WITHDRAWN: Information fusion in social big data: Foundations, state-of-the-art, applications, challenges, and future research directions

Yaqoob, Ibrar; Chang, Victor; Gani, Abdullah; Mokhtar, Salimah Binti; Hashem, Ibrahim Abaker Targio; Ahmed, Ejaz; Anuar, Nor Badrul; Khan, Samee U.

doi:10.1016/j.ijinfomgt.2016.04.014

Cited by 31 publications

(12 citation statements)

References 53 publications

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“…Big data is characterised by a huge volume and variety of data types; created at continually increasing rates [38]. Big data analytics allows for the extraction of meaningful information from large amounts of data produced by ICTs [37][38][39].…”

Section: Research Frameworkmentioning

confidence: 99%

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

et al. 2017

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Abstract:As cities become increasingly complex, Information and Communication Technologies (ICTs) bring smartness into organisations and communities, contributing to a more competitive tourism destination, i.e., smart tourism destinations. Enhanced information access coupled with a new kind of tourists avid for online content and predisposed to share information on social media, allows for a better understanding of tourist behaviour regarding their spatial distribution in urban destinations. Thus, smart tourism portrays individuals as information makers, refining the available alternatives for tracking their location. Big data analytics is a technology with the potential to develop Smart City services. From the analysis of the spatial distribution of tourists in the city of Lisbon based on data collected from the 'Panoramio' social network, we identify the most popular places in the city in a context of tourist visits. This new data largely contributes to understanding the consumption of space within urban tourist destinations and therefore enables us to differentiate the overcrowded places from the ones with potential to grow. This allows decision-makers to imagine new ways of planning and managing towards a sustainable 'smart' future.

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Section: Research Frameworkmentioning

confidence: 99%

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

et al. 2017

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“…Data Fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source (Castanedo 2013). It has been applied in many fields where data is distributed and generated from diverse sources (Yaqoob et al 2016). In affective design, to get a holistic view about products, businesses ought to integrate information from these multiple channels.…”

Section: Data Fusionmentioning

confidence: 99%

Affective design using machine learning: a survey and its prospect of conjoining big data

Chan

Kwong

Clark

et al. 2018

International Journal of Computer Integrated Manufacturing

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Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today's competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design.

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“…Snidaro et al (2015) provided the comprehensive status of recent and current research on context-based information fusion systems, tracing back the roots of the original thinking behind the development of the concept of ''context.' ' Yaqoob et al (2016) provided a comprehensive view of information fusion in social big data, including the foundations, state-of-the-art research, applications, challenges, and future research directions. Xu and Zhao (2016) presented an overview of the existing intuitionistic fuzzy decision-making theories and methods from the perspective of information fusion, involving the determination of attribute weights, the aggregation of intuitionistic fuzzy information, and the ranking of alternatives.…”

Section: Meta-analysismentioning

confidence: 99%

Multi-source data fusion study in scientometrics

Yue

Wang

et al. 2017

Scientometrics

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This paper provides an introduction to multi-source data fusion (MSDF) and comprehensively overviews the ingredients and challenges of MSDF in scientometrics. As compared to the MSDF methods in the sensor and other fields, and considering the features of scientometrics, in this paper an application model and procedure of MSDF in scientometrics are proposed. The model and procedure can be divided into three parts: data type integration, fusion of data relations, and ensemble clustering. Furthermore, the fusion of data relations can be divided into cross-integration of multi-mode data and matrix fusion of multi-relational data. To obtain a clearer and deeper analysis of the MSDF model, this paper further focuses on the application of MSDF in topic identification based on text analysis of scientific literatures. This paper also discusses the application of MSDF for the exploration of scientific literatures. Finally, the most suitable MSDF methods for different situations are discussed.

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WITHDRAWN: Information fusion in social big data: Foundations, state-of-the-art, applications, challenges, and future research directions

Cited by 31 publications

References 53 publications

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

Affective design using machine learning: a survey and its prospect of conjoining big data

Multi-source data fusion study in scientometrics

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