We summarize the results of the IEEE BigData 2023 Cup: Object Recognition with Muon Tomography using Cosmic Rays - a data mining competition organized at the KnowledgePit.ai platform in association with the IEEE BigData 2023 conference. We describe the challenge at the heart of the competition task, as well as the data acquisition and preparation steps. We present the entire process of preparing experiments and subsequent data analysis for the purpose of recognizing X-rayed objects using muon tomography techniques. We conclude this analysis by presenting the baseline as well as the winning solution of the object segmentation algorithms for the research space reconstruction and object classification.

We present an approach to tensor compression and decomposition, as well as to a design of data processing algorithms on the top of them. Our implementation uses the popular scalable data processing framework Apache Parquet to effectively store the data. This library does not directly store tensors as native data types, but we slightly changed its implementation for our purpose using its specific data storage format and extending it with additional compression. We summarize the performance of tensor storage, as well as the effectiveness of multiple machine learning methods and their hyperparameter tuning.

We present an approach to data and information granulation known from the Infobright Community Edition (ICE) analytical database engine, now reimplemented within the two popular scalable data processing frameworks: Apache Parquet and ROOT. Both of these libraries, do not directly realize the idea of resolving queries based on rough-set-driven calculations on granulated data statistics, what was one of the biggest accelerators in ICE. We summarize the implementation of such level of operations and compare the performance of analytical SQL queries in ROOT, Parquet, and ICE.