Dimension UI

Contents

• Program description

• Download
• Development

• Documentation
• License
• Contact

Program Description

Workspace

Dashboard

Report

Ad-hoc

General Information

Key Features of Dimension UI:

| № | Feature | Description | |---|------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | Real-time Data Collection & Analysis | Data is collected in real time, enabling users to monitor metrics online, analyze responses to various impacts, review historical data, and perform comparative analysis with other indicators | | 2 | Flexible Data Collection Profiles | Rapid configuration of data collection profiles — particularly useful when speed is critical, allowing users to quickly gather specialized statistical data for detailed evaluation of system or component characteristics | | 3 | Local Storage in Dimension-DB | Block-columnar database with built-in data compression and deduplication. Project repositories on GitHub | | 4 | Advanced Time-Series Data Mining | Built-in support for Matrix Profile and ARIMA algorithms for advanced time-series analysis and predictive analytics | | 5 | No-code DB Exploration | Ad-hoc data visualization from external databases via JDBC with automatic SQL query generation. Both time-series and regular table data supported | | 6 | Dashboard System | Displaying metrics from multiple sources in a single interface. Quick access to metrics simplifies workflows when reviewing large volumes of analytical data | | 7 | Built-in Reporting System | Generating PDF reports based on collected data for further analysis |

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Application Areas

| № | Application Area | Description | |---|-------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | Real-time Monitoring | Monitoring information systems, hardware/software systems, and databases in real time | | 2 | Load & Stress Testing | Evaluation of hardware and software systems during load and stress testing, preparation of reports based on testing results | | 3 | IoT Monitoring | Monitoring the parameters of the Internet of Things (IoT) devices | | 4 | Information Security | Complex monitoring of information security of systems with access to data sources via JDBC and HTTP API (Prometheus, JSON) | | 5 | Education & Training | Training in courses related to data processing and analysis: quick setup of data collection, visualization and advanced data analysis for system administrators, DBAs, developers and analysts | | 6 | Diagnostics | Diagnostics of problems in complex systems providing APIs via JDBC and HTTP (Prometheus, JSON), including heterogeneous environments and Java microservices | | 7 | Multidimensional Analysis | Analysis and visualization of time-series data (real-time and historical), including predictive analytics via JDBC and HTTP (Prometheus, JSON) | | 8 | No-code DB Exploration | Direct JDBC connection to external databases to explore time-series data in tables and views with automated SQL generation |

Supported Databases for Direct JDBC Connection (No-code Mode)

| No. | Database | Type | |-----|----------------------|------------| | 1 | PostgreSQL | relational | | 2 | Oracle | relational | | 3 | Microsoft SQL Server | relational | | 4 | MySQL | relational | | 5 | ClickHouse | analytical | | 6 | Firebird | relational | | 7 | SQLite | relational | | 8 | DuckDB | analytical |

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Minimum technical requirements

Hardware requirements

Table 1. Hardware requirements

| Parameter | Description | |:-----------------|:-----------------------------------------------------------------------------------------------------------------------------------| | CPU and RAM | Processor with a frequency of at least 500 MHz and a memory capacity of at least 250 MB, depending on the volume of processed data | | CPU architecture | Intel 64-bit (x8664), AMD 64-bit (x8664), Arm 64-bit (aarch64) | | Disk | Disk size depending on the volume of processed data |

Software requirements

Table 2. Software requirements

| Software | Requirements | Link | |:-------------|:-----------------------|:------------------------------------------------------------| | Java | Java version 25+ | Java | | Maven | Latest current version | Maven | | Git | Latest current version | Git | | Dimension-DB | Latest current version | Dimension-DB | | Dimension-DI | Latest current version | Dimension-DI | | Dimension-TT | Latest current version | Dimension-TT |

Operating system requirements

Table 3. Operating system requirements

| Software | Requirements | |:-----------------|:----------------------| | Operating system | Windows, Linux, MacOS |

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Getting started with the project

Building the Project

To compile the application into an executable jar file, do the following:

• Install JDK version 25 or higher, Maven and Git on your local computer:

java -version  
    mvn -version
    git --version

• The Dimension-UI application depends on Dimension-DI and Dimension-DB, which must be installed first:

git clone <Dimension-DI URL>
    cd dimension-di
    mvn clean install

git clone <Dimension-DB URL>
    cd dimension-db
    mvn clean install

• Download the source codes of the application to your local computer using Git:

git clone <Dimension-UI URL>
    cd dimension-ui

• Compile and install modules for project using Maven:

mvn clean compile
    mvn clean install -U

• Execute the Maven command to build an executable jar file with tests running:

mvn clean package

The executable jar file with launch scripts for Windows/Linux will be in the dimension-ui-<VERSION>.zip archive at the relative path desktop/target

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Installation and configuration

• Windows Platform, run.bat

SET JAVA_HOME=C:\PROGRAM FILES\JAVA\jdk-25  
    SET JAVAEXE="%JAVAHOME%\bin\java.exe"
    chcp 65001
  
    %JAVA_EXE% -Xmx1024m -DtestMode=false -Dfile.encoding=UTF8 -jar desktop-1.0-SNAPSHOT-jar-with-dependencies.jar

• Linux platform, run.sh

#!/bin/bash
  
    export JAVA_HOME=/usr/lib/jvm/java-25-openjdk-amd64
    export JAVAEXE=$JAVAHOME/bin/java
    export LANG=en_US.UTF-8
$JAVA_EXE -Xmx1024m -DtestMode=false -Dfile.encoding=UTF8 -jar desktop-1.0-SNAPSHOT-jar-with-dependencies.jar

To start working with the application you need to run the executable file run.bat/run.sh

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Description of the logical structure

Application Architecture

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Algorithms

For anomaly detection and motif discovery, the Matrix profile algorithm family is used, which uses the Z-normalized Euclidean distance to compare time series ranges.

Z-normalized Euclidean distance

"Z-normalization" is a standardization process, and its goal is to transform all of our data so that the mean is 0 and the standard deviation is 1. It's like translating all data into a common language, so that any values ​​can be easily compared, without being affected by the scale or spread of the original values.

When we put this together, the Z-normalized Euclidean distance allows us to compare the Euclidean distance between data sets that have been normalized to a standard form. This way, we can be sure that we are comparing a distance that is independent of the original scales of the data.

This distance is often used in machine learning and statistics, for example for time series analysis or data clustering, where it is important to compare the "shape" of the data rather than the "size".

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Program API

Methods Used

| Method | Description | GUI | |---------------------|--------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------| | JDBC Connection | Connecting to databases (PostgreSQL, Oracle, etc.) to collect data. | 🔍 Preview | | HTTP Requests | Requesting data through APIs (Prometheus) to collect metrics. | 🔍 Preview | | Data Analysis | Applying algorithms (Matrix Profile, ARIMA) to detect anomalies, patterns in data, and predictive analytics. | 🔍 Preview | | Visualization | Generating charts and graphs based on retrieved data. | 🔍 Preview |

Input Parameters

| Parameter | Description | GUI | |----------------------|----------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------| | JDBC URL | Database connection string (e.g., jdbc:postgresql://localhost:5432/mydb) | 🔍 Preview | | HTTP Endpoint | Prometheus API URL (e.g., http://localhost:9090/api/v1/query) | 🔍 Preview | | SQL Query | Table name, SQL query, or metric name (e.g., cpuusage) | 🔍 Preview | | Table Column | Table column for data visualization, grouping type, chart format | 🔍 Preview | | Time Range | Data interval for analysis (start/end date) | 🔍 Preview |

sequenceDiagram
    participant User as User
    participant Program as Program
    participant Profile as Profile
    participant Task as Task
    participant Workspace as Workspace
    participant Metrics as Metrics
    participant Reporting as Reporting
    participant Adhoc as Ad-hoc Queries
User->>Program: Launch     User->>Profile: Create Profile     alt Manual Mode         User->>Task: Fill Out Task         Task->>Task: Bind Connection         Task->>Task: Specify Requests     else Use Templates         User->>Profile: Fill Profile Using Templates     end     User->>Workspace: Go to Profile     Workspace->>Profile: Launch with Start Button     User->>Metrics: View Metrics     Metrics->>Metrics: Compare     Metrics->>User: Launch Analysis / View History     User->>Metrics: Select Metrics from Various Profiles     User->>Reporting: Go to Reporting Tab     Reporting->>User: Select Metrics for Report     User->>Adhoc: Go to Ad-hoc Queries     Adhoc->>User: Select Connection, Table/View     Adhoc->>User: Display Data and Analyze     User->>Program: Complete Program

%JAVA_EXE% -Xmx1024m -DLaF=dark -Dfile.encoding=UTF8 -jar desktop-1.0-SNAPSHOT-jar-with-dependencies.jar