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Preparing for your Method Engineering exam and tired of dense textbooks? This engaging and concise summary is your ultimate shortcut to success! It covers all the essentials—from conceptual and domain modeling to model-driven development, Mendix, data persistence, security, XPath, Process-Deliverable Diagrams, and situational method engineering—explained in a clear, structured way that’s perfect for quick revision. Designed to save you hours of study time while boosting your confidence, th...

This two-page Process Mining (PROM) Cheat Sheet is designed to make studying process mining a whole lot easier. It pulls together the most important techniques—like Alpha Miner, Heuristic Miner, and Inductive Miner for process discovery, as well as conformance checking (fitness, precision, generalization) and performance analysis—all in one clear and compact overview. Instead of flipping through slides or notes, you can quickly look up the essentials and focus on what really matters. Whether...

Struggling to wrap your head around Software Architecture before your exam? This sharp, exam-focused summary is your shortcut to understanding the essentials—fast and efficiently. What’s Inside? • Architecture vs. Design – Know the difference and why it matters • Availability Tactics – Fault detection, recovery, and prevention explained clearly • Deployability – Understand dev, staging, and production environments • Key Patterns – MVC, SOA, Broker, Pub/Sub, Layered Arc...

Struggling to make sense of Business Process Management (BPM) before your exam? This concise, exam-focused summary is your shortcut to mastering the key concepts—fast. What’s Inside? • BPM Lifecycle – From identification to monitoring • Process Modeling – BPMN basics, gateways, loops, and model quality • Redesign Techniques – Devil’s Quadrangle, heuristics & redesign orbit • Implementation – BPMS architecture, automation, and execution engines • Process Mini...

Tackling your Requirements Engineering midterm? Don’t waste time digging through endless materials—this expertly crafted summary gives you a clear, structured, and efficient way to study the key concepts you need! What’s Inside? Pohl’s Three Dimensions – Understanding Specification, Agreement & Systems Elicitation Techniques – Functional vs. Non-Functional Requirements The RE Process – How requirements evolve through spiral development Information Exchange – Known K...

Are you preparing for your Requirements Engineering (RE) exam and feeling overwhelmed by dense textbooks and complex concepts? This expertly crafted exam summary will help you study efficiently and focus on what really matters! What’s Inside? Pohl’s Three Dimensions – Specification, Agreement & Systems Elicitation Techniques – Functional & Non-Functional Requirements Quality Frameworks – Glinz Model, UGA Operators, and Requirement Satisfaction User Stories & Agile Methodo...

This extensive exam summary is your go-to guide for understanding research methodologies in Business Intelligence. It introduces essential concepts such as empirical research approaches (qualitative and quantitative), philosophical paradigms (positivism, interpretivism), and systematic literature review techniques like meta-analysis and narrative synthesis. The guide also explores research strategies, including surveys, case studies, and experimental methods, alongside key topics like sampling t...

This document is a concise yet comprehensive overview of essential research methods, ideal for students and professionals alike. It covers the foundational 6 P’s framework (Purpose, Products, Process, Participants, Paradigm, Presentation) and provides guidance on selecting appropriate research approaches, including inductive and deductive reasoning. Key topics include sampling techniques, ethical considerations, and detailed explanations of research strategies such as surveys, experiments, ...

This two-page Data Science and Statistics (DSS) Cheat Sheet is designed to support students preparing for the INFOMDSS open book exam, providing a focused yet comprehensive overview of essential clustering, classification, and evaluation metrics. With quick-reference explanations of key concepts—such as clustering methods (K-means, DBSCAN), performance metrics (Silhouette Index, ROC curves), and data quality measures—this cheat sheet is also a valuable resource for other data science courses...

This 2 page (one piece of paper) cheatsheet gives a full overview of all of the theoretical datascience topics including classification approaches like Bayes Theorem. Please note that the preview page is blurred as this page makes half of the complete document, therefore including a bunch of valuable info.