5 research outputs found
Elevating database performance: current caching and prefetching strategies for online databases in Nigeria
This study investigated caching and prefetching techniques to improve data access performance in online databases, considering factors like data popularity, access patterns, and cache coherence. The research design adopted for this
study was the descriptive survey. The population comprised of 1387 undergraduates computer science students in public tertiary institutions in Ekiti State. Simple random sampling technique was adopted to select 150 computer science students from three public tertiary institutions in the study area. The instrument used for data collection was a structured 4 Likert type questionnaire. The questionnaire was distributed to the respondents to find out the effectiveness of caching and prefetching techniques on online database. The instrument was both face and content validated by two experts from department of computer science in Bamidele Olumilua University of Education Science and Technology, Ikere-Ekiti, Ekiti State. The reliability of the instrument was ensured using Pearson Product Moment Correlation formula which yielded a coefficient of 0.97. The data collected were analyzed using descriptive statistics such as mean and standard deviation. The result showed that the current caching and prefetching techniques employed in online databases are highly effective; the different access patterns have effect on the effectiveness of caching and prefetching techniques in online databases and there are impacts of
cache coherence mechanisms on the efficiency of caching and prefetching techniques in online databases. It was therefore recommended that the inclusion of caching and prefetching in curriculum is important across all educational level in Nigeria. In addition, caching and perfecting has come under fire for focusing mostly on computer science
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Advanced Prefetching and Caching of Models with PrefetchML
International audienceCaching and prefetching techniques have been used for decades in database engines and file systems to improve the performance of I/O intensive application. A prefetching algorithm typically benefits from the system's latencies by loading into main memory elements that will be needed in the future, speeding-up data access. While these solutions can bring a significant improvement in terms of execution time, prefetching rules are often defined at the data-level, making them hard to understand, maintain, and optimize. In addition, low-level prefetching and caching components are difficult to align with scalable model persistence frameworks because they are unaware of potential optimizations relying on the analysis of metamodel-level information, and are less present in NoSQL databases, a common solution to store large models. To overcome this situation we propose PrefetchML, a framework that executes prefetching and caching strategies over models. Our solution embeds a DSL to configure precisely the prefetching rules to follow, and a monitoring component providing insights on how the prefetch-ing execution is working to help designers optimize his performance plans. Our experiments show that PrefetchML is a suitable solution to improve query execution time on top of scalable model persistence frameworks. Tool support is fully available online as an open-source Eclipse plugin