Methods and frameworks for GeoSpatioTemporal data analytics In the era of social networks internet of things and location based services many online services produce a huge amount of data that have valuable objective information such as geographic coordinates and date time. These characteristics (parameters) in the combination with a textual parameter bring the challenge for the discovery of geospatiotemporal knowledge. This challenge requires efficient methods for clustering and pattern mining in spatial temporal and textual spaces. In this thesis we address the challenge of providing methods and frameworks for geospatiotemporal data analytics. As an initial step we address the challenges of geospatial data processing: data gathering normalization geolocation and storage. That initial step is the basement to tackle the next challenge geospatial clustering challenge. The first step of this challenge is to design the method for online clustering of georeferenced data. This algorithm can be used as a server side clustering algorithm for online maps that visualize massive ...