ORM-Frameworks sind eine häufig genutzte Möglichkeit, die Unterschiede zwischen der objektorientierten Programmierung und der relationalen Datenspeicherung zu überbrücken. Gleichzeitig sind rekursive Beziehungen in vielen Datenmodellen vorhanden, um Hierarchien und andere baum- oder netzartigen Strukturen abzubilden. Diese Arbeit beschäftigt sich mit der Problemstellung, wie man Daten in rekursiven Beziehungen in einem ORM-Framework möglichst effizient aus der Datenbank laden kann. Zur Lösung des Problems werden zuerst fünf verschiedene Lösungsansätze theoretisch entwickelt, und anschließend in Entity Framework Core (EF Core) umgesetzt. Dies geschieht so, dass sich die entstandenen Implementierungen wie normale LINQ-Abfragen in EF Core nutzen lassen. Anschließend wurden die Lösungsansätze in verschiedenen Szenarien mit verschiedenen SQL-Dialekten getestet, gefolgt von einer Betrachtung der Testergebnisse nach den verschiedenen Test-Parametern. Diese Auswertung zeigt, dass Abfragen mit rekursiven allgemeinen Tabellenausdrücken und Abfragen mit KeyLoading in den meisten Fällen am effizientesten sind. Die anderen drei Ansätze sind entweder generell zu langsam oder nur in speziellen Situationen nutzbar. Die Auswertung zeigt auch, dass die Performance der verschiedenen Lösungsansätze immer von der Situation abhängig ist, besonders vom ge- nutzten SQL-Dialekt.:1 Einleitung 2 Rekursive Beziehungen in relationalen Datenbanken 2.1 Definition 2.2 Rekursive Abfragen durch allgemeine Tabellenausdrücke 3 Object-Relational Mapping und Entity Framework Core 3.1 Object Relational Impedance Mismatch 3.1.1 Konzepte der objektorientierten Programmierung 3.1.2 Konzepte von relationalen Datenbanken 3.1.3 Probleme durch Unterschiede zwischen den Konzepten 3.2 Überbrückung des Impedance Mismatch durch Object-Relational Mapping 3.2.1 ORM-Frameworks 3.2.2 Allgemeine Lösungsansätze 3.2.3 Zugehörige Daten laden 3.3 Entity Framework Core 3.3.1 Generierung des Datenbankschemas 3.3.2 Abfragen mit LINQ 3.3.3 C# Expressions 3.3.4 Ablauf einer Abfrage in Entity Framework Core 3.3.5 Der ChangeTracker 3.3.6 Abfragen mit RawSql 4 Rekursive Abfragen in ORM Frameworks 4.1 Allgemeine Lösungsansätze 4.1.1 Anforderungen an die Lösungsansätze 4.1.2 Grundlegende Überlegungen 4.1.3 Rekursive allgemeine Tabellenausdrücke 4.1.4 Benutzerdefinierte Funktionen 4.1.5 Laden mit Hilfseigenschaft 4.1.6 Vertikales und horizontales Aufrollen 4.1.7 KeyLoading 4.2 Umsetzung in Entity Framework Core 4.2.1 Anforderungen im Kontext von Entity Framework Core 4.2.2 Rekursive allgemeine Tabellenausdrücke 4.2.3 Laden mit Hilfseigenschaft 4.2.4 Vertikales Aufrollen 4.2.5 Horizontales Aufrollen 4.2.6 KeyLoading 5 Test der Lösungsansätze 5.1 Testverfahren 5.1.1 Performancemessung in C# 5.1.2 Messung der SQL-Abfragedauer 5.2 Test-Szenarien 5.3 Auswertung der Testergebnisse 5.3.1 Anmerkungen zu Messungen mit TotalProcessTime 5.3.2 Auswertung nach verschiedenen Kriterien 5.3.3 Allgemeine Auswertung 5.3.4 Auswertung nach Verzweigungen 5.3.5 Auswertung nach Rekursionstiefe 5.3.6 Auswertung nach Anzahl der Strukturen 5.3.7 Auswertung nach dem Laden zugehöriger Daten 5.3.8 Auswertung nach SQL-Dialekt 5.3.9 Auswertung nach Beziehungstyp 5.3.10 Auswertung nach Schlüsseltyp 6 Fazit A Messwerte

<p>In modern industrial robots the components in the transmission contain nonlinearities. These nonlinearities need to be to estimated either for better control or to use the parameters for diagnosis of the system. There is a lot of work done within system identification and mainly within the field of iterative parameter estimation.</p><p>This thesis considers recursive grey-box identification for a nonlinear model of the transmission in an industrial robot. The nonlinearities that are identified are friction, spring stiffnes, hysteresis and backlash. These nonlinearities are a part of the models that are presented in this thesis. Apart from models there is a need for some sort of algorithm for the identification and some different recursive algorithms are presented. The main subject of this thesis is the identification of parameters and the excitation signals needed for the identification of each parameter.</p><p>The models and algorithms presented in this thesis work in a principle point of view. Despite this they work in varying extent for the different types of parameters. Estimation of linear and nonlinear friction and linear spring stiffnes works relatively well. Nonlinear spring stiffnes and hysteresis have not been possible to estimate. Backlash which is estimated with a hybrid variant of a RPEM which is not fully recursive works best. When it is not possible to identify the parameters suggestions on other solutions are given, such as for example extension of the model, use of other algorithms or optimization of the excitation signal.</p>

In modern industrial robots the components in the transmission contain nonlinearities. These nonlinearities need to be to estimated either for better control or to use the parameters for diagnosis of the system. There is a lot of work done within system identification and mainly within the field of iterative parameter estimation. This thesis considers recursive grey-box identification for a nonlinear model of the transmission in an industrial robot. The nonlinearities that are identified are friction, spring stiffnes, hysteresis and backlash. These nonlinearities are a part of the models that are presented in this thesis. Apart from models there is a need for some sort of algorithm for the identification and some different recursive algorithms are presented. The main subject of this thesis is the identification of parameters and the excitation signals needed for the identification of each parameter. The models and algorithms presented in this thesis work in a principle point of view. Despite this they work in varying extent for the different types of parameters. Estimation of linear and nonlinear friction and linear spring stiffnes works relatively well. Nonlinear spring stiffnes and hysteresis have not been possible to estimate. Backlash which is estimated with a hybrid variant of a RPEM which is not fully recursive works best. When it is not possible to identify the parameters suggestions on other solutions are given, such as for example extension of the model, use of other algorithms or optimization of the excitation signal.

In the last decade, some researches on the star problem in trace monoids (is the iteration of a recognizable language also recognizable?) has pointed out the interest of the finite power property to achieve partial solutions of this problem. We prove that the star problem is decidable in some trace monoid if and only if in the same monoid, it is decidable whether a recognizable language has the finite power property. Intermediary results allow us to give a shorter proof for the decidability of the two previous problems in every trace monoid without C4-submonoid. We also deal with some earlier ideas, conjectures, and questions which have been raised in the research on the star problem and the finite power property, e.g. we show the decidability of these problems for recognizable languages which contain at most one non-connected trace.

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In the last decade, some researches on the star problem in trace monoids (is the iteration of a recognizable language also recognizable?) has pointed out the interest of the finite power property to achieve partial solutions of this problem. We prove that the star problem is decidable in some trace monoid if and only if in the same monoid, it is decidable whether a recognizable language has the finite power property. Intermediary results allow us to give a shorter proof for the decidability of the two previous problems in every trace monoid without C4-submonoid. We also deal with some earlier ideas, conjectures, and questions which have been raised in the research on the star problem and the finite power property, e.g. we show the decidability of these problems for recognizable languages which contain at most one non-connected trace.

This Master's thesis utilizes random forest and leave-one-out cross-validation to predict the success of megaprojects involving infrastructure. The goal was to enhance the efficiency of the design and engineering phase of the infrastructure and construction industries. Due to the small sample size of megaprojects and limitated data sharing, the lack of data poses significant challenges for implementing artificial intelligence for the evaluation and prediction of megaprojects. This thesis explore how megaprojects can benefit from data collection and machine learning despite small sample sizes. The focus of the research was on analyzing data from thirteen megaprojects and identifying the most influential data for machine learning analysis. The results prove that the incorporation of expert data, representing critical success factors for megaprojects, significantly enhanced the accuracy of the predictive model. The superior performance of expert data over economic data, experience data, and documentation data demonstrates the significance of domain expertise. In addition, the results demonstrate the significance of the planning phase by implementing feature selection techniques and feature importance scores. In the planning phase, a small, devoted, and highly experienced team of project planners has proven to be a crucial factor for project success. The thesis concludes that in order for companies to maximize the utility of machine learning, they must identify their critical success factors and collect the corresponding data. / Denna magisteruppsats undersöker följande forskningsfråga: Hur kan maskininlärning och insiktsfull dataanalys användas för att öka effektiviteten i infrastruktursektorns plannerings- och designfas? Denna utmaning löses genom att analysera data från verkliga megaprojekt och tillämpa avancerade maskininlärningsalgoritmer för att förutspå projektframgång och ta reda på framgångsfaktorerna. Vår forskning är särskilt intresserad av megaprojekt på grund av deras komplicerade natur, unika egenskaper och enorma inverkan på samhället. Dessa projekt slutförs sällan, vilket gör att det är svårt att få tillgång till stora mängder verklig data. Det är uppenbart att AI har potential att vara ett ovärderligt verktyg för att förstå och hantera megaprojekts komplexitet, trots de problem vi står inför. Artificiell intelligens gör det möjligt att fatta beslut som är datadrivna och mer informerade. Uppsatsen lyckas med att hanterard det stora problemet som är bristen på data från megaprojekt. Uppsatsen motiveras även av denna brist på data, vilket gör forskningen relevant för andra områden som präglas av litet dataurval. Resultaten från uppsatsen visar att evalueringen av megaprojekt går att förbättra genom smart användning av specifika dataattribut. Uppsatsen inspirerar även företag att börja samla in viktig data för att möjliggöra användningen av artificiell intelligens och maskinginlärning till sin fördel.