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An increasing amount of data is stored and managed in today's information systems. For this purpose, relational databases are used very often. Fast access to data is becoming increasingly important and great emphasis is placed on its improvement. The data access time can be significantly improved by creating partitions and index structures, or their combinations. From the point of view of efficiency, it is not appropriate or necessary to access all data. They can be divided into smaller parts, which will facilitate the execution of certain operations and bring efficiency, whether in terms of time or performance. As far as indexing is concerned, there is also an opportunity to access only the data sought, thus avoiding the Table Access Full method. This paper deals with the effect of partitioning and indexing on data access time, which is compared in seven different scenarios of different combinations of partitions created over tables and indexes.

Relational databases have fulfilled the role of a primary data repository for decades. With the development of modern technologies, the type of databases has come to the forefront which - unlike relational databases - have an unconventional structure which allows greater flexibility in data storage. Since the design and implementation of non-relational databases, which dates to the end of the 20th century, various types of non-relational databases have been proposed - the most popular non-relational databases include document, graphic, column and key-value databases. Although the flexibility of non-relational databases is an excellent feature in many respects, the freedom of the data structure and the freedom of defining data type cause considerable problems in number of database aspects. One of the situations in which the freedom of the data structure causes problems is the transformation of non-relational databases (the most common document database MongoDB or DynamoDB) to relational or other type of database. Undefined values often cause a termination of a running process and/or higher error rate in the transformation process. For these reasons, it is necessary to solve problems with undefined values either before or during the process of transformation and thus avoid application errors. To this end, we have created a module for solution of the problem of undefined values in the transformation of a non-relational database MongoDB to a relational database Oracle. The main purpose of this article is not to eliminate errors when starting the process, but to design and implement a module that can apply a dynamic solution based on input data and the selection of the target database when unknown values occur in the non-relational database MongoDB.

Relational database systems cover the main part of the current data management of information technology. Data are formed into the relations connected using relationships. Each tuple is physically stored in the database operated by the background processes of the instance. The user query is transferred to the server, analyzed and processed. Data are sent back to the user as the result set. The main part of the processing and query evaluation is just access to the data themselves. If there are a suitable index and conditions, access can be optimized. Vice versa, if there is no relevant index, the whole table must be scanned sequentially, which can bring high demands and processing steps resulting in poor performance. This paper deals with index access methods, the process of the data access, its optimization, architecture and whole apparatus. The main contribution is in our own approach, which aims to remove the need to search the entire table physically by using Table Access Full access path. The structural index denoted as the master is used to access and locate a record with an emphasis on fragmentation options.

Data are used in a large number of different fields. Large databases with huge amounts of data come to the fore. They are a very important part of many of information systems, from commercial systems, through technical and technological systems, the web, and mobile applications to the management of scientific data in various fields. Fast access to data is, therefore, becoming increasingly important today and great emphasis is placed on improving it. Initially, the data access time may be only slightly increased working with smaller tasks, but with a larger number of larger tasks, there is a significantly higher data access time that needs to be reduced. From the point of view of efficiency, it is not appropriate or necessary to access all data, and therefore it is necessary to divide this data into smaller parts and thus create partitions, which will facilitate the execution of certain operations and bring efficiency, whether in terms of time or performance. This paper discusses partitioning, its various techniques, methods, and the benefits it brings. It compares access times to tables with and without partitions, regarding various numbers of table parts that are accessed.

This article describes the theoretical basis of the optimization to be used in the information system supporting the bike-sharing system. We define the problems which occur in this system and present their mathematical optimization models with a detailed description.