A traveller's digital identity: an analysis of current mobile travel apps and tourist behaviours for better digital discovery in urban environments

10.32920/ryerson.14665929.v1 ◽

2021 ◽

Author(s):

Rebecka A. Calderwood

Keyword(s):

Big Data ◽

Social Interactions ◽

Real Time ◽

Competitive Analysis ◽

Instant Messaging ◽

Urban Environments ◽

Common Interest ◽

User Engagement ◽

Digital Identity ◽

And Performance

The purpose of this research is to improve an understanding of travellers as users of mobile travel applications (apps). Research was conducted on travel motives, mapping and navigation-related technology, as well as mobile travel app attributes to find the best design and performance solutions for user engagement. Secondary research and a competitive analysis of current mobile travel apps was performed to rate the effectiveness of app interfaces. Using these findings as a guide, it proposes an interactive self-guided travel app under the title DROP/PIN. DROP/PIN allows travellers to explore urban environments in real-time through personalized interests, big data, instant messaging and navigational technology. Through this implementation, a prototype of the app was created. DROP/PIN aims to foster a traveller’s digital identity through constant personalization and data recognition, social interactions by profile compatibility, and a common interest in global exploration.

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Our Knowledge Management Hubble May Need Glasses

International Journal of Knowledge Management ◽

10.4018/ijkm.2018010103 ◽

2018 ◽

Vol 14 (1) ◽

pp. 30-50 ◽

Cited By ~ 2

Author(s):

William H. Money ◽

Stephen J. Cohen

Keyword(s):

Big Data ◽

Knowledge Management ◽

Real Time ◽

Complex Data ◽

Data Systems ◽

Fault Handling ◽

Architectural Framework ◽

Big Data Systems ◽

And Performance ◽

Streaming Big Data

This article analyzes the properties of unknown faults in knowledge management and Big Data systems processing Big Data in real-time. These faults introduce risks and threaten the knowledge pyramid and decisions based on knowledge gleaned from volumes of complex data. The authors hypothesize that not yet encountered faults may require fault handling, an analytic model, and an architectural framework to assess and manage the faults and mitigate the risks of correlating or integrating otherwise uncorrelated Big Data, and to ensure the source pedigree, quality, set integrity, freshness, and validity of the data. New architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time will contribute to organizational knowledge and performance. System designs must mitigate faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This article concludes that with improved designs, real-time Big Data systems may continuously deliver the value of streaming Big Data.

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Anthropocentric: Real-time data for human centric architecture

10.26686/wgtn.17019428 ◽

2021 ◽

Author(s):

◽

Timothy Voss

Keyword(s):

Big Data ◽

Real Time ◽

Mixed Reality ◽

User Engagement ◽

Design Development ◽

Time Data ◽

Design Elements ◽

Large Sets ◽

Device Applications ◽

Way Finding

<p>This is thesis explores applications of Mixed Reality, commonplace technologies and representation techniques in embodied and interactive design, through the development of an airport wayfinding system. The proposition that airports can be difficult to navigate, struggling to foster social connections, along with the challenging notion of providing an interface for Big Data spatially to users, motivates the research. The development of personalised spatial way finding techniques aids methods for the use of location and big data to ergonomically and spatially represent users’ navigation of space. Through methods of connecting people virtually within a single physical location using a unified design language, social implications of space are enhanced and extended. Finally, space which functions efficiency provides real-time feedback. Key theory in Human Computer Interaction and Embodied Design informs the research, through mixed reality, technology and data-form translations. Research is done over two stages, the first explores data inputs from users and represents these in 2D graphics. The second develops three separate design elements to create a spatial way finding system, to allow user engagement. These are a virtual projection, a set of physical forms and a set of wearable device applications. Design development happens through iterations within each experiment, and are always informed by previous work. The result is an inhabitable data space with seamless embodied design exploring the localisation of large sets of data.</p>

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Anthropocentric: Real-time data for human centric architecture

10.26686/wgtn.17019428.v1 ◽

2021 ◽

Author(s):

◽

Timothy Voss

Keyword(s):

Big Data ◽

Real Time ◽

Mixed Reality ◽

User Engagement ◽

Design Development ◽

Time Data ◽

Design Elements ◽

Large Sets ◽

Device Applications ◽

Way Finding

<p>This is thesis explores applications of Mixed Reality, commonplace technologies and representation techniques in embodied and interactive design, through the development of an airport wayfinding system. The proposition that airports can be difficult to navigate, struggling to foster social connections, along with the challenging notion of providing an interface for Big Data spatially to users, motivates the research. The development of personalised spatial way finding techniques aids methods for the use of location and big data to ergonomically and spatially represent users’ navigation of space. Through methods of connecting people virtually within a single physical location using a unified design language, social implications of space are enhanced and extended. Finally, space which functions efficiency provides real-time feedback. Key theory in Human Computer Interaction and Embodied Design informs the research, through mixed reality, technology and data-form translations. Research is done over two stages, the first explores data inputs from users and represents these in 2D graphics. The second develops three separate design elements to create a spatial way finding system, to allow user engagement. These are a virtual projection, a set of physical forms and a set of wearable device applications. Design development happens through iterations within each experiment, and are always informed by previous work. The result is an inhabitable data space with seamless embodied design exploring the localisation of large sets of data.</p>

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TOWARDS A CLOUD BASED SMART TRAFFIC MANAGEMENT FRAMEWORK

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w4-447-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 447-453

Author(s):

M. M. Rahimi ◽

F. Hakimpour

Keyword(s):

Big Data ◽

Real Time ◽

Traffic Management ◽

Data Retrieval ◽

Storage Management ◽

Distributed Environment ◽

Management Framework ◽

Distributed Framework ◽

And Performance ◽

Smart Traffic

Traffic big data has brought many opportunities for traffic management applications. However several challenges like heterogeneity, storage, management, processing and analysis of traffic big data may hinder their efficient and real-time applications. All these challenges call for well-adapted distributed framework for smart traffic management that can efficiently handle big traffic data integration, indexing, query processing, mining and analysis. In this paper, we present a novel, distributed, scalable and efficient framework for traffic management applications. The proposed cloud computing based framework can answer technical challenges for efficient and real-time storage, management, process and analyse of traffic big data. For evaluation of the framework, we have used OpenStreetMap (OSM) real trajectories and road network on a distributed environment. Our evaluation results indicate that speed of data importing to this framework exceeds 8000 records per second when the size of datasets is near to 5 million. We also evaluate performance of data retrieval in our proposed framework. The data retrieval speed exceeds 15000 records per second when the size of datasets is near to 5 million. We have also evaluated scalability and performance of our proposed framework using parallelisation of a critical pre-analysis in transportation applications. The results show that proposed framework achieves considerable performance and efficiency in traffic management applications.

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