Sample HI5033 Database Design Report

HI5033 Database Design Report Sample

Assignment Description

This assessment requires individual completion. Students are expected to complete a critique and conduct a literature review to discuss a contemporary issue relating to Database Systems and identify appropriate approaches to address this issue. The topic is “Evaluating the Impact of Indexing Techniques on Query Performance in Relational Databases”.

Each student is required to search the literature and find a minimum of ten (10) academic research papers (references) related to this topic. Subsequently, the student must critically analyse the selected references and provide an in-depth discussion on how they reflect the topic. This assessment is worth 40% of the unit’s grade and is a major assessment. Students are advised to begin working on this assessment as soon as possible.

Deliverable Description

You need to submit the final version of your assignment in Week 12. The structure of the final report should include a cover page and 5 sections as follows:

1. Introduction

State the purpose and objectives of the report, present an overview of the topic, and outline the scope of your analysis.

2. Literature Review

Summarize key findings from at least ten academic papers, highlighting relevant theories, methodologies, and results. Identify major trends, gaps, and areas of consensus or disagreement within the literature.

3. Discussion

Critically analyse the references reviewed in the literature review. Discuss how these sources contribute to the understanding of database optimisation, examining their relevance, strengths, and limitations.

4. Conclusion

Summarize your findings, emphasizing key insights from the literature review and discussion.

5. References

Provide a comprehensive list of all references cited in your report, formatted according to the Adapted Harvard Referencing style.

Solution

Introduction

The purpose of the report is to understand the perceptions in the existing literature related to the impact of the indexing technique and query performance in a relational. The main objective of this review is to understand the concept in detail and to know the implications of different aspects, techniques, models, and theories related to indexing and query that are associated with database performance and advancement. Indexing technique and query performance are a significant part of relation and database because they choose the effectiveness and feasibility of different databases for the Assignment Help in responding to challenges and advancement areas. In this respect, the nature of changes in indexing and query performance shows the evolution of databases. The scope of the analysis of literature will show different areas of query management as well as indexing techniques that are focused on the database application as well as developed by different researchers as well as real-time implementations. The scope is also to understand those areas that create advancement in the different databases.

Literature Review

Gadde (2021) opines that artificial intelligence helps reduce downtime and increase the accuracy of relational databases. The discussion shows an investigation of the understanding of leveraging artificial intelligence for improvement and enhancement of efficiency by minimising down time in relational database systems. It is seen that alignment of both traditional and modern proactive theoretical solutions of maintenance seems effective in providing the result. Historical data were reviewed on certain metrics, leading to the understanding that a random forest-based predictive model accuracy in maintenance and performance. It is there for the fact that the AI-driven predictive model as a trend enhances the database management system is gaining momentum.

Ali et al. (2022) argue that techniques like compact data structure and multi-query Optimisation are seen to be effective in enhancing data quality performance within the system. That discussion shows a review of techniques and systems that help contribute to the RDF query graph. The focus is on identifying the growth of RDF in terms of graph-based models that provide a theoretical understanding of challenges and KPIs. In terms of gaining efficiency graph partitioning in distributed systems shows more accuracy than local systems in scalability. The discussion is based on reviewing and examining the results of 135 RDF/SPARQL engines and 12 benchmarks. The discussion shows Limited knowledge about scalability in RDF and multi-query Optimisation as a concept.

Han et al. (2021) highlight that ML-based methods help to enhance the performance of the traditional query execution process. The discussion creates an understanding of the role of different estimation cardinality estimation methods to improve the functioning of the Quadri Optimisation in relational databases. The research focuses on understanding a model with respect to theoretical approaches of DBMS that helps in comparing traditional methods with modern machine learning-based approaches. The study shows different new aspects in the machine learning data driven method like P-error instead of E error that helps in improving the functionalities and feasibility of a system. The methods include the establishment of new benchmarks for real-world databases and the evaluation of multiple cardinality estimation methods. The finding shows that machine learning will create a modernised trend of shifting from traditional to modern query Optimisation models. There is inadequate information about the concept of the real world in this context.

Kossmann, Papenbrock, and Naumann (2022) view that dependence on data improves query optimisation. The discussion provides results of the survey in which different types of data-dependent processes are reviewed in query Optimisation in a relational database system. Rule-based and cost-based Optimisation approaches are considered for the review of the model in terms of theoretical understanding. Findings show that refinement of the execution plan, computation reduction and enhancement of indexing strategies showed improvement in enquiry Optimisation who data dependencies. A review of the survey is adopted as a methodology to gain insights. Trends indicate that there is increased research on the advanced metadata for query Optimisation. The low implementation rate of data-dependent processes in real practice limits knowledge and focus in the discussion.

Hosen et al. (2024) highlight that a new model or query language can enhance the abilities of the query. The discussion focuses on creating a new query language and a model for temporal graph data to understand its effectiveness in creating efficiency. Graph database principles are considered to identify the utilisation and features of the model. The finding shows that the model is capable of increasing the feasibility and efficiency of both real-world and synthetic data sets. The discussion indicates a significant growing interest in temporal data graphs and progressive query language. There is limited historical data about the temporal paragraph in the existing query model that can be compared through the discussion.

Pan, Wang, & Li (2024) argue that vector data management shows challenges in indexing and hybrid query execution. The discussion shows in-depth research on vector database management systems to outline its key challenges and future scope in research and techniques. Theoretical concepts like embedding-based retrieval, data management principles and similarities search are focused on and considered for gaining relevant insights. The finding highlights different categories and emerging techniques of vector database management systems including all its visible challenges. Models with unstructured data or large databases are growing rapidly facilitating the need for an advanced approach of vector database management in the coming time. Gaps in studies of hybrid query or Optimisation techniques are found within the discussion.

Wang et al. (2021) opine that Milvus is a vector-based data management system capable of managing high-dimensional data. Discussion highlights a specific with the database management model to show its competence and effectiveness in responding to high dimensional data of Artificial Intelligence and databased applications. Theoretical attributes and principles of system architecture have been focused on understanding the need for this model in data query management. The results indicate that Milvus is looking to outperform an existing database system by offering advanced functionalities. An extensive benchmark is set for evaluation of the specific vector database management system to compare it with multiple other database systems. Future trend shows increasing use of Artificial Intelligence and data science facilitates the need for a high performance vector database management system. Limitations of studies are seen in understanding hybrid data query processing.

Liu et al. (2021) view that error reduction and logical operation show effective query execution on knowledge graphs. The purpose of the discussion is to identify ways through which the execution in the knowledge graph can be enhanced. The mention of neutral embedding-based methods is done to identify gaps in the traditional approaches. The finding reveals that the proposed method is seen to enhance accuracy and efficiency in the query management of a knowledge graph. The methodology involves proposing a modern that can address embedding entities and queries in a vector space. The trend highlighted is a shift from rule based model to deep learning approaches. Limitation of discussion is seen in identifying the management of complex queries associated with various nodes.

Trummer et al. (2021) point out that the neural approach improves performances in various software analysis applications. The discussion sheds light on neural software analysis, in which approaches are exploded to enhance the efficiency and performance of systems. Finding show that neural approaches are effective in creating systematic functioning of analysis software Complex code patterns leading to enhancement of the existing process. The methods involve conducting training on the neural networks on large codebases. A major trend is seen in the adoption of machine learning in software engineering for code analysis. Gaps in the study are found in understanding the consequences of integrating modern neural analysis with traditional ones in terms of efficiency and flexibility.

Wang et al. (2022) opine that V-chain+ is an effective model that improves query performance more than existing systems. The study aims to explore the Boolean range of queries and their effectiveness in the blockchain database. Findings show that the proposed model in the research has significantly addressed mitigating gaps in the existing system and increasing efficiency enquiry performance within the database. A sliding window accumulator and an objective registration index are designed as part of the method to conduct this research. The trend shows that there is increasing integration of index techniques with the blockchain for data retrieval. Gaps of knowledge are seen in inefficiency and prayer solutions to the challenges used to Complex query management.

Khan et al. (2023) point out that a NoSQL database is better than SQL in managing large volumes of unstructured data. The discussion focuses on a comparative assessment of two types of data: NoSQL and SQL. The outcome shows that NoSQL by Scheme with less structure and horizontal scalability, responds effectively and better than SQL databases to manage Complex data. The methodology adopted for this purpose is a systematic review of literature With a definite number of sample data. The growing trend is seen in the adoption of NoSQL databases for big data applications and Systems. The study shows a gap in providing effective solutions to issues like interoperability and data portability.

Cao et al. (2021) state that a native cloud-based database is capable of quick responses to adverse situations or failures. The study aims to explore in native cloud-based database in terms of its application for aggravated data centres to improve efficiency, availability and performance. Resource disaggregation theory provides a strong theoretical framework to identify aspects related to the New database designing. The finding suggests that the specific database is helpful in failure recovery faster in practice. Methods like optimistic locking, index-aware prefetching, etc, are used for creating the design of the proposed model. Trends show growing interest in serverless and aggravated databases to respond to scalability and cost-effectiveness. An understanding of the scalability gap is found in studies within the traditional and existing databases.

Fotache et al. (2023) opine that data masking has minimal impact on query improvement. The discussion explored the role of performance penalty in dynamic data masking in SQL databases focusing on Oracle. Privacy by design model and security theories are considered to identify different principles and aspects related to data masking in SQL databases. The results show that dynamic determine asking has a weak influence on query management in an SQL database. Methods include query analysis, machine learning models, etc. The future growing interest in the alignment of security and compliance in databases holds a significant part in the discussion. The discussion provides less focus on the Cost performance of dynamic data masking in Real-world scenarios.

Panda, Dash & Jena, (2021) highlights that Particle Swarm Optimisation increases efficiency of the query execution. The purpose of the study is to optimise the query response time of blockchain by applying evolutionary algorithms. Blockchain indexing, cryptographic security theories helps to understand the framework more dominantly and significantly. Finding show that PSO reduce computational costs within systems. The adoption of artificial intelligence in the optimisation of blockchain databases is a major trend. The limitations of the study are seen in identifying the role of artificial intelligence in the centralised system in terms of query response management.

Wu & Cong (2021) state that deep autoregressive models provide better accuracy and improve cardinality estimation in databases. The discussion provides a new proposed model for improving estimation cardinality in a database. Gumbel Software trick and deep learning techniques provide a theoretical framework for understanding the requirement of the proposed database. The results show a reduction in error and an improvement in efficiency in workload shifts. The study is done using a hybrid training model. Trends in the discussion is seen in growing AI Adoption in cardinality estimation within a database. The gap is seen in identifying further Optimisation techniques.

Discussion

Considering the literature review, one of the major aspects that is considered as a strength focus on advancement related to query execution or management is the adoption of artificial intelligence. In most of the review of discussion artificial intelligence is found to be effective in handsome database system by reduction of error down time and and enhancement of scalability efficiency and performance. Artificial intelligence enhances the performance of a query in a database system proficiently (Panda et al., 2021). In terms of understanding future scope, it is also seen that the growing interest and adoption of artificial intelligence is the need for more advanced and data-driven models for queries and indexing. This growing interest also has led to different new model proposals, as seen in the existing literature. The second positive aspect that comes out of the literature review is the focus on creating models that reflect the alignment of both the traditional and modern forms, focusing on that theoretical foundation and creating a progressive approach for relational database query management. Deep learning-based models are seen to Breach the gap between data-driven and query-driven approaches. It simply means that all of the focus in proposing new models and techniques identifies strengths of the traditional approaches and modern technology in terms of mitigating the gaps and challenges. This shows are very potential nature of Advanced indexing techniques as well as query management in relational databases.

Major weakness or gaps seen in the existing model is limitations of knowledge related to advanced Optimisation or Technology implementation for making a traditional database efficient. Specific models are seen to be proposed for mitigating only specifications like blockchain. There is a Limited number of knowledge in identifying a specific solution that can address multiple databases and their challenges. In this respect, there is a need for more future research on areas right real-time analysis of computational cost neural network, etc, to understand the appearance of relational database query management in the coming time. Despite growing demand, the discussion shows certain limitations of AI in indexing and query management. There is less focus on real-time query Optimisation, which reflects the scalability of a database. It is to be noted that artificial intelligence means more explanation in terms of application to real-world scenarios. Database complexities are also growing with time and creating different unrecognisable patterns of data constructed data that create obstacles for the user to perform efficiently. In this respect, future research on AI applications in the area of query analysis and indexing needs to be focused more on real business case studies.

The sources used for the literature review collectively show that database Optimisation is a significant part of adjusting for future challenges and opportunities. Performance enhancement is the primary purpose for this Optimisation, for which integration with artificial intelligence hybrid or cloud-based approaches and security management needs to be focused and prioritised.

Conclusion

To conclude, it can be said that query Optimisation is one of the significant aspects that continuously changes with the advancement of Technology and the changing nature of the different database systems. So, most of the articles provide an understanding of square Optimisation and different models and techniques that are relevant to creating a positive impact on performance. It is also to be noted that the discussion shows an inclination to our artificial intelligence as one of the potential technology that addresses the need for database indexing and query management over a long time. That discussion highlights different types of proposed models and theoretical approaches that enhance the dimension of knowledge related to the current implications of relational databases as well as query performance and indexing approaches. This is significant in identifying the potential of the database development areas.

References