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Approximate Query Answering Techniques for Array DBMS

Student: Anna Denisenko

Supervisor: Ramon Antonio Rodriges Zalipynis

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Year of Graduation: 2021

Raster DBMS (Database Management Systems) work with data for which a multidimensional array is a natural representation. For example, this data includes weather and climate forecasts, UAV (Unmanned Aerial Vehicle) data, Earth remote sensing (ERS) data. Raster algebra is a source of typical queries for raster DBMS: the calculation of an algebraic expression, the input of which several multidimensional arrays (rasters) are supplied. Modern raster DBMSs perform accurate calculations when answering inquiries, but this is not always necessary. For example when the analyst applies the same formula several times in a row, but with slightly different parameters and renders the result. On the screen small inaccuracies can be almost invisible, but approximate calculations can significantly speed up responses to queries. This paper explores the possibility of speeding up responses to queries in raster DBMS due to preprocessing of stored arrays using JPEG image algorithms. This reduces the amount of input data and reduces the amount of disk I/O, thereby reducing response time to the request. Also, some operations can be performed directly over the compressed arrays, without decoding them. In the work, experiments were carried out on the remote sensing data, since they find wide application in urban infrastructure planning, agriculture and forestry, emergency response. Comparisons were made between the volumes of the original and compressed data, and time to perform operations on them. It was found that the considered method speeds up computations and reduces the amount of data by a factor of 10. When the considered approaches are introduced into raster DBMS, the efficiency of solving practically important problems can be increased in the areas mentioned above.

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