Data Detection Framework for Spatio-Temporal Data Mining
P. Madhuri, K. Srinivasa Rao, S. K. Yadav · Zenodo (CERN European Organization for Nuclear Research) · 2020
With the unpredictable increase in the generation and use of spatiotemporal data sets, the efficient handling of large volumes of spatiotemporal sets has been the subject of many research efforts. With the phenomenal growth of computer technology everywhere, mining from the enormous amount of spatiotemporal data sets is seen as a central technology that can provide information for real-world applications. Big spatial data apps cover a wide variety of interests, including infectious disease monitoring, simulation of climate change, opioid addiction, and others. As a result, significant research efforts are carried out within these applications to facilitate efficient analysis and intelligence by either offering spatial extensions to existing machine learning solutions or designing new solutions from scratch. Based on the concepts of states and events, the conceptual model was developed and the use of time as a basis for organising spatial data allowed the time and place of any modifications to be recorded. This paper proposes a conceptual-level spatio-temporal modelling approach, called MADS. The idea results from the description of the conditions for a conceptual model to be fulfilled. We can easily formalize spatiotemporal data mining challenges using the proposed knowledge discovery system.