High Performance SQL

Myalapalli, Vamsi Krishna; Savarapu, Pradeep Raj

doi:10.1109/indicon.2014.7030467

Cited by 15 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, it was the task of the developers to ensure the availability and security of these files, as well as the monitoring of changes, versions, and accessibility in case of moving to another repository, disc, etc. [27], [35], [36].…”

Section: Figure 5 Version Codesmentioning

confidence: 99%

Treating Temporal Function References in Relational Database Management System

Kvet,

Papan,

Durneková

2024

IEEE Access

View full text Add to dashboard Cite

Databases form an inseparable part of the information systems. They hold the data formed by the data models. Data are operated by the database management system, which takes an interlayer between the data and the external environment. They can be purely obtained from the database using the Select statement or can act as the inputs for the function calls. Data can be temporally or positionally oriented, allowing the storage of multiple tuple states for one object. Temporal databases can be defined by various precision frames and granularities, from object level through synchronization groups up to the est attribute granularity. Inspired by temporal databases, we provide new solutions for managing functions, and code can also evolve over time. Namely, individual parameters, properties, and method calculations may change over time. It is not only about internal computation and performance optimization but also about ways of processing and calculation. Thus, the function is not deterministic across the time axis. Existing solutions are primarily based on versioning considering the function name, which, however, takes significant risk related to the reference correctness as it depends on the function name. Precisely, there is no checking of the validity referencing specific function versions. The proposed solution aims to prepare an automated, robust transformation and mapping solution that emphasizes individual versions of the functions and calls relevant functions based on the referenced time frames. Thanks to the provided reliable transformation layer, multiple function versions can exist, respecting the valid version of the function that existed at the time of validity of the relevant tuple. The function is always called by the original name, and a particular version is automatically processed based on the input data and referred time frame. In the performance evaluation process, we focus on three stages -pre-processing phase (parsing, checking, and loading), version identification, and loading. Multiple solutions were created and discussed. The best results were provided by model 4 based on the transformation module in the temporal layer, for which the total cost of processing within the Select command, compared to the reference solution, was reduced by 68.40%. The most significant saving was detected in parsing and checking since the function versions are directly available for the reference calls (72.96%). Version identification is done by the optimized searching B+tree index tree, reducing the costs by up to 69.42%. The proposed solution is based on managing multi-versioning by propagating new clauses, commands, and background processes, managing the whole system autonomously. Furthermore, performance optimizations can later enhance individual versions, like more sophisticated data structures. Thus, each version can have multiple implementations. The additional demands on data storage and structure management are less than 1% globally. INDEX TERMS temporal databases, function versioning, dynamic function versi...

show abstract

Section: Figure 5 Version Codesmentioning

confidence: 99%

Treating Temporal Function References in Relational Database Management System

Kvet,

Papan,

Durneková

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…SQL plays a vital role in managing relational data because of its higher performance and support of unlimited functions to deal with relational aspects. SQL supports many applications in many fields like web applications and information discovery applications (5,6).The combination of the features of R programming and SQL opens up new horizons in the field of combining and analysing data. This environment provides unlimited flexibility and tools to treat the data according to the desired criteria.…”

Section: Introductionmentioning

confidence: 99%

An Optimised Method for Fetching and Transforming Survey Data based on SQL and R Programming Language

Hasan¹

2019

Baghdad Sci.J

View full text Add to dashboard Cite

The development of information systems in recent years has contributed to various methods of gathering information to evaluate IS performance. The most common approach used to collect information is called the survey system. This method, however, suffers one major drawback. The decision makers consume considerable time to transform data from survey sheets to analytical programs. As such, this paper proposes a method called 'survey algorithm based on R programming language' or SABR, for data transformation from the survey sheets inside R environments by treating the arrangement of data as a relational format. R and Relational data format provide excellent opportunity to manage and analyse the accumulated data. Moreover, a survey system based on structured query language and R programming language is designed to optimize methods to manage survey systems by applying large features offered via combining multi data science languages. The experiments verified enhancements of flexibility, technical tools, and data visualization features employed to process the collected data from different aspects; therefore, the proposed approach demonstrates a simple case study to enhance the evaluation requirements of the proposed technique. Finally, the estimated results of this research can be used to improve the methods of information management on different aspects such as survey systems and other data models that hold the relational and non-relational models using SABR. This method demonstrated improved accuracy of data collected, reduced data processing time and arranged data to the willing model.

show abstract

Wait Event Tuning in Database Engine

Myalapalli

2016

Advances in Computing Applications

View full text Add to dashboard Cite

High Performance SQL

Cited by 15 publications

References 1 publication

Treating Temporal Function References in Relational Database Management System

Treating Temporal Function References in Relational Database Management System

An Optimised Method for Fetching and Transforming Survey Data based on SQL and R Programming Language

Wait Event Tuning in Database Engine

Contact Info

Product

Resources

About