Homogeneous and heterogeneous distributed classification for pocket data mining (original) (raw)

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.

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.

Distributed hoeffding trees for pocket data mining

2011 International Conference on High Performance Computing & Simulation, 2011

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Data Mining on Smartphones: An Introduction and Survey

ACM Computing Surveys

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...

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.

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.

Context-aware collaborative data stream mining in ubiquitous devices

2011

Recent advances in ubiquitous devices open an opportunity to apply new data stream mining techniques to support intelligent decision making in the next generation of ubiquitous applications. This paper motivates and describes a novel Context-aware Collaborative data stream mining system CC-Stream that allows intelligent mining and classification of time-changing data streams on-board ubiquitous devices. CC-Stream explores the knowledge available in other ubiquitous devices to improve local classification accuracy. Such knowledge is associated with context information that captures the system state for a particular underlying concept. CC-Stream uses an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the instance space and their context similarity in relation to the current context.

Collaborative Data Stream Mining in Ubiquitous Environments Using Dynamic Classifier Selection

International Journal of Information Technology & Decision Making, 2013

In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model ...

Data stream mining in ubiquitous environments: state-of-the-art and current directions

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2014

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Exploiting Need of Heterogeneous Data Mining Environment Based on Mobile Computing Environments

International Journal of Computer Applications, 2010

Today the concept of Data Mining services is not alone sufficient. Data mining services play an important role in the field of Communication industry. Data mining is also called knowledge discovery in several database including mobile databases and for heterogeneous environment. In this paper, we discuss and analyze the consumptive behavior based on data mining technology. We discuss and analyze different aspects of data mining techniques and their behavior in mobile devices. We also analyze the better method or rule of data mining services which is more suitable for mobile devices. In this paper, we survey several aspects of open service framework based on grid structure which provides the heterogeneous environment for data mining on mobile computing environments.