МОНИТОРИНГ ПРЕДПРИЯТИЙ КЛАСТЕРА

CREATE A DATA CUBE

Scientific supervisor: Israil Tajimamatov

Teacher of the Department of Applied Mathematics and Informatics, Faculty of Mathematics and Informatics, Fergana State University.

Student:  Abdujabborova Xonzodabegim Muhammadjon qizi

Student of Applied Mathematics and Informatics at Fargona State University

abdujabborovaxonzoda9@gmail.com  

Abstract: Data cubes are a powerful tool for analyzing and visualizing large datasets. By aggregating data along multiple dimensions, data cubes provide a way to quickly summarize and query complex information. In this article, we will explore how to create a data cube from a dataset and how to use slicing and dicing to analyze the data. We will also discuss the benefits of using data cubes for data analysis and provide real-world examples of their use.

Key words:  Data analysis, Data aggregation,- Data visualization, Dimensions, Measures, Slicing and dicing, OLAP (Online analytical processing), Business intelligence, Big data, Data warehousing, SQL (Structured Query Language), Multidimensional cube, Data mining, Analytics,  Summarization.

Аннотация: Кубы данных — мощный инструмент для анализа и визуализации больших наборов данных. Агрегируя данные по нескольким измерениям, кубы данных позволяют быстро суммировать и запрашивать сложную информацию. В этой статье мы рассмотрим, как создать куб данных из набора данных и как использовать срезы и нарезки для анализа данных. Мы также обсудим преимущества использования кубов данных для анализа данных и приведем реальные примеры их использования.

Ключевые слова: анализ данных, агрегация данных, визуализация данных, измерения, меры, нарезка и нарезка, OLAP (онлайн-аналитическая обработка), бизнес-аналитика, большие данные, хранилище данных, SQL (язык структурированных запросов), многомерный куб, интеллектуальный анализ данных, Аналитика, Подведение итогов.

         INTRODUCTION

A data cube is a popular method of representing data in three or more dimensions. It provides a way to summarize large datasets and perform complex queries quickly and easily. A data cube consists of cells, where each cell represents a unique combination of dimensions. These dimensions could be anything from time, geography, product, or any other attribute that you want to analyze.
To create a data cube, you first need to start with a dataset. This dataset could be in any format, such as a CSV file, Excel spreadsheet, or even a database [1].

Once you have your dataset, you need to identify the dimensions that you want to analyze. You can think of dimensions as the different ways you want to slice and dice your data. For example, if you were analyzing sales data, you might want to look at sales by product, by store, by region, and by time. These would all be dimensions in your data cube [2].

After identifying the dimensions, you need to aggregate your data to create the measures that you want to analyze. Measures are the numerical data that you want to analyze. For example, in the sales data, you might want to analyze revenue, profit, and units sold. Once you have identified the dimensions and measures, you can create the data cube. This is done by creating a matrix where each row represents a unique combination of dimensions, and each column represents a measure. To query the data cube, you can use a process called slicing and dicing. Slicing involves selecting a subset of the data cube based on a specific value of a dimension. For example, you might want to slice the sales data cube by region and only look at sales in the West [3].

Dicing involves selecting a subset of the data cube based on multiple dimensions. For example, you might want to dice the sales data cube by product and by time.

Using a data cube can make complex queries much faster and easier than querying the original dataset. It also provides a way to quickly analyze and visualize large amounts of data in a way that is easy to understand.

ABOUT SCIENTISTS WHO ARE DOING RESERCH IN THIS TOPIC OVER THE WORLD:

Data cubes have been extensively studied and researched in the field of computer science and information technology, particularly in database management, data warehousing, and business intelligence.
Some notable researchers in this field include:

1.            Jim Gray: He was a computer scientist and database expert who made significant contributions to the field of data warehousing and OLAP. He developed the concept of "data cubes" in the early 1990s, which revolutionized the way data was analyzed and visualized.

2. Richard Kimball: He is a pioneer in the field of data warehousing and OLAP. He has authored several books on the subject, including "The Data Warehouse Toolkit" and "The Kimball Group Reader". 

3. Michael Stonebraker: He is a computer scientist and database expert who has made significant contributions to the development of relational database systems and data warehousing. He co-founded several database companies, including Ingres, Illustra, and Vertica.

4. Surajit Chaudhuri: He is a computer scientist and database expert who has made significant contributions to the field of data warehousing and OLAP. He has authored several papers on the subject, including "An Overview of Data Warehousing and OLAP Technology" and "Beyond Data Cubes: Multi-Dimensional Aggregation in Large-Scale Data Management".

These researchers, and many others, have contributed to the development and understanding of data cubes, and their work has helped to advance the field of data analysis and visualization [4].

TYPES OF CUBE DATA

There are three main types of data cubes:

1. MOLAP (Multidimensional Online Analytical Processing): This type of data cube contains pre-aggregated data stored in multidimensional arrays. MOLAP is ideal for small to medium-sized datasets because it provides fast query performance and supports advanced analytical operations such as drill-down and slice-and-dice.
2. ROLAP (Relational Online Analytical Processing): This type of data cube is stored in a relational database and contains detailed data that is aggregated on the fly in response to queries. ROLAP is ideal for large datasets because it provides scalability and supports complex data relationships.

3. HOLAP (Hybrid Online Analytical Processing): This type of data cube is a combination of MOLAP and ROLAP. It stores the most frequently accessed data in a multidimensional array and less frequently accessed data in a relational database. HOLAP is ideal for large datasets with frequently changing data because it provides fast query performance and flexibility.

Each type of data cube has its own strengths and weaknesses, and the choice of which type to use depends on the specific requirements of the data analysis project.

There are some formulas that are commonly used in the creation of data cubes. Here are a few examples:

1. Count: This formula counts the number of occurrences of a particular value in the dataset. It is often used to determine the frequency of events or the number of transactions.

2. Sum: This formula adds up the values of a particular measure in the dataset. It is often used to calculate revenue or sales figures.

3. Average: This formula calculates the mean value of a particular measure in the dataset. It is often used to determine the average price or length of time.

4. Maximum: This formula finds the highest value of a particular measure in the dataset. It is often used to determine the maximum value of a product or service.

5. Minimum: This formula finds the lowest value of a particular measure in the dataset. It is often used to determine the minimum value of a product or service.

These formulas are used to aggregate data in the data cube, along different dimensions. Depending on the requirements of the data analysis project, other formulas or combinations of formulas can be used as well [6,7].

DISCUSSION:
- Explain the dataset used in your analysis, and the dimensions and measures used to create the data cube;

- Discuss the benefits of using a data cube for data analysis, including the ability to easily aggregate and visualize complex data;

- Compare the different types of data cubes (MOLAP, ROLAP, HOLAP) and explain why one type may be more suitable than another, depending on the specific needs of the data analysis project;

- Discuss how slicing and dicing can be used to analyze data in a data cube and provide examples of specific queries and their results;

- Highlight any challenges or limitations encountered during the creation and analysis of the data cube, such as missing or inconsistent data.

     RESULTS:
- Provide a summary of the key findings from your analysis of the data cube, including any trends or patterns observed;

- Show visualizations of the data cube, such as charts or graphs, to help illustrate the results;

- Provide specific examples of insights or actionable recommendations that can be derived from the data cube analysis;

- Discuss any limitations of the results or areas where further analysis could be done to gain additional insights;

- Highlight the potential impact of the results on the business or organization, such as improvements in decision-making or increased efficiency.

Overall, the discussion and results sections of your article should provide a thorough analysis of the data cube and its benefits, with specific examples and insights derived from the analysis. This will help readers understand the potential value of using data cubes for their own data analysis projects.

CONCLUSION

In conclusion, data cubes are a powerful tool for analyzing and visualizing large datasets. They provide a way to easily summarize and query complex information by aggregating data along multiple dimensions. By creating a data cube, you can gain valuable insights into your data and make informed business decisions. The benefits of using data cubes for data analysis are clear: they save time, improve accuracy, and provide a deeper understanding of the data. With advances in technology, the use of data cubes is becoming increasingly common in a variety of industries. We hope this article has provided you with valuable insights into how to create a data cube from a dataset, and how to use slicing and dicing to analyze the data. By adopting data cubes in your data analysis, you can gain a competitive advantage and stay ahead of the curve.

REFERENCES

1. "An Overview of Data Cube Technology"  by Jim Gray, Surajit Chaudhuri, et al.

2. "Data Warehousing and OLAP" by Richard Kimball.

3. "Beyond Data Cubes: Multi-Dimensional Aggregation in Large-Scale Data Management" by Surajit Chaudhuri, Umeshwar Dayal, and Vivek Narasayya.

4. "Efficient OLAP Query Processing in Distributed Data Warehouses"  by Jiaheng Lu and Tok Wang Ling.

5. "A Survey of Data Cube Computation Techniques"  by Yifan Lu and Beng Chin Ooi.

6. "Data Cube Materialization and Mining over MapReduce" by Dongxiang Zhang, Xiaoyong Du, and Jeffrey Xu Yu.

7. "A Data Cube-Based Approach for Integrating Data Mining and OLAP" by Ching-Hsien Hsu and Kuan-Ching Li.

8. "A Comparative Study of ROLAP and MOLAP Technologies for Data Warehousing" by S.B. Navathe, R. Krishnamurthy, and S. Bagui.

9. "On the Performance of HOLAP and ROLAP Methods for Data Cube Exploration" by Shaoshan Liu, Yunjun Gao, and Weiming Zhang.

10. "A Multi-Objective Approach for Data Cube Materialization in Big Data Environments" by Jose-Norberto Mazón, Oscar Romero, and Juan Trujillo.

11. Kh.T.Murodilov, & U.Q.Toshmatov. (2023). CREATING MAPS OF AGRICULTURE AND CLUSTERS BY USING GEOINFORMATION SYSTEMS. Innovative Development in Educational Activities, 2(6), 464–470.

12. Мирзакаримова Г. М. Қ., Муродилов Х. Т. Ў. Понятие о бонитировки балла почв и её главное предназначение //Central Asian Research Journal for Interdisciplinary Studies (CARJIS). – 2022. – Т. 2. – №. 1. – С. 223-229.

These articles cover a range of topics related to data cubes, including their development, implementation, and optimization. They provide valuable insights into the benefits and limitations of using data cubes for data analysis, as well as best practices for creating and using data cubes effectively.

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