Open Mobile Miner: A Toolkit for Building Situation-Aware Data Mining Applications

Haghighi, Pari Delir; Krishnaswamy, Shonali; Zaslavsky, Arkady; Gaber, Mohamed Medhat; Sinha, Abhijat; Gillick, Brett

doi:10.1080/10919392.2013.807713

Cited by 27 publications

(15 citation statements)

References 24 publications

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“…In addition to such tools, there are studies aiming at sources such as decision making, battery, bandwidth, storage in literature. It supports aware approaches with its hybrid design which is equipped with a tool called Mobile miner suitable for mobile devices [6].A based software framework has also been proposed for managing and analyzing large data on .However, some restrictions on mobile devices prevent their use for such purposes.…”

Section: IImentioning

confidence: 99%

Examining the Status of Big Healthcare Data in Mobile Environments: Framework Criteria and Selection

Erdal¹

2018

IJTSRD

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Mobile devices are the center of our lives. The use of the devices exceeded their development objectives. They have become an inseparable part of ours with the application about photography, music, games, education and even health. Mobile applications have gained importance in areas that are not tolerated to delay or loss of data such as health field. There should be some sensitivity in the development of such applications. In this study, the criteria found in healthcare applications developed on mobile platforms have been examined. As a result of the study, suggestions are given to those who will develop health applications on mobile platforms.

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Section: IImentioning

confidence: 99%

Examining the Status of Big Healthcare Data in Mobile Environments: Framework Criteria and Selection

Erdal¹

2018

IJTSRD

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“…On the basis of the principles of adaptation to enable scalable and efficient data stream mining onboard mobile devices, an integrated and extensible toolkit to support the rapid development mobile data mining application's has been developed and readers are referred to Refs for a detailed overview of the toolkit. This toolkit named the Open Mobile Miner comes with a number of built‐in implementations of adaptive and light‐weight mobile data mining algorithms.…”

Section: Ubiquitous Data Mining: From Algorithms To Applicationsmentioning

confidence: 99%

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

Gaber

Gama

Krishnaswamy

et al. 2014

WIREs Data Min & Knowl

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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. activity recognition, smart homes, and emergency or disaster management like bush fires.1,2 Thus, the key focus is on developing data stream mining algorithms that are highly scalable or computationally efficient, energy-efficient, and context or resource-aware. These features enable the continued operation of data stream mining algorithms in a highly dynamic mobile or ubiquitous environment. The typical constraints that have to be addressed in performing mobile or ubiquitous data stream mining are: (1) data streams are generated and sent in real time in a stream format with little or no potential for persistent storage, (2) resource constraints include limited computational resources such as memory, processor speed, network bandwidth, battery power, and screen real-estate, (3) temporal constraints refer to real-time information and decision-making needs that, in turn, necessitate the analysis to be online, incremental, and continuous, (4) mobility of users and devices and the connectivity issues thereof, and 116

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“…Mobile data stream mining has been well researched over the last few years . Open Mobile Miner is a stream mining tool that is tailored to function on mobile platforms . The screen of the mobile device can get cluttered rapidly when trying to visualize the results of a data stream mining process, running onboard the device.…”

Section: Visual Data Mining For Data Streamsmentioning

confidence: 99%

An overview of interactive visual data mining techniques for knowledge discovery

Stahl

Gabrys

Gaber

et al. 2013

WIREs Data Min & Knowl

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In the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry. C 2013 Wiley Periodicals, Inc.

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Open Mobile Miner: A Toolkit for Building Situation-Aware Data Mining Applications

Cited by 27 publications

References 24 publications

Examining the Status of Big Healthcare Data in Mobile Environments: Framework Criteria and Selection

Examining the Status of Big Healthcare Data in Mobile Environments: Framework Criteria and Selection

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

An overview of interactive visual data mining techniques for knowledge discovery

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