Methods and algorithms for processing big data using quantum algorithms

Characteristics of big data
Big data characteristics are features that distinguish large volumes of data from more traditional data sets. Let us outline several main characteristics of big data:
Volume: Big data is characterized by a sheer volume of information. These can include terabytes, petabytes, exabytes, or even zettabytes of data. Unlike small data sets, big data requires special methods and tools to process and analyze it.
Speed: Big data can enter the system at very high speed. For example, in the case of streaming data or data at the first point in time. Processing this data requires the ability to process information on the fly and in a timely manner. Processing large volumes of data can require significant computing resources and time. Therefore, efficient data processing methods and algorithms are necessary to perform calculations in a reasonable time. When the speed becomes very high, the tools corresponding to the data are likely to be processed in a special way. Big data tools are capable of extracting and analyzing data from huge data sets very quickly, which is especially useful for fast-changing data that can be analyzed using in-memory processing. Big data tools are capable of distributing complex processing jobs across a large number of nodes, reducing computational complexity [5].
Diversity: Big data is often a mixture of different types of data from different sources. This may include structured data (such as tables and databases), semi-structured data (such as JSON or XML), and unstructured data (such as text files or media files). Handling these diverse types of data requires flexibility and powerful algorithms. Big data can include a variety of types of information such as texts, images, audio and video, and files. This requires special data processing techniques that can handle different formats and structures.
Working with big data requires special processing methods and algorithms, such as distributed data storage, parallel computing and machine learning [2].
In general, big data represents huge amounts of information that require special methods and tools for effective processing, analysis and use (Figure 1).
Big data characteristics are features that distinguish large volumes of data from more traditional data sets. Several key characteristics of big data, such as the 3Vs, have the greatest impact on performance.
The known processing time is directly proportional to the increase in data volume. That is, the larger the volume of data, the more time it takes to process it. It should be noted here that the time for storing, processing and analyzing data is directly proportional not only to the volume of data, but also to the speed of data flow and its diversity (1).
(1)
If the dependence of the time for storing, processing and analyzing data on the volume of data, their speed and variety is expressed by the equations given below in (2), then equation (1) can be written as equation (3).





(2)




(3)

In general, the time for storing, processing and analyzing data, as well as the dependence on the volume of data, its speed, and diversity can also be expressed through the graph presented in Figure 2.

Fig. 2 Uneven increase in processing time when exposed to different characteristics of big data

As can be seen from the above formulas and the graph presented in Figure 3, data processing time increases as the volume, speed and variety of data increases. Of course, this graph cannot be brought to t=const by any method of big data processing, but the level of accuracy of this graph can be increased.

In addition to these characteristics, several additional characteristics of big data are no less important - complexity, end-to-end communication, variability, heterogeneity [28].
It is these characteristics that make processing big data a complex task and require special approaches and tools for its effective processing and analysis.

Fig.3 Big data infrastructure