A comparative analysis and implementation of indicators for sustainable water management - An application in Cascais

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis research aims to identify and assess relevant indicators that follow a municipality's water management system's current state. Moreover, this paper will use the design research methodology to create a monitoring system's conceptual model that displays these identified indicators. Contemporary problems in water management are characterized by increasing complexity. Uncertainties due to climate change provide new challenges that humanity needs to tackle. Therefore, cities need to implement monitoring systems with clear indicators, clean data, set targets, and goals to successfully achieve their long-term sustainable development plans. The creation of a conceptual model is eventually to make this implementation more accessible to different municipalities. The defined indicators should meet the criteria to increase the usefulness and understanding for city managers and decision-makers from diverse backgrounds. Eventually, the goal is to apply this conceptual model to the Cascais water management case, where the local water indicators will be displayed in a Power BI report to see if Cascais' set climate action goals are on their way to being met

Models for Improvement Management and Operational Performance

Achieving high levels of performance in the manufacturing environment requires an increase in speed, quality and reliability of existing technologies. This is inherently related with the need to develop adequate process monitoring and modelling/simulation approaches, along with innovative optimization and maintenance strategies. The purpose of this dissertation is to provide a framework that can be used as a tool by decision makers when evaluating and controlling the performance of a system. To achieve this, a structured conceptual model should be developed, which should not be tied to any particular model. The idea with this framework is to assess information obtained during production and use it to generate relevant KPIs for monitoring and evaluating the system?s performance. This information should then be fed into a DSS that can provide suggestions or even actively influence simulation parameters to apply the identified improvement measures. Finally, the proposed framework is to be applied in a case-study to evaluate its relevance in the improvement of manufacturing operations in a real-world context

A dynamic dashboarding application for fleet monitoring using semantic web of things technologies

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored—these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early

CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.Comment: 8 pages, 2 figures, 1 tabl

Analysis of Lisbon visitors’ internet access behavior: behavior analysis through the identification of clusters

Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing IntelligenceThis master's thesis focuses on clustering the internet access behavior of urban visitors in the Lisbon urban area. To promote smart city development, the study aims to provide insights into visitors' behaviors while accessing the internet in Lisbon, enabling improved decision-making processes for city management, and enhancing the overall online and offline experience for visitors. The over-tourism phenomenon has put a strain on infrastructure, public transportation, and cultural heritage sites. Therefore, innovative methods are needed for effective smart city management, particularly in urban mobility. The increasing availability of Wi-Fi networks during travel has generated valuable data that can be used to develop groundbreaking approaches to understanding visitors’ behaviors and mobility patterns in urban areas. This knowledge enables the analysis and clustering of urban visitors' behavior, contributing to improved decision-making processes in smart city management