the theoretical framework of the technology acceptance model (TAM). After minor revisions, the extended TAM accounted for 86% of the variance within the model indicating good fit in predicting assessment professional’s intent to use GIS technology. Additionally, perceived quality of training was found to be a significant determinant of success with regard to all adoption constructs, and simple GIS applications used for visualization and land records management were the most utilized in the field. With these findings, organizations such as the IAAO would be able to design best practices and educational opportunities within the professional work environment and provide adequate guidance and support. This in turn may produce a positive impact on the innovation and influx of GIS usage within the property assessment field to produce more accurate and equitable assessments

Building Consensus using a Collaborative Spatial Multi-Criteria Analysis System

This thesis studies the use of a collaborative spatial Multi-Criteria Analysis tool in site evaluation with multiple participants. The approach is situated within the context of three concepts of space, choice and participation, and is informed by fields as diverse as Decision-Making, Participatory Planning, Geographical Information Systems, Decision Support Systems, Voting, and Group Collaboration. A collaborative spatial Multi-Criteria Analysis software tool called MapChoice was designed for this thesis, built upon open source components and featuring easy-to-use decision support functionality in both single-user and collaborative modes. MapChoice was then evaluated in a real-world site selection situation with a case study on the location of much-needed affordable housing in the Town of Collingwood, Ontario. Based on previous discussions and workshops on the project, a workshop was held with a group of community housing advocates to compare a set of possible sites for an affordable housing project according to a set of spatial and aspatial criteria. The study indicates that a collaborative spatial MCA approach can be used in dealing with complex planning problems, and that it has the potential to contribute to improved consensus between participants

The Information Systems Field: Making a Case for Maturity and Contribution

In this paper, I explore the question of whether the field is progressing well. In doing so, I base my opinion on anchors from four independent studies that I have conducted over the years. These studies treat the field in different ways: as an aggregator of terms, a complex adaptive system, part of a knowledge market, and an evolving biological system. The four perspectives offer different ways of framing the question of progress. I describe these perspectives and make the case based on the conclusions formed from logic and data that the field has indeed progressed splendidly. I argue that the field is maturing and making a contribution, and we should be proud of what we have accomplished. However, through each perspective, I also identify some vicious circles to avoid if we are to continue to progress. The portrait is one of optimism and hope, along with the need for sound stewardship going forward

BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS

This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a data warehouse structure and knowledge management tools within the decision making framework. 1600 students' records have been analyzed to explore the KDD process, and another 2000 records have been used to build and test the data mining techniques within the KDD process. Moreover, the research has analyzed the key characteristics of data warehouses and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a data warehouse for the Egyptian Universities that handle their admission and registration related archival data. The decision makers' potential benefits of the data warehouse within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will also be used as a clustering package. Finally, the contribution of this research is found in the following areas: A new DSS development methodology; The development and validation of a new research questionnaire (i.e. ARDSSQ); The development of the admission and registration data warehouse; The evaluation and use of cluster analysis proximities and techniques in the KDD process to find knowledge in the students' records; And the development of the ARDSS software that encompasses the advantages of the KDD and DW and submitting these advantages to the senior admission and registration managers in the Egyptian Universities. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems