Unification of a data presentation model and format conversion based on a non-relational Neo4j DBMS

Nowadays, due to the digitalization concept, a lot of software tools have appeared, including those using artificial intelligence methods that process large data streams (big data) of varying degrees of complexity. Voice assistants, chat bots, search recommender systems not only use incoming up-to-date data, but also store and analyze changes in this data, the number of which is constantly growing. Under the conditions of a combinatorial explosion hazard, the multidimensional modeling problems, the efficient requests processing, and the necessary information extraction arise. This article presents the analysis of the possibility of increasing the efficiency of multidimensional OLAP modeling and temporal data extraction based on built-in software components offered by the non-relational DBMS Neo4j. The choice of a graph DBMS is due to the absence of the need to strictly fix the data structure at the initial stage, as well as on the flexibility of the data presentation structure itself, which can change as new information becomes available. Making changes to strict prefixed relational table views is an expensive operation. The typical way to store temporal data (time moments and intervals) is to store timestamps as node attributes. At the same time, this option for storing and handling events may not be effective enough in the case of a large dimension of the data representation. The experimental results have shown that the graph of a multidimensional data cube can be projected onto the coordinate axes in the form of separate temporal slices, where the abscissa axis displays the event start time, and the ordinate axis displays its end time. Additional axes, if necessary, can be introduced to determine the cause-effect relationship of processes occurring simultaneously in time. At the same time, the rules of Allen's temporal logic will be supported. The paper considers the possibility of unifying the representation model of the internal data structure of varying complexity based on graphs.