Nonpoint source (NPS) pollution is one of the leading causes of water quality problems in the United States. Bioretention has become one of the more frequently used stormwater management practices for addressing NPS pollution in urbanized watersheds in New England. Yet despite increased acceptance, bioretention is not widely practiced. This study explores and evaluates the efficacy of bioretention for protecting urban water quality. This research found that numerous monitoring methods are used by researchers and industry experts to assess the effectiveness of stormwater best management practices (BMPs) and low impact development (LID) practices that include bioretention. The two most common methods for analyzing and evaluating water quality data are pollutant removal efficiency and effluent quality. While effluent quality data is useful for characterizing classes of BMP treatment performance on a statistical basis, pollutant removal efficiency is more representative of the actual pollutant load being reduced by the stormwater treatment practice over time, and is used in Total Maximum Daily Load (TMDL) assessments. However, despite this difference, monitoring is still arguably the best method for determining the effectiveness of stormwater treatment practices. Monitoring of bioretention performance results is needed to inform improvements to design standards and guidance to aid state and local municipalities in the proper selection of bioretention/stormwater controls. This study advocates for instituting fine-scale, “safe-to-fail” design experiments as part of an adaptive management process that is used to advance bioretention design guidance and future applications of monitoring practice(s) that target reduction of pollutants in downstream receiving waterbodies. This innovative approach could result in increased use of bioretention in New England urban environments.

As modern systems are becoming increasingly complex, they are also becoming more and more cumbersome to diagnose and fix when things go wrong. One domain where it is very important for machinery and equipment to stay functional is in the world of medical IT, where technology is used to improve healthcare for people all over the world. This thesis aims to help with reducing downtime on critical life-saving equipment by implementing automatic analysis of system logs that without any domain experts involved can give an indication of the state that the system is in. First, a literature study was performed where three potential candidates of suitable neural network architectures was found. Next, the networks were implemented and a data pipeline for collecting and labeling training data was set up. After training the networks and testing them on a separate data set, the best performing model out of the three was based on GRU (Gated Recurrent Unit). Lastly, this model was tested on some real world system logs from two different sites, one without known issues and one with slow image import due to network issues. The results showed that it was feasible to build such a system that can give indications on external parameters such as network speed, latency and packet loss percentage using only raw system logs as input data. GRU, 1D-CNN (1-Dimensional Convolutional Neural Network) and Transformer's Encoder are the three models that were tested, and the best performing model was shown to produce correct patterns even on the real world system logs. / I takt med att moderna system ökar i komplexitet så blir de även svårare att felsöka och reparera när det uppstår problem. Ett område där det är mycket viktigt att maskiner och utrustning fungerar korrekt är inom medicinsk IT, där teknik används för att förbättra hälso- och sjukvården för människor över hela världen. Syftet med denna avhandling är att bidra till att minska tiden som kritisk livräddande utrustning inte fungerar genom att implementera automatisk analys av systemloggarna som utan hjälp av experter inom området kan ge en indikation på vilket tillstånd som systemet befinner sig i. Först genomfördes en litteraturstudie där tre lovande typer av neurala nätverk valdes ut. Sedan implementerades dessa nätverk och det sattes upp en datapipeline för insamling och märkning av träningsdata. Efter att ha tränat nätverken och testat dem på en separat datamängd så visade det sig att den bäst presterande modellen av de tre var baserad på GRU (Gated Recurrent Unit). Slutligen testades denna modell på riktiga systemloggar från två olika sjukhus, ett utan kända problem och ett där bilder importerades långsamt på grund av nätverksproblem. Resultaten visade på att det är möjligt att konstruera ett system som kan ge indikationer på externa parametrar såsom nätverkshastighet, latens och paketförlust i procent genom att enbart använda systemloggar som indata.  De tre modeller som testades var GRU, 1D-CNN (1-Dimensional Convolutional Neural Network) och Transformer's Encoder. Den bäst presterande modellen visade sig kunna producera korrekta mönster även för loggdata från verkliga system.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

In den vorliegenden Untersuchungen wurde das Ziel verfolgt die Nutzbarkeit des Milchinhaltsstoffes Laktose als praxistaugliche Managementhilfe zu prüfen. Die Primärdaten stammen aus drei israelischen Hochleistungsherden, über mehrere Laktationen erhoben. Der Parameter Laktosegehalt wurde in der Datenaufbereitung dahingehend geprüft, ob dieser zur Gesundheits- und Leistungsvorhersage ausreicht oder welche zusätzlichen Merkmale für die Verwendung in einem Prognose-Modell von Bedeutung sein könnten. Als leistungs- bzw. gesundheitsrelevante Ereignisse (Events) wurden Brunst, Diarrhoe, Endometritis, Fieber, Infektionen, Klauenerkrankungen, Mastitis, Stress, Stoffwechselstörungen sowie Verletzungen zugeordnet. Die Bewertung der Nützlichkeit einzelner Merkmale für die Prädiktion erfolgte anhand der Erkennungsraten. Zwei- und dreistufige Entscheidungsbäume wurden entwickelt, um diese Events zu identifizieren. Ein einzelnes Merkmal ist oft nicht ausreichend, weshalb verschiedene Kombinationen von Variablen analysiert wurden. Die wichtigste Erkenntnis der vorliegenden Arbeit besteht darin, dass der Abfall der Laktosekonzentration und Laktosemenge immer ein kritisches Ereignis darstellt. Das Hauptziel eines Gesundheitsmonitorings im Milchkuhbestand sollte deshalb darin bestehen, frühzeitig eine Stoffwechselüberlastung "sichtbar" oder "erkennbar" zu machen. Unabhängig davon, welche Erkrankung sich anbahnt, muss das Herdenmanagement darauf hinwirken, die Glukoseversorgungssituation des Einzeltieres zu verbessern. Aus der Analyse für die einzelnen Herden und Laktationen kann grundlegend abgeleitet werden, dass die Ergebnisse der Milchkontrolldaten, die im Zuge der datengestützten Herdenüberwachung erhoben wurden, sich verwenden lassen, um den Leistungs- und Gesundheitsstatus der Kühe im Laktationsverlauf einzuschätzen und zu prognostizieren. Die Verwendung von Informationen zum Laktosegehalt des Gemelks verbesserten in jedem Fall die Erkennungsraten. / The aim of the current studies was to investigate whether the milk ingredient lactose can be used as a practical support management. The primary data comes from three Israeli high-performance herds, collected over several lactations. In the data preparation, the parameter "lactose content" was examined to see whether it is sufficient for a health and performance prediction or whether additional features may be of importance for usage in a forecasting model. Oestrus, diarrhea, endometritis, fever, infections, hoof diseases, mastitis, stress, metabolic disorders, and injuries have been assigned to the performance- and/or health-affecting events. The usefulness of individual features for the prediction was evaluated on the basis of the recognition rates. Thus two- and three-level decision trees have been developed to identify these events. As one single feature is often insufficient, different combinations of variables were analyzed. The most important finding of this study is that the drop in the lactose concentration and lactose quantity always represents a critical event. The main objective of a health monitoring in the dairy herd should therefore be to make a metabolic overload "visible" or "recognisable" at an early stage. Whichever disease begins to take shape, the herd management must work on improving the glucose supply situation of the individual animal. In conclusion from the analysis of the individual herds and lactations it can be inferred that the results of the milk control data collected in the course of the data-based herd monitoring can be used in order to assess and to predict the performance and health status of the cows in the course of lactation. The use of information on the lactose content of the milk improved in any case the recognition rates.