Data Mining on Smartphones: An Introduction and Survey

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 paper, 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.

On-device query intent prediction with lightweight LLMs to support ubiquitous conversations

“…The main reason for choosing Ernie is that it is publicly available and contains state-of-the-art lightweight LLMs suitable for on-device deployment. According to a recent survey 78 , the RAM capacity of low-end (< $ 150 ) to mid-end (< $ 550) mobile devices falls in the range between 3 and 8 Gb. Considering that most of the RAM will be occupied by background services and other running apps, we set 2 Gb as the upper-bound for LLM size so that it can be deployed on commodity mobile devices.…”

Improving Delivery Probability in Mobile Opportunistic Networks with Social-Based Routing

“…MONs [4,5] are a variant of delay tolerant networks (DTNs) [5] focused on providing communication in networks with an unstable dynamic topology shaped by mobile devices. The emerging works in computation have substantiated this discipline [6] and have enabled the viability of new disruptive applications for smartphones and wearables [7,8]. Moreover, these devices have been strongly sustained by the strides in energy consumption [9].…”

Secure Food-Allergen Determination by Combining Smartphone-Based Raw Image Analyses and Liquid Chromatography–Mass Spectrometry for the Quantification of Proteins Contained in Lateral Flow Assays

“…Moreover, ratiometric corrections for ambient light variation may be more efficient for this approach due to the limited processing performed. Data analyses of these larger files may be cumbersome on older phones but is not expected to be inhibiting on novel models since processing speed of novel smartphones is approachingand on occasions, exceedingthe speed of current-generation laptops …”

“…Data analyses of these larger files may be cumbersome on older phones but is not expected to be inhibiting on novel models since processing speed of novel smartphones is approaching� and on occasions, exceeding�the speed of current-generation laptops. 22 The present study evaluates the opportunity of using DNG smartphone images and ratiometric ambient light correction for color quantification of LFAs via a smartphone followed by subsequent LC−MS analyses directly on extracts obtained from the LFAs. The performance of RGB color channels and gray scale from JPEG images were compared with raw intensity values from DNG images under various lighting conditions.…”

“…Data analyses of these larger files may be cumbersome on older phones but is not expected to be inhibiting on novel models since processing speed of novel smartphones is approaching—and on occasions, exceeding—the speed of current-generation laptops. 22 …”