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1

Implementation of the Concept of a Repository for Automated Processing of Semi-Structural Data

Piech Mateusz, Rakoczy Bartosz, Dajda Jacek, Kisiel-Dorohinicki Marek

Journal of Telecommunications and Information Technology

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2020

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nr 1

76--86

EN

Semi-structural data tend to be problematic due to the sparsity of their attributes and due to the fact that, regardless of their type, they are immensely diverse. This means that data storage is a challenge, especially when the data contained within a relational database – often a strict requirement deﬁned in advance. In this paper, we present a thoroughly described concept of a repository that is capable of storing and processing semi-structural data. Based on this concept, we establish a database model comprising the architecture and the tools needed to search the data and build relevant processors. The processor described may assign roles and dispatch tasks between the users. We demonstrate how the capacities of this repository are capable of overcoming current limitations by creating a system for facilitated digitization of scientiﬁc resources. In addition, we show that the repository in question is suitable for general use, and, as such, may be adapted to any domains in which semi-structural data are processed, without any additional work required.

2

Porównanie wydajności wybranych parserów JSON z parserem używającym innej metody odczytu

Grzegorz Koter Przemysław

Journal of Computer Sciences Institute

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2020

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Vol. 16

300--304

EN

The analysis of parsing method of currently used JSON parsers, that are comprised of tokenizer and parser module, led to conclusion, that their performance could be improved. As a means to prove it, new JSON parser has been developed, whose modules are dedicated to process structure of either an object or an array. In both modules, tokenization step is combined with building step of document model, representing its structure. Performance test involved processing of sample document 1000 times, per 20 repeats, and was carried out by three compared parsers. Proposed parser was nearly 89% and 42% faster than other two, and its memory usage was mediocre. Results are promising enough to consider real-world usage, thus improving the efficiency of JSON processing.

PL

Analiza metody przetwarzania obecnie używanych parserów JSON, które są złożone z modułu tokenizera i parsera, doprowadziła do wniosku, że ich wydajność może być poprawiona. W celu dowiedzenia tego, opracowano parser, którego moduły są podzielonego względem przetwarzanej struktury, czyli obiektu i tablicy. W obu modułach krok wyodrębniania tokenów dokumentu jest połączony z krokiem budowy reprezentacji struktury dokumentu. Test wydajności obejmował przetworzenie przykładowego dokument 1000 razy w każdym z 20 powtórzeń, przez trzy porównywane parsery. Proponowany parser był szybszy o około 89% i 42% od pozostałych dwóch, jednocześnie zużycie pamięci było przeciętne. Wyniki są na tyle obiecujące, by rozważyć faktyczne użycie, poprawiając efektywność przetwarzania dokumentów JSON.

3

Model for dynamic and hierarchical data repository in relational database

Piech M., Fracz W., Turek W., Kisiel-Dorohinicki M., Dajda J., Byrski A.

Computer Science

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2018

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Vol. 19 (4)

479--500

EN

The aim of this research is to build an open schema model for a digital sources repository in a relational database. This required us to develop a few advanced techniques. One of them was to keep and maintain a hierarchical data structure pushed into the repository. A second was to create constraints on any hierarchical level that allows for the enforcement of data integrity and consistency. The created solution is mainly based on a JSON file as a native column type, which was designed for holding open schema documents. In this paper, we present a model for any repository that uses hierarchical dynamic data. Additionally, we include a structure for normalizing the input and description for the data in order to keep all of the model assumptions. We compared our solution with a well-known open schema model – Entity-Attribute-Value – in the scope of saving data and querying about relationships and contents from the structure. The results show that we achieved improvements in both the performance and disk space usage, as we extended our model with a few new features that the previous model does not include. The techniques developed in this research can be applied in every domain where hierarchical dynamic data is required, as demonstrated by the digital book repository that we have presented.

4

A new approach to storing dynamic data in relational databases using JSON

Piech M., Marcjan R.

Computer Science

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2018

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Vol. 19 (1)

5--22

EN

JavaScript Object Notation was originally designed to transfer data; however, it soon found another use as a way to persist data in NoSQL databases. Recently, the most popular relational databases introduced JSON as native column type, which makes it easier to store and query dynamic database schema. In this paper, we review the currently popular techniques of storing data with a dynamic model with a large number of relationships between entities in relational databases. We focus on creating a simple dynamic schema with JSON in the most popular relational databases and we compare it with well-known EAV/CR data model and the document database. The results of precisely selected tests in the field of Criminal Data suggest that the use of JSON in dynamic database schema greatly simplifies queries and reduces their execution time compared to widely used approaches.