Affiliation:
1. Charles Sturt University, Bathurst, NSW, Australia
Abstract
Data mining is the science of extracting information or “knowledge” from data. It is a task commonly executed on cloud computing resources, personal computers and laptops. However, what about smartphones? Despite the fact that these ubiquitous mobile devices now offer levels of hardware and performance approaching that of laptops, locally executed model-training using data mining methods on smartphones is still notably rare. On-device model-training offers a number of advantages. It largely mitigates issues of data security and privacy, since no data is required to leave the device. It also ensures a self-contained, fully portable data mining solution requiring no cloud computing or network resources and able to operate in any location. In this article, we focus on the intersection of smartphones and data mining. We investigate the growth in smartphone performance, survey smartphone usage models in previous research, and look at recent developments in locally executed data mining on smartphones.
Funder
Australian Government Research Training Program
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Reference210 articles.
1. Dan Ackerman. 2007. Apple MacBook 2007 Model review. Retrieved from https://www.cnet.com/reviews/apple-macbook-2007-model-review/.
2. Forest PA : Constructing a decision forest by penalizing attributes used in previous trees
3. Clock rate versus IPC
4. Charu C. Aggarwal. 2015. Data Mining: The Textbook. Springer.
5. Rakesh Agrawal, Ramakrishnan Srikant, et al. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), Vol. 1215. Citeseer, 487–499.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献