Demestichas, KonstantinosKonstantinosDemestichasRemoundou, KonstantinaKonstantinaRemoundouLoumiotis, IoannisIoannisLoumiotisAdamopoulou, EvgeniaEvgeniaAdamopoulouMüller, WilmuthWilmuthMüllerPallmer, DirkDirkPallmerMühlenberg, DirkDirkMühlenbergKozik, RafalRafalKozikChoras, MichaelMichaelChorasFaure, DavidDavidFaureHorincar, RoxanaRoxanaHorincarBrodie of Brodie, Edward BenedictEdward BenedictBrodie of BrodieJacobe de Naurois, CharlotteCharlotteJacobe de NauroisChandramouli, KrishnaKrishnaChandramouliRosca, AlexandraAlexandraRosca2022-03-062022-03-062021https://publica.fraunhofer.de/handle/publica/26839010.1007/978-3-030-69460-9_4Nowadays, law enforcement agencies - LEAs - are forced to deal with extreme volumes of data, being in need to analyse from heterogeneous data sources, uncover hidden relationships, trends and patterns of incidents and ultimately reach solid evidence to be used in court. In this chapter, a system is presented that can assist LEA officers in fighting crime, that, following the collection of the primary data, it applies semantic reasoning tools that allow the system to relate pieces of data, based on their inner relationships, and extract new information based on the asserted facts and rules defined by the LEAs. Then, the results derived by the reasoners and the initial data are fused using appropriate tools including a trajectory, a person and an event fusion tool, to be finally visualized by the proposed bipartite graphs. The proposed system is expected to decrease the time required to solve a crime by LEA's officers.ensemanticsreasoningfusionmachine learningtrend predictionlaw enforcement agencies004670book article