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The term "big data" refers to the vast amount of structured and unstructured data generated by businesses, organizations, and individuals on a daily basis. The rapid growth of big data has led to the development of new technologies and techniques for storing, processing, and analyzing these data in order to extract valuable information. This study examines some of these technologies, compares their pros and cons, and provides solutions for handling specific types of reporting using big data tools. In addition, this paper discusses some of the challenges associated with big data and suggests approaches that could be used to manage and analyze these data. The findings demonstrate the benefits of efficiently managing the datasets and choosing the appropriate tools, as well as the efficiency of the proposed solution with hands-on examples. [ABSTRACT FROM AUTHOR]

For decades SI prefixes have been used incorrectly in the IT world to define volumes of binary information. Kilo, mega, giga, etc. were widely accepted all over the world for powers of 2 instead of powers of 10. The introduction of new binary information prefixes kibi, mebi, gibi, etc. started in 1999. This change was proved difficult, because the old prefixes already gained very wide adoption in the society. Most hardware and software manufacturers refused to use the new units of measurement, probably because it was not profitable for them from marketing point of view. Some software companies even refused to start measuring correctly with the new values for the old prefixes. This problem was transferred into the Bulgarian education system, where the innovation was met with strong conservatism and even an outright reluctance of teachers to teach the new units. The article examines the problem from a pedagogical point of view and suggests some guidelines for effective teaching of the new units in the Information Technology curriculum for the Bulgarian schools. [ABSTRACT FROM AUTHOR]

Three-dimensional imaging of whole mammalian brains at single-neuron resolution has generated terabyte (TB)- and even petabyte (PB)-sized datasets. Due to their size, processing these massive image datasets can be hindered by the computer hardware and software typically found in biological laboratories. To fill this gap, we have developed an efficient platform named TDat, which adopts a novel data reformatting strategy by reading cuboid data and employing parallel computing. In data reformatting, TDat is more efficient than any other software. In data accessing, we adopted parallelization to fully explore the capability for data transmission in computers. We applied TDat in large-volume data rigid registration and neuron tracing in whole-brain data with single-neuron resolution, which has never been demonstrated in other studies. We also showed its compatibility with various computing platforms, image processing software and imaging systems. [ABSTRACT FROM AUTHOR]