RU2811359C1 - Method and system for forming partitioned data marts containing geodata and their use in process of data storage operation - Google Patents

Abstract

FIELD: information technologies.

SUBSTANCE: invention relates to a method and system for generating partitioned data marts containing geodata. The method contains stages at which data is received as input, including: geodata, a list of numerical values from the minimum to the maximum value of the scale level of the generated data marts and a dataset with arbitrary attributes for forming a data mart. The received geodata and dataset are combined. Based on the result of the merger carried out at the previous step, tiles and their identifiers are formed for all levels of scale of the generated data marts as follows: for the current level of scale, in a cycle using tile identifiers, all data available for this tile in the combined dataset is obtained; the tiles are converted into a format for processing by the data mart and stored in the memory of the data storage device. The resulting tiles are used during the operation of the data warehouse to form partitioned data marts containing geodata in the following way: selecting the required level of scale; automatically detecting all tile IDs at the current zoom level; requesting tiles according to certain identifiers, creating and visualizing corresponding data marts.

EFFECT: increasing the efficiency of tile identification.

8 cl, 5 dwg

Description

TECHNICAL FIELD

[1] This technical solution generally relates to methods for processing data and preparing data structures for viewing and visualization, namely, a method for creating partitioned data marts containing geodata and using them during the operation of a data warehouse.

BACKGROUND OF THE ART

[2] Currently, there is a known method of processing data in data warehouses, in which three standard data layers are logically distinguished:

• Initial (stage);

• Normalized (detailed);

• Adapted for viewing (showcases).

[3] As a rule, separate technical solutions are used to process each layer. Storefronts are often partitioned (sharded, for example, on various database instances) according to some criteria, for example, period, client id, and in general, according to any other attribute.

[4] In any data marts, when it comes to big data, partitioning is usually used. In particular, geovitrines partition data by assigning data to one or another geographical object.

[5] The technical solution described in RU 2681361 “SYSTEM FOR DEVELOPING A USER INTERFACE FOR INPUT, DISPLAY AND MODIFICATION OF VECTOR SPATIAL DATA” is known from the prior art, 04/18/2018, Federal State Treasury Military Educational Institution of Higher Education Academy of the Federal Security Service of the Russian Federation (RU ): “the system for generating a user interface for entering, displaying and modifying vector spatial data additionally contains a control unit for processing a set of initial vector spatial data, a unit for generating program code for functional parts of the user interface, a unit for storing metadata about the original vector spatial data, a unit for storing intermediate data, a block for storing metadata about the output vector spatial data.”

[6] The technical solution described in US11036752B2 “Optimizing incremental loading of warehouse data”, 08.08.2016, Oracle International Corp. is known from the prior art. (US): optimization of incremental data loading into the database is presented by identifying affected partitions and disabling only these partitions while data is being loaded, and data loading and storage is also described.”

[7] The disadvantages of the known solutions are the fact that when processing data and partitioning it, each partition has a format that can only be worked with by the specific database management system in which the partitioning process was carried out, which imposes significant restrictions on their use.

DISCLOSURE OF INVENTION

[8] This technical solution is aimed at eliminating the disadvantages inherent in existing solutions known from the prior art.

[9] The technical problem solved in this technical solution is that when processing data and partitioning it, each partition has a format that can only be worked with by a specific database management system in which the partitioning process was carried out, which imposes significant restrictions on their use.

[10] The main technical result that appears when solving the above problem is the expansion of the arsenal of technical means by providing the ability to create and use partitioned data in the form of static and ready-to-transfer files.

[11] Additional technical results that appear when solving the above problem are acceleration of data processing, acceleration of integration of data storage modules and acceleration of data caching at various levels of processing when working with partitioned data.

[12] The specified technical results are achieved thanks to the implementation of a method for forming partitioned data marts containing geodata and their use during the operation of a data warehouse, implemented using a processor and a data storage device, including the following steps:

• receive data as input, including: geodata, a list of numerical values from the minimum to the maximum value of the scale level of the generated data marts and a dataset with arbitrary attributes for forming a data mart;

• combine the received geodata and dataset;

• based on the result of the combination carried out at the previous step, tiles and their identifiers are formed for all levels of scale of the generated data marts as follows:

for the current zoom level, in a cycle using tile identifiers, all data available for this tile in the combined dataset is obtained;

convert the tiles into a format for processing by the data mart and store them in the memory of the data storage device.

• use the received tiles during the operation of the data warehouse to form partitioned data marts containing geodata in the following way:

select the required zoom level;

automatically detect all tile IDs at the current zoom level;

request tiles according to certain identifiers, create and visualize corresponding data marts.

[13] In one of the particular examples of the implementation of the method, when generating tiles and their identifiers, additional calculations are performed on the data, allowing the attributes to be changed.

[14] In another particular example of the method, the selection of the scale level is carried out through preset values.

[15] In another particular example of the method, the selection of the scale level is carried out dynamically.

[16] In addition, the stated technical result is achieved through a system for generating partitioned data marts containing geodata and their use during the operation of the data warehouse, containing:

at least one data processing device;

at least one data storage device;

at least one program, wherein the one or more programs are stored on one or more storage devices and executed on one or more processing devices, wherein the one or more programs perform the following steps:

• receive data as input, including: geodata, a list of numerical values from the minimum to the maximum value of the scale level of the generated data marts and a dataset with arbitrary attributes for forming a data mart;

• combine the received geodata and dataset;

• based on the result of the combination carried out at the previous step, tiles and their identifiers are formed for all levels of scale of the generated data marts as follows:

for the current zoom level, in a cycle using tile identifiers, all data available for this tile in the combined dataset is obtained;

convert the tiles into a format for processing by the data mart and store them in the memory of the data storage device.

• use the received tiles during the operation of the data warehouse to form partitioned data marts containing geodata in the following way:

select the required zoom level;

automatically detect all tile IDs at the current zoom level;

request tiles according to certain identifiers, create and visualize corresponding data marts.

[17] In one of the particular examples of the implementation of the method, when generating tiles and their identifiers, additional calculations are performed on the data, allowing the attributes to be changed.

[18] In another particular example of the method, the selection of the scale level is carried out through preset values.

[19] In another particular example of the method, the selection of the scale level is carried out dynamically.

BRIEF DESCRIPTION OF THE DRAWINGS

[20] The features and advantages of the present technical solution will become apparent from the following detailed description and the accompanying drawings, in which:

[21] FIG. 1 illustrates a block diagram of the claimed method.

[22] FIG. Figure 2 illustrates an example of the operation of the described technical solution at a scale level from 0 to 1.

[23] FIG. Figure 3 illustrates an example of the operation of the described technical solution at scale level 2.

[24] FIG. 4 illustrates an example of the operation of the described technical solution at scale level 13.

[25] FIG. 5 illustrates a system for implementing the claimed method.

IMPLEMENTATION OF THE INVENTION

[26] The terms and concepts necessary for the implementation of this technical solution will be described below.

[27] Partitioning or partitioning is the division of stored database objects (such as tables, indexes, materialized views) into separate parts with separate physical storage parameters. Used to improve manageability, performance, and availability for large databases. Possible criteria for dividing data used during partitioning are by predefined ranges of values, by lists of values, using hash function values; in some cases other options are used. Composite separation criteria are understood as sequentially applied criteria of different types. Unlike partitioning, where each segment is managed by a separate DBMS instance, and means of coordination between them are used (which allows you to distribute the database across several computing nodes), with partitioning, access to all sections is carried out from a single DBMS instance (or symmetrically from any instance of a cluster DBMS , such as Oracle RAC).

[28] Tiled, tiled or familiar graphics (from the English tile - tile) - a method of creating large images (usually levels in computer games) from small fragments of the same size. Tiles are small images of the same size that serve as fragments of a larger picture. The number of tiles per “world” can reach several hundred. In this case, the matrix of cells stores only the numbers of tiles, due to which memory savings are achieved when constructing huge two-dimensional spaces.

[29] A data set or dataset is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where each column of the table corresponds to a different variable, and each row corresponds to a record in the data set. Datasets store values for each variable, such as the height and weight of an object, for each member of the dataset. Datasets can also consist of a collection of documents or files.

[30] Scale level - a list of numerical values from a minimum to a maximum value agreed in advance between the end user and the geovitrine system. Viewing these storefronts is possible only at one of the zoom levels defined in this list. The method is suitable for various geodetic projections, but the projection of geoobjects onto the plane used in a specific implementation of the method must also be agreed upon in advance.

[31] The claimed technical solution can be implemented, for example, by a system, machine-readable medium, server, etc. In this technical solution, a system means, including a computer system, a computer (electronic computer), CNC (computer numerical control), PLC (programmable logic controller), computerized control systems and any other devices capable of performing a given, clearly defined sequence of operations (actions, instructions).

[32] A command processing device means an electronic unit or an integrated circuit (microprocessor) that executes machine instructions (programs).

[33] An instruction processing device reads and executes machine instructions (programs) from one or more storage devices, such as devices such as random access memory (RAM) and/or read only memory (ROM). ROM can be, but is not limited to, hard drives (HDD), flash memory, solid-state drives (SSD), optical storage media (CD, DVD, BD, MD, etc.), etc.

[34] Program - a sequence of instructions intended for execution by a computer control device or a command processing device.

[35] The term "instructions" as used in this application may refer generally to software instructions or software commands that are written in a given programming language to perform a specific function, such as, for example, retrieving and processing data, generating a user profile , reception and transmission of signals, analysis of received data, user identification, etc. Instructions can be implemented in a variety of ways, including, for example, object-oriented methods. For example, instructions can be implemented using the programming language C++, Java, Python, various libraries (for example, Microsoft Foundation Classes), etc. Instructions that carry out the processes described in this solution can be transmitted over both wired and wireless data channels, for example, Wi-Fi, Bluetooth, USB, WLAN, LAN, etc.

[36] The presented method for creating partitioned data marts containing geodata and their use during the operation of a data warehouse (Fig. 1 shows a diagram of the method) solves the problem of providing the ability to create and use partitioned data in the form of static and ready-to-transfer files, as well as accelerating data processing, accelerating the integration of data storage modules and accelerating data caching at various levels of data processing when working with partitioned data by sequentially performing the following steps:

• receive data as input, including: geodata, a list of numerical values from the minimum to the maximum value of the scale level of the generated data marts and a dataset with arbitrary attributes for forming a data mart;

• combine the received geodata and dataset;

• based on the result of the combination carried out at the previous step, tiles and their identifiers are formed for all levels of scale of the generated data marts as follows:

for the current zoom level, in a cycle using tile identifiers, all data available for this tile in the combined dataset is obtained;

convert the tiles into a format for processing by the data mart and store them in the memory of the data storage device.

• use the received tiles during the operation of the data warehouse to form partitioned data marts containing geodata in the following way:

select the required zoom level;

automatically detect all tile IDs at the current zoom level;

request tiles according to certain identifiers, create and visualize corresponding data marts.

[37] In the example of implementing the method below (functions in the JS language indicating the tools and examples of source and resulting data), GeoJSON with the wgs84 coordinate system was used as geodata. But it is possible to use any format (kml, csv, avro, parquet, ...) with any coordinate system.

Etc. with further geo-coordinates.

Etc. with further partitioned data (tiles) in the form of separate files.

[38] The described technical solution proposes to store and use partitioned data in the form of static files ready to be transferred to the user.

Advantages of using files:

1. File processing is one of the basic tools of most OSes, and is supported by the kernel. No additional tool development required

2. Data caching is easy at various levels (server, CDN network, proxy server, client device)

3. It is easy to integrate various data storage modules as file transfer is easy and there is a wide range of basic tools for this.

[39] Unlike solutions known in the prior art, where each partition has a format understandable only to a specific DBMS, in this solution each partition is stored as a separate file. Also in this solution it is proposed to combine in each file for the selected geographical area both the measures themselves that are of interest to the user, and auxiliary information (contours of areas, points, other cartographic layers).

[40] Consider the traditional way of storing partitioned data, for example in a PostgreSQL DBMS: traditionally, the configuration files and data files used by the database cluster are stored together in the cluster data directory, usually called PGDATA (after the name of the environment variable that can be used to define it ). The usual location for PGDATA is /var/lib/pgsql/data. There can be several clusters on one computer, managed by different server instances. The PGDATA directory contains several subdirectories and control files, as shown in the list below. In addition to these required elements, the cluster configuration files postgresql.conf, pg_hba.conf, and pg_ident.conf are traditionally stored in PGDATA, although they can be located elsewhere.

PG_VERSION A file containing the major version number of PostgreSQL.

base A subdirectory containing subdirectories for each database

global Subdirectory containing cluster-wide tables such as pg_database

pg_commit_ts Subdirectory containing transaction commit timestamp data.

pg_clog A subdirectory containing transaction commit status data.

pg_dynshmem A subdirectory containing files used by the dynamic shared memory subsystem.

pg_logical A subdirectory containing state data for logical decoding.

pg_multixact Subdirectory containing status data for multiple transactions (used for general row locks)

pg_notify Subdirectory containing LISTEN/NOTIFY status data.

pg_replslot Subdirectory containing replication slot data.

pg_serial A subdirectory containing information about completed serializable transactions.

pg_snapshots Subdirectory containing exported snapshots.

pg_stat Subdirectory containing permanent files for the statistics subsystem.

pg_stat_tmp Subdirectory containing temporary files for the statistics subsystem.

pg_subtrans Subdirectory containing subtransaction status data.

pg_tblspc A subdirectory containing symbolic links to tablespaces.

pg_twophase Subdirectory containing state files for prepared transactions.

pg_xlog Subdirectory containing WAL (Write Ahead Log) files.

postgresql.auto.conf File used to store configuration parameters set by ALTER SYSTEM.

postmaster.opts A file that records the command line parameters with which the server was last started.

postmaster.pid Lock file recording the current postmaster process ID (PID), cluster data directory path, postmaster startup timestamp, port number, Unix domain socket directory path (blank on Windows), first valid listen_address (IP address or * , or empty if TCP is not listening) and the shared memory segment identifier (this file is missing after the server is shut down)

[41] Each database in the cluster has a subdirectory in PGDATA/base named after the OID of the database in pg database. This subdirectory is the default location for database files; in particular, its system directories are stored there.

[42] Each table and index are stored in a separate file. For normal relationships, these files are named by the table or index file node number, which can be found in pg_class.relfilenode. But for temporary relationships, the file name is of the form tBBBFFF, where BBB is the identifier of the backend that created the file, and FFF is the file node number. In any case, in addition to the main file (a/k/a main fork), each table and index has a free space map that stores information about the free space available in the relation. The free space map is stored in a file with a name consisting of the file node number and the suffix _fsm. Tables also have a visibility map stored in a fork with the _vm suffix to keep track of which pages are known to not have dead tuples. Unregistered tables and indexes have a third branch known as the initialization fork, which is stored in the fork with the suffix _init.

[43] Let's consider the data structure from the described solution: the simplest possible structure of simple files with the same purpose. Each file contains both the geometry that fell into this tile, as well as dimensions and measures. Partitioning attributes, their number and sequence are selected depending on the problem being solved.

[44] Below is an example of partitioning by date (y_2021-01-01), layer (fields or adm) and XYZ grid (Leaflet). The file format used is protobuf (pbf):

[45] Advantages of combining data and geometry in one file:

1. Minimizing client-server interactions

2. It is easy to add a new level of data granularity

3. The complexity and labor-intensive operation of data queries does not depend on the number of objects, dimensions and measures in one query

Example 1: The user wants to obtain economic indicators of the Samara region over the past few years.

In this case, the data can be partitioned as follows:

• File 1: "Samara region, 2020, economic indicators"

• File 2: "Samara region, 2021, economic indicators"

• File 3: "Samara region, 2022, economic indicators"

Example 2: The user wants to obtain economic indicators of one district of the Samara region over the past few years.

In this case, the data can be partitioned as follows:

• File 1: "Samara region, Volzhsky district, 2020, economic indicators"

• File 2: "Samara region, Volzhsky district, 2021, economic indicators"

• File 3: "Samara region, Volzhsky district, 2022, economic indicators"

As can be seen from the examples above, there is no need to recalculate previously calculated data, but only to calculate a new “scale” of data.

[46] Data marts in data warehouses contain a portion of pre-calculated data (data layers). In the described solution, this concept is fully implemented, and moreover, not only the data is pre-calculated, but also the server’s response to the client is prepared in advance in the form of a ready-made file. The advantage of this approach is that calculations can be carried out during periods of minimal server load. All possible variants of user requests are pre-calculated, but only changed files are actually replaced. However, if the server part of the method implementation can determine a set of files that are affected by recalculation, then only these files can be recalculated and replaced. For example, if the economic indicators of one region of the Russian Federation have changed, then only files affecting this region will be recalculated, as well as all files of a higher hierarchy level (aggregates throughout the Russian Federation). In this case, files belonging to other regions will be recalculated and will not be affected.

[47] However, the method does not exclude the parallel use of traditional DBMSs for storing pre-calculated data on the server based on tile identifiers, in particular, for partitioning, storing auxiliary information, and calculating aggregates. Essential in this method is the optimization of the main user function - viewing data.

[48] Tile IDs can be obtained: either from the input requirements for the calculation; either based on the received geodata, or initially all possible values are taken.

[49] The file identifier encoding system is known to both the server and the client, and makes it easy to obtain the identifier of the requested data object.

[50] The coding system can be either standard or proprietary for each client.

As an example, consider:

• XYZ mesh from Leaflet

For example, to visualize a map of our planet, you can use a cylindrical projection (if the distortions of this projection are acceptable) and cutting into tiles of the same size (in pixels).

At the maximum distance (Zoom=0) the entire map fits into one tile: in Fig. 2.

At each subsequent zoom level, the number of tiles increases by 2 times (relative to the previous one) along the x and y axes.

Zoom=1: in Fig. 2;

Zoom=2: in Fig. 3;

Zoom=13: in Fig. 4.

[51] In a particular implementation, the user selects the geographic area that he needs by generating the identifier of the desired file using a previously known formula. The user then accesses the specific file.

[52] In a user application, for example, a browser, based on the coding system known from the previous paragraph and the current user actions, for example, the selected area for display, the file identifiers necessary for downloading are determined. Advantage: the formula is known in advance, it is simple, so this operation does not require large computing power.

[53] Based on the received list of file identifiers, the user device downloads them in a standard way for the user OS. Advantages:

• no implementation of file downloading is required - this functionality is standard and is already implemented in the user's OS;

• it is possible to use parallelization if there is support at the OS and protocol level, using various protocols (HTTP, FTP, S3);

• caching can be used at various levels (user device, local network (proxy server), server);

• local data caching on the user side is easily implemented, for example, when implementing a mobile application, since files need to be saved.

[54] When using the main user function of the system (data viewing), when data is changed in the system, the user device downloads only the changes, and not all the data. There are several well-known ways to implement this step, for example, using the E-Tag header of the HTTP protocol, parameterizing the rules for determining file identifiers. Advantages:

• minimizing the volume of transmitted data;

• existence of existing implementations of this step;

• unlimited horizontal scaling of the system, for example, you can simply copy identical files to N servers and perform balancing using the round robin method, which eliminates the need for complex management of container architecture;

• high speed of the server part, since there are no server calculations during a user request (all data is pre-calculated);

• high speed of the server part, since the user downloads mainly the necessary data;

• convenient management of data granularity.

[55] In general (see Fig. 5), the system for the formation of partitioned data marts containing geodata and their use during operation of the data warehouse (500) contains one or more processors (501), memory means, united by a common information exchange bus. such as RAM (502) and ROM (503) and input/output interfaces (504).

[56] The processor (501) (or multiple processors, multi-core processor, etc.) may be selected from a variety of devices commonly used today, for example, from manufacturers such as: Intel™, AMD™, Apple™, Samsung Exynos ™, MediaTEK™, Qualcomm Snapdragon™, etc. The processor or one of the processors used in the system (500) must also include a graphics processor, for example an NVIDIA GPU with a CUDA-compatible programming model or Graphcore, the type of which is also suitable for carrying out the method in whole or in part, and can also be used for training and application of machine learning models in various information systems.

[57] RAM (502) is a random access memory and is designed to store machine-readable instructions executable by the processor (501) to perform the necessary logical data processing operations. RAM (502) typically contains executable operating system instructions and related software components (applications, program modules, etc.). In this case, the available memory capacity of the graphics card or graphics processor can act as RAM (502).

[58] A ROM (503) is one or more permanent storage devices, such as a hard disk drive (HDD), a solid state drive (SSD), flash memory (EEPROM, NAND, etc.), optical storage media ( CD-R/RW, DVD-R/RW, BlueRay Disc, MD), etc.

[59] To organize the operation of device components (500) and organize the operation of external connected devices, various types of I/O interfaces (504) are used. The choice of appropriate interfaces depends on the specific design of the computing device, which can be, but is not limited to: PCI, AGP, PS/2, IrDa, FireWire, LPT, COM, SATA, IDE, Lightning, USB (2.0, 3.0, 3.1, micro, mini, type C), TRS/Audio jack (2.5, 3.5, 6.35), HDMI, DVI, VGA, Display Port, RJ45, RS232, etc.

[60] To ensure user interaction with the device (500), various means (505) of I/O information are used, for example, a keyboard, a display (monitor), a touch display, a touch pad, a joystick, a mouse, a light pen, a stylus, a touch panel, trackball, speakers, microphone, augmented reality tools, optical sensors, tablet, light indicators, projector, camera, biometric identification tools (retina scanner, fingerprint scanner, voice recognition module), etc.

[61] The networking facility (506) enables data transmission via an internal or external computer network, such as an Intranet, the Internet, a LAN, or the like. One or more means (506) may be used, but not limited to: Ethernet card, GSM modem, GPRS modem, LTE modem, 5G modem, satellite communication module, NFC module, Bluetooth and/or BLE module, Wi-Fi module and etc.

[62] The specific selection of device elements (500) to implement various software and hardware architectural solutions may vary while maintaining the required functionality provided. In particular, such an implementation can be performed using electronic components used to create digital integrated circuits. Without limitation, microcircuits can be used, the logic of which is determined during manufacture, or programmable logic integrated circuits (FPGAs), the logic of which is specified by programming. For programming, programmers and debugging environments are used to set the desired structure of a digital device in the form of a circuit diagram or program in special hardware description languages: Verilog, VHDL, AHDL, etc. An alternative to FPGAs are: programmable logic controllers (PLCs), basic matrix crystals ( BMK) requiring a factory production process for programming; ASICs are specialized custom large-scale integrated circuits (LSIs), which are significantly more expensive for small-scale and single-piece production. Thus, implementation can be achieved by standard means based on the classical principles of implementing the fundamentals of computer technology.

[63] The submitted application materials disclose preferred examples of implementation of a technical solution and should not be interpreted as limiting other, particular examples of its implementation that do not go beyond the scope of the requested legal protection, which are obvious to specialists in the relevant field of technology.

Claims (29)

1. A method for generating partitioned data marts containing geodata, implemented using a processor and a data storage device, including the following steps: - receive data as input, including: geodata, a list of numerical values from the minimum to maximum value of the scale level of the generated data marts and a dataset with arbitrary attributes for forming a data mart; - combine the received geodata and dataset; - based on the result of the combination carried out at the previous step, tiles and their identifiers are formed for all levels of scale of the generated data marts as follows: - for the current level of scale in a cycle, using tile identifiers, all data available for this tile in the combined dataset is obtained; - convert the tiles into a format for processing by the data mart and store them in the memory of the data storage device; - use the received tiles during the operation of the data warehouse to form partitioned data marts containing geodata in the following way: - select the required scale level; - automatically detect all tile IDs at the current zoom level; - request tiles according to certain identifiers, create and visualize corresponding data marts. 2. The method according to claim 1, characterized in that when generating tiles and their identifiers, additional calculations are performed on the data, allowing the attributes to be changed. 3. The method according to claim 1, characterized in that the selection of the scale level is carried out through preset values. 4. The method according to claim 1, characterized in that the choice of the scale level is carried out dynamically. 5. A system for generating partitioned data marts containing geodata, containing: - at least one data processing device; - at least one data storage device; - at least one program, where one or more programs are stored on one or more data storage devices and executed on one or more data processing devices, and one or more programs performs the following steps: - receive data as input, including: geodata, a list of numerical values from the minimum to maximum value of the scale level of the generated data marts and a dataset with arbitrary attributes for forming a data mart; - combine the received geodata and dataset; - based on the result of the combination carried out at the previous step, tiles and their identifiers are formed for all levels of scale of the generated data marts as follows: - for the current level of scale in a cycle, using tile identifiers, all data available for this tile in the combined dataset is obtained; - convert the tiles into a format for processing by the data mart and store them in the memory of the data storage device; - use the received tiles during the operation of the data warehouse to form partitioned data marts containing geodata in the following way: - select the required scale level; - automatically detect all tile IDs at the current zoom level; - request tiles according to certain identifiers, create and visualize corresponding data marts. 6. The system according to claim 5, characterized by the fact that when generating tiles and their identifiers, additional calculations are performed on the data, allowing the attributes to be changed. 7. The system according to claim 5, characterized in that the choice of the scale level is carried out through preset values. 8. The system according to claim 5, characterized in that the choice of the scale level is carried out dynamically.