Data Mining on Smartphones: An Introduction and Survey (original) (raw)
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DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets
Advanced Data Mining and Applications, 2019
Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data mining and knowledge discovery tools for mobile devices are surprisingly rare. DataLearner is a new data mining application designed specifically for Android devices that imports the Weka data mining engine and augments it with algorithms developed by Charles Sturt University. Moreover, DataLearner can be expanded with additional algorithms. Combined, DataLearner delivers 40 classification, clustering and association rule mining algorithms for model training and evaluation without need for cloud computing resources or network connectivity. It provides the same classification accuracy as PCs and laptops, while doing so with acceptable processing speed and consuming negligible battery life. With its ability to provide easy-to-use data mining on a phone-size screen, DataLearner is a new portable, selfcontained data mining tool for remote, personalised and educational applications alike. DataLearner features four elementsthis paper, the app available on Google Play, the GPL3-licensed source code on GitHub and a short video on YouTube.
Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments
2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010
Pocket Data Mining PDM is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.
IJERT-An Overview on Mobile Data Mining
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/an-overview-on-mobile-data-mining https://www.ijert.org/research/an-overview-on-mobile-data-mining-IJERTV3IS090857.pdf During initial days, mobiles were only used as a medium for communication. This view of mobile phones as communication devices changed with the advent of a new category of mobile devices known as smart phones. These smart phones along with communication are capable of doing things that a computer can do. With the advancement of technology, these smart phones are becoming more and more powerful in terms of storage and computing features. The smart phone can provide us with data about various aspects such as movement of the user, behavior of the user, communication, etc. Today, since, most of the communication takes place through mobiles, it is very important for the analyists to analyze the data generated by mobile phones. A large amount of data is generated by mobile phones as the use of mobile phones is increasing continuously. In this paper, an overview of mobile data mining is provided and its techniques and applications are discussed.
Mobile data mining on small devices through web services
2010
Analysis of data is a complex process that often involves remote resources (computers, software, databases, files, etc.) and people (analysts, professionals, end users). Recently, distributed data mining techniques are used to analyze dispersed data sets. An advancement in this research area comes from the use of mobile computing technology for supporting new data analysis techniques and new ways to discover knowledge from every place in which people operate.
Mining Personal Data Using Smartphones and Wearable Devices: A Survey
Sensors, 2015
The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.
Mefail Tahiri, Ejup Rustemi: The two-way impact between data mining and mobile technologies
Mobile devices have changed almost everything in the way how we communicate, work and deal with data. In some way they have facilitated businesses around the world, but in many ways they have produced a vast amount of data that most of the time are very complex and difficult to deal with. Data mining is a valuable tool in dealing with this issue. This paper will try to explain the relationship between data mining and mobile devices and technologies and the possibilities that they offer to improve the way we operate in small or large enterprises, schools, etc.
Mobile Mind: A Fully Mobile Platform Based Machine Learning Application
In recent years, mobile devices have developed significantly in terms of technical capabilities, computing power, storagecapacity and ability of sensing different activities via intelligent built-in sensors. In this perspective, capabilities of ultimate mobile phone technology have begun to make mobile systems to be a candidate novel platform for machinelearning and data mining activities. In this study, a fully mobile platform based machine learning application namedMobile Mind is designed and implemented. While, all other current mobile platform based machine learning and datamining applications are using central data mining servers to perform analysis, Mobile Mind does all tasks on cell phone’sprocessor and memory. On the other hand, Mobile Mind currently supports support vector regression and kernelrecursive least squares regression algorithms with polynomial and radial basis kernels to allow users performingpredictive data mining operations on flat CSV (comma separated values) files. By this study, it is shown that mobileplatforms are becoming native and ubiquitous platforms for machine learning purposes from now on. Therefore, the needfor central data mining servers and web service usage for data transferring will started to be less and less in the future.Furthermore, a native fully mobile machine learning tool presents unlimited opportunities to the mobile applicationprogrammers. Especially dealing with sensor data driven applications has much potential in this point of view
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014
Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1-3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts.
The mobile data challenge: Big data for mobile computing research
2012
This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies. First we review the Lausanne Data Collection Campaign (LDCC) -an initiative to collect unique, longitudinal smartphone data set for the basis of the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; and discuss some of the key aspects in order to generate privacy-respecting, challenging, and scientifically relevant mobile data resources for wider use of the research community. The concluding remarks will summarize the paper.
Distributed Classification for Pocket Data Mining
Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems, Warsaw, Springer, 2011
Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can sub-scribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. The concept has shown to be feasible in an earlier study using technological enablers of mobile software agents. Mobile agents roam the network to discover relevant data streams, and then (mobile) agents that encapsulate relevant data mining technology to derive a decision or result collaboratively from various agents. In this paper, we propose the use of distributed Hoe?ding trees and Naive Bayes classi?ers in the PDM framework. The choice of these techniques is the trade o? between accuracy of the former technique and resource e?ciency of the later. An extensive experimental study is reported showing the efficiency of the collaborative data mining with the two classifiers.