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Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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In is paper, a new technique in complex adaptation is investigated. By presenting the closest retrieved cases to a neural network, it learns about the domain of the problem being solved. The new problem is then fed to the trained neural network and the output becomes the solution to that problem. The methodology is applied to a problem in the steel construction and the sought output is the cost estimation of pre-engineered steel buildings.Several experiments are conducted to prove that these steps are successful. System verification is done and shows that both the system and the methodology are successful to develop a complete adaptation mechanism in CBR.