Advancements of Randomized Data Structures for Geospatial Data

Increasing amount of geospatial data acquired by various sensory improvements and increased governmental and societal interest in creating this data, results in rising issues in data storage and processing for this data type. Therefore this work describes the setting for a probabilistic data structure based on Random Projections and Bloom Filters to enable a more efficient processing of geospatial data. The paper states four research questions, which, apart from general considerations on probabilistic data structures, elaborate on the advantages of such data structures in their use together with new hardware like field programmable gate arrays (FPGA) or quantum computers. The main contribution is to structure the problem space and define solution approaches for future research in this field.     «

Increasing amount of geospatial data acquired by various sensory improvements and increased governmental and societal interest in creating this data, results in rising issues in data storage and processing for this data type. Therefore this work describes the setting for a probabilistic data structure based on Random Projections and Bloom Filters to enable a more efficient processing of geospatial data. The paper states four research questions, which, apart from general considerations on probab...     »