Cascading Cache Layer in Content Management System
DOI:
https://doi.org/10.18034/abr.v8i3.542Keywords:
Cache, Cascading, Layers, Memory, Storage, Buffers, AlgorithmAbstract
Caching involves the temporal storing of data in a separate folder. Cascading is the arrangement of something in sequence from top to bottom. Cascading cache layer in content management system places data in layers and sequence in order of importance. The cached data are also removed based on their order of importance. Caching is majorly about input and output of content and data, this brings the need for cascading management system to make accessing data easier than usual. This work takes a look into caching and how it works. It considers various levels of caching in the content management systems. It tries to explain what cascading is in a content management system as well as its importance. This work explains how cascading cache in layers would make it faster and more efficient to access data.
Downloads
References
Ganapathy, A. (2015). AI Fitness Checks, Maintenance and Monitoring on Systems Managing Content & Data: A Study on CMS World. Malaysian Journal of Medical and Biological Research, 2(2), 113-118. https://doi.org/10.18034/mjmbr.v2i2.553
Ganapathy, A. (2016). Speech Emotion Recognition Using Deep Learning Techniques. ABC Journal of Advanced Research, 5(2), 113-122. https://doi.org/10.18034/abcjar.v5i2.550
Ganapathy, A. (2017). Friendly URLs in the CMS and Power of Global Ranking with Crawlers with Added Security. Engineering International, 5(2), 87-96. https://doi.org/10.18034/ei.v5i2.541
Neogy, T. K., & Paruchuri, H. (2014). Machine Learning as a New Search Engine Interface: An Overview. Engineering International, 2(2), 103-112. https://doi.org/10.18034/ei.v2i2.539
Paruchuri, H. (2015). Application of Artificial Neural Network to ANPR: An Overview. ABC Journal of Advanced Research, 4(2), 143-152. https://doi.org/10.18034/abcjar.v4i2.549
Paruchuri, H. (2017). Credit Card Fraud Detection using Machine Learning: A Systematic Literature Review. ABC Journal of Advanced Research, 6(2), 113-120. https://doi.org/10.18034/abcjar.v6i2.547
Vadlamudi, S. (2015). Enabling Trustworthiness in Artificial Intelligence - A Detailed Discussion. Engineering International, 3(2), 105-114. https://doi.org/10.18034/ei.v3i2.519
Vadlamudi, S. (2016). What Impact does Internet of Things have on Project Management in Project based Firms?. Asian Business Review, 6(3), 179-186. https://doi.org/10.18034/abr.v6i3.520
Vadlamudi, S. (2017). Stock Market Prediction using Machine Learning: A Systematic Literature Review. American Journal of Trade and Policy, 4(3), 123-128. https://doi.org/10.18034/ajtp.v4i3.521
Vadlamudi, S. (2018). Agri-Food System and Artificial Intelligence: Reconsidering Imperishability. Asian Journal of Applied Science and Engineering, 7(1), 33-42. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1192
--0--
Downloads
Published
Issue
Section
License
Copyright (c) 2018 Apoorva Ganapathy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Asian Business Review is an Open Access journal. Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a CC BY-NC 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of their work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal. We require authors to inform us of any instances of re-publication.
How to Cite
