Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This domain has been extensively studied by researchers in sociology, psychology, and statistics. More recently, it has drawn interest from computer scientists, especially with advances in artificial intelligence. Despite this, the literature indicates that work in this latter discipline is still in its early stages. This study aims to understand the challenges faced by machine learning approaches in crime linkage and to support foundational knowledge for future data-driven methods. To achieve this goal, we conducted a comprehensive survey of the main literature on the topic and developed a general framework for crime linkage processes, thoroughly describing each step. Our goal was to unify insights from diverse fields into a shared terminology to enhance the research landscape for those intrigued by this subject.

Leesman hopes that in coaching Team Liquid, he can translate formative life experiences across esports and traditional sports into competitive success. Growing up in Cranbrook, British Columbia, Leesman was (and still is) an avid NBA and NFL fan, spending four years in high school quarterbacking the local under-18 Rocky Mountain Rams football team and then staying on for a year as their offensive coordinator. He idolizes Phil Jackson, especially the way Jackson would often refuse to call timeouts when his teams were struggling, preferring to let them learn how to execute and escape adversity in the moment. With no timeouts during a League of Legends game, Leesman believes proper preparation is essential to impactful coaching.