Interactive self-adaptive clutter-aware visualisation for mobile data mining

Gaber, Mohamed Medhat; Krishnaswamy, Shonali; Gillick, Brett; AlTaiar, Hasnain; Nicoloudis, Nicholas; Liono, Jonathan; Zaslavsky, Arkady

doi:10.1016/j.jcss.2012.09.009

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…The approach is based on a theoretical underpinning that balances perception, clutter, and amount of information of the visual image, having two observations: (1) the more information, the greater the clutter of the visualized image; (2) the higher the clutter, the less the perception of the image. With the objective of increasing the information and reducing the clutter to enhance the perception, Gaber has developed Adaptive Clutter Reduction (ACR) . ACR is a generic theory that states an optimization function that works on maximizing both the information and perception while reducing the amount of clutter.…”

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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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

Self Cite

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“…In connection with PEs, not only are PSDs being made resourceful but also providing numerous APIs to maximize the benefits from on-board sensors and device-resident data [ 1 ]. Despite the immense growth in micro- and nanotechnologies, PSDs are still restricted in terms of computational power, energy consumptions, on-board memory, and small screen real-estate [ 7 ]. Moreover, limited storage capacities and bandwidth are major constraints of PSDs.…”

Section: Introductionmentioning

confidence: 99%

“…Local visualization at small screen real-estate in PSDs is useful for basic visualization. This limitation sometimes necessitates remote visualization using more resourceful PSDs or external environments, because detailed and graphic intensive visualization are not usually provided in most PSDs [ 7 ]. Currently graphic-intensive PSDs are emerging but they are not very useful for real-time data mining systems displaying continuous patterns and utilizing immense battery and computational powers.…”

Section: Introductionmentioning

confidence: 99%

“…Smartphones generally have sufficient screen size for basic visualization facilities; however, detailed graphics-rich visualization requires the use of larger multimedia display screens, such as that of tablets, desktops, or overhead projectors. An adaptive visualization approach has been proposed to effectively deal with on-screen clutter of results from data stream mining algorithms [ 7 ]. The research on knowledge visualization in PSDs is still at its early stages, and many issues related to adaptation, fast results handling, and scalability with volatile data rates remain to be addressed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mining Personal Data Using Smartphones and Wearable Devices: A Survey

Rehman

Liew

Wah

et al. 2015

Sensors

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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.

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Data-Imagined Decision Making in Organizations: Do Visualization Tools Run in the Family?

Locoro

Ravarini

2020

Digital Business Transformation

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Interactive self-adaptive clutter-aware visualisation for mobile data mining

Cited by 9 publications

References 20 publications

An overview of interactive visual data mining techniques for knowledge discovery

An overview of interactive visual data mining techniques for knowledge discovery

Mining Personal Data Using Smartphones and Wearable Devices: A Survey

Data-Imagined Decision Making in Organizations: Do Visualization Tools Run in the Family?

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