CC BY
2
Пример
Появление квантовых вычислений и связанных с ними алгоритмов, таких как алгоритм Шора, требует от криптографов пересмотра существующих методов и разработки новых алгоритмов, устойчивых к квантовым атакам.
Заключение
Криптоанализ играет ключевую роль в обеспечении безопасности данных в современном мире. Несмотря на его высокую вычислительную сложность и связанные с ним этические вопросы, преимущества криптоанализа в повышении безопасности криптографических систем, разработке новых методов шифрования и защите от несанкционированного доступа очевидны. Постоянное развитие этой области науки способствует созданию более надежных и эффективных криптографических решений.
Список использованной литературы:
1. Schneier, B. (1996). Applied Cryptography: Protocols, Algorithms, and Source Code in C. John Wiley & Sons.
2. Menezes, A., van Oorschot, P., & Vanstone, S. (1996). Handbook of Applied Cryptography. CRC Press.
3. Koblitz, N. (1994). A Course in Number Theory and Cryptography. Springer.
4. Shor, P. W. (1994). Algorithms for Quantum Computation: Discrete Logarithms and Factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science (FOCS).
5. Stallings, W. (2016). Cryptography and Network Security: Principles and Practice. Pearson Education.
6. Ferguson, N., & Schneier, B. (2003). Practical Cryptography. John Wiley & Sons.
© Алламурадова М.К., 2024
УДК 1082
Аммар Висам Альтахер
Технический университет Аль-Фурат Аль-Аусат, Технический колледж менеджмента, Кандидат технических наук - руководитель отдела информационных технологий
Вавилон - Ирак Абдулла Хасан Хусейн
Доктор философии, Университетский колледж Имама Аль-Кадхума, Ирак
Вавилон - Ирак
СРАВНИТЕЛЬНЫЙ АНАЛИЗ МАШИННОГО ОБУЧЕНИЯ И ТРАДИЦИОННЫХ МЕТОДОВ ОПТИМИЗАЦИИ ЗАПРОСОВ В СИСТЕМАХ УПРАВЛЕНИЯ БАЗАМИ ДАННЫХ С ПОМОЩЬЮ ТЕСТА АДАПТИВНОГО
МАТЕМАТИЧЕСКОГО МОДЕЛИРОВАНИЯ
Аннотация
Приведены результаты оценки состояния ...
Ключевые слова
оптимизация запросов, машинное обучение, системы управления базами данных, производительность, адаптивность, математическое моделирование
UDC 1082
Ammar Wisam Altaher
Al-Furat Al-Awsat Technical University, Technical Collage of Management, Phd - Head of information technology department
Babelon - Iraq Abdullah Hasan Hussein Phd-Imam Al-kadhum University College, Iraq
Babelon - Iraq
COMPARATIVE ANALYSIS OF MACHINE LEARNING AND TRADITIONAL QUERY OPTIMIZATION METHODS IN DATABASE MANAGEMENT SYSTEMS WITH ADAPTIVE MATHEMATICAL MODELING TEST
Annotation
The results of the state assessment are presented ...
Keywords
query optimization, machine learning, database management systems, performance, adaptability, mathematical modeling
Abstract
Query optimization is fundamental to the Productivity of information management systems (DBMS) affecting how queries are Round and Information is accessed. this report presents amp relative psychoanalysis of car acquisition (ml)-based question optimization methods and conventional optimization approaches incorporating possible Representations to instance the advantages and limitations of apiece wise. Through a detailed examination of Effectiveness Adjustability and mathematical Representations, the research reveals that ML-based approaches are very important in Improve Query Effectiveness and Adjustability Although they come with challenges related to Information requirements and system integration. This paper provides an in-depth comparison of traditional ML-based Question optimization methods focusing on Effectiveness and flexibility. The understandings from this read render general reason for the evolving landscape painting of question optimization in contemporary information
1. Introduction
Query optimization is an important aspect of Information base management which aims to reduce Question execution time by choosing the most efficient execution plan. conventional question Improvers typically employ trial-and-error and cost-based methods to reach this end. However, emerging Calculator learning (ML) techniques introduce new possibilities for more accurate and scalable Question optimization.
This paper provides an in-depth comparison of traditional ML-based Question optimization methods focusing on Effectiveness and flexibility. it includes possible examples to spotlight name differences and prospective advantages of apiece access discussing implications for real-world Uses and prospective developments in the Information direction.
2. Traditional Query Optimization Methods
Traditional Question optimization in a DBMS uses a combination of cost-based and rule-based methods to Produce control systems. these methods analyze the costs of disparate check options and take the last calculable costs.
2.1 Cost-Based Optimization
Cost-based optimization requires calculating the mathematical parameters necessary for the control systems and choosing the most efficient one. costs are normally deliberately founded along factors such as
arsenic Methodor sentence i/o employment and store employment.
2.2 Mathematical Model of Cost Estimation
The cost ( C ) of a query execution plan ( P ) can be represented as:
n
C(P) = i = ^Wi • Ri
¿=1
where:
- ( R_i ) represents the estimated resource usage for the ( i )-the component of the execution plan (e.g., CPU time, disk I/O).
- ( w_i ) is the weight associated with the ( i )-the resource.
The optimizer selects the plan ( P_opt ) with the minimum cost:
Popt = arg minC(P) PEP
where ( P ) is the set of all possible execution plans for a given query.
2.3 Rule-Based Optimization
Cost-based optimization involves calculating the mathematical parameters required for the control systems and selecting the most efficient one. costs are normally deliberately founded along factors such as arsenic Methodor sentence i/o employment and store employment.
2.4 Limitations of Traditional Methods
Traditional optimization methods often rely on static cost Representations and default rules which do not accurately capture today's Complicated Information or Adjust to changes in Information and Effectiveness This can lead to suboptimal Question plans and increased execution times.
3. Machine Learning-Based Query Optimization
ML-based Question optimization methods use Information-driven techniques to determine the most efficient Procedures based on historical Question Effectiveness Information. these techniques accommodate dynamic information and line characteristics provision further right and versatile Improvements.
3.1 Machine Learning Techniques
3.1.1 Supervised Learning
Supervised learning Representations are trained on labeled Information sets where each input Question is associated with an optimal execution plan. These representations are fit to call the be or operation of the unit execution depending along the stimulus characteristics inch head.
3.1.2 Reinforcement Learning
Reinforcement learning involves training an agent to optimize query plans by interacting with the environment and receiving feedback in the form of rewards. The agent learns to select execution plans that maximize the cumulative reward, which is typically related to query performance.
3.1.3 Deep Learning
Deep getting-to-know strategies mainly Nerve-related Webs can capture Complicated Layouts in question Effectiveness records enabling them to generalize higher throughout special sorts of queries and workloads.
3.2 Mathematical Model of ML-Based Query Optimization
The optimization process can be modeled as a learning problem where the goal is to minimize the expected cost of executing queries:
РЛ = argpmempEQ - D[C(P | Q)]
Where:
- ( Q ) represents a query sampled from the distribution ( D ).
- ( C(P|Q) ) is the cost of executing the query ( Q ) with the plan ( P ).
ML models aim to learn a function (f ) that maps queries ( Q ) to their optimal plans ( P ):
[P = f(Q)]
This function ( f ) is learned by minimizing the loss ( L ), which represents the difference between the predicted and actual costs:
E
L(f)= (Q p) „ D [(C(f(Q) | Q)-C(P | Q))2]
4. Comparative Analysis
4.1 Performance Comparison
4.1.1 Accuracy of Cost Estimation
Traditional techniques often use heuristic-primarily based rules that may not correctly estimate the value of complicated query execution plans. This can result in suboptimal performance, mainly for queries with difficult be part of conditions or involving huge datasets. ML-based totally strategies, with the aid of contrast, can leverage huge datasets and complicated algorithms to offer greater correct price estimations. This effects in better query performance and reduced execution instances. Studies have shown that ML-based totally optimizers can lessen question execution times through up to 30% in comparison to traditional strategies.
4.1.2 Execution Efficiency
ML-based Improvers can dynamically Adjust to modifications inside the Information base environment deciding on execution plans that limit useful Supply utilization and execution time. This adjustability is important in active environments where statistics and head Layouts often switch.
4.1.3 Performance Metrics:
To quantify the performance enhancements, do not forget the subsequent metrics
-Query Execution Time ((T_exec)): The time taken to execute a question. Lower execution instances suggest better overall performance.
-Resource Utilization (U): The amount of computational sources used to execute a question, which includes CPU and reminiscence. Lower utilization shows more efficient execution.
The improvement in execution time for an ML-primarily based optimizer over a traditional optimizer may be expressed as:
^Texec Texec,traditional Texec,ML
4.2 Adaptability Comparison
4.2.1 Handling Diverse Workloads
Traditional optimizers are often tailored to unique database configurations and workload patterns, which limits their adaptability to numerous or converting workloads. This can bring about inefficiencies in question execution whilst the workload modifications.
ML-based methods can be educated on diverse datasets and adapt to a huge range of question styles and data distributions, making them extra flexible in managing distinctive workloads.
4.2.2 Adaptation to Dynamic Environments
Traditional optimizers may additionally warfare to conform to dynamic environments where statistics and
query styles trade regularly. They rely upon predefined guidelines that won't seize the current state of the database.
ML-based totally techniques, with the aid of comparison, are mainly effective in dynamic environments. Techniques together with on line studying and switch studying permit ML models to constantly update their predictions primarily based on new facts, making sure that they stay effective even as situations change .
4.2.3 Adaptability Metrics:
To measure adaptability, consider the following metrics:
- Query Plan Stability (S_plan): The ability to maintain consistent performance across different workloads. Higher stability indicates better adaptability.
- Retraining Frequency (F_retrain): The frequency with which the optimization model needs to be retrained to maintain performance. Lower frequency indicates better adaptability.
The adaptability improvement can be expressed as:
^Splan Splan,ML Splan,traditional
5. Challenges and Considerations
5.1 Data Requirements
ML-based Question optimization requires large amounts of amazing schooling information to Construct accurate Representations. This information has to work instance of the current queries and workloads encountered in the Information base which get work tough to clear and keep.
5.2 Model Complexity
ML particularly deep learning models, can be complex and computationally extensive. This complexity can result in accelerated aid utilization and longer education instances, which should be balanced in opposition to the advantages of improved question optimization.
5.3 Integration Challenges
Integrating ML-based totally optimization strategies into current database structures can be technically difficult, in particular in phrases of making sure compatibility and preserving overall performance. Careful consideration should take delivery of to the effect of these techniques on machine architecture and useful resource usage.
6. Applications and Case Studies
6.1 Transactional Workloads
In environments with high transaction volumes ML-primarily based Improvers have been proven to provide extra Question execution plans lowering latency and improving throughput compared to standard methods.
6.2 Analytical Workloads
For Complicated search queries, ML-based Improvers can determine optimal execution strategies that very importantly reduce Question Methoding time. these methods are specifically important in the bearing of great information sets where conventional methods get fought to deal with the Complicatedness and book of information.
6.3 Hybrid Workloads
ML-based optimization strategies also are powerful in hybrid environments that involve transactional and analytical workloads. away acquiring to real operation facts these techniques get accommodate to the alone requirements of disparate head sorts provision park Effectiveness plans for an associate in a nursing comprehensive run of queries.
7. Future Directions
7.1 Advanced ML Techniques
Future studies ought to discover superior ML techniques such as ensemble getting-to-know and deep reinforcement getting-to-know to similarly decorate the Precision and Productivity of question optimization These techniques have the ability to offer even greater sophisticated fashions that may take care of complex and dynamic workloads greater successfully.
7.2 Improved Data Collection
Developing methods for producing exceptional education records for numerous and Complicated workloads is vital for the achievement of ML-based Question optimization.
7.3 Integration with Modern Database Systems
Integrating ML-primarily based optimization techniques with contemporary database structures requires cautious consideration of gadget structure and overall performance. Future research must look at tactics for seamlessly integrating those techniques into existing structures even as retaining compatibility and overall performance.
8. Conclusion
learning-based Question optimization methods offer significant Improvements over traditional approaches in terms of Effectiveness and Adjustability. they render further right-be estimations better Effectiveness Productivity and greater Adjustability to different and active workloads. However, the application of these methods requires careful consideration of Information requirements Representation Complicatedity and integration challenges. arsenic the area of car acquisition continues to rise it is potential that ml-based question optimization leaves run associate in nursing progressively important role in enhancing the Productivity and Adjustability of Information base direction systems. References
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© Аммар Висам Альтахер , Абдулла Хасан Хусейн, 2024
УДК 51-74
Базарова Э.Б.
Преподаватель кафедры прикладной математики и информатики Туркменский государственный университет имени Махтумкули
Ашхабад, Туркменистан Гараев Д.
Преподаватель кафедры Математики и методики её преподавания Туркменский государственный педагогический институт имени Сеидназара Сейди
Туркменабад, Туркменистан)
МАТЕМАТИЧЕСКИЕ МЕТОДЫ В ГРАФИЧЕСКИХ ПРОЕКТАХ
Аннотация
В статье рассматриваются основные математические методы, применяемые в графических проектах, такие как линейная алгебра, теория графов, теория чисел, численные методы, анализ данных и геометрия. Обсуждаются их применение и влияние на создание реалистичных и интерактивных изображений. Приведены примеры использования математических методов в различных аспектах компьютерной графики, а также их роль в повышении качества и безопасности графических проектов.
Ключевые слова
математические методы, графические проекты, линейная алгебра, теория графов, теория чисел,
численные методы, анализ данных, геометрия, компьютерная графика, визуализация данных
Введение
Современные графические проекты требуют применения сложных математических методов для создания реалистичных и интерактивных изображений. От компьютерной графики и анимации до обработки изображений и визуализации данных - математика играет ключевую роль в различных аспектах разработки графических проектов. В данной статье рассматриваются основные математические методы, используемые в графических проектах, их преимущества и примеры применения.
Основные математические методы
1. Линейная алгебра
