Conference PaperPDF Available
When Augmented Reality meets Big Data
Carlos BERMEJO, Zhanpeng HUANG, Tristan BRAUD, and Pan HUI
System and Media Laboratory,
Department of Computer Science and Engineering,
The Hong Kong University of Science and Technology,
Clear Water Bay, Kowloon, Hong Kong
Abstract—We live in an era where we are overloaded with
data, and this can be the key for gaining rich insights about
our world. Augmented reality (AR) enables us the possibility to
visualize and analyse the growing torrent of data in an interactive
canvas. We can display complex data structures in simpler and
more understandable ways that was not possible before. Big Data
is a new paradigm results from the myriad data sources such as
transactions, Internet, social networks, health care devices and
sensor networks. AR and big data have a logical maturity that
inevitably will converge. The tread of harnessing AR and big data
to breed new interesting applications is starting to have a tangible
presence. In this paper, we explore the potential to capture
value from the marriage between AR and big data technologies,
following with several challenges that must be addressed to fully
realize this potential.
The development of Internet, social network, mobile devices
have had an impact in the amount data transmitted and shared
across the globe. Besides, we are facing a new Business era
where data-driven decisions are better decisions.
Human intuition tells us it will be easier to make sense of
and interact with information if it is merged with the physical
world [15]. As a modality to display information by overlaying
virtual content on the current view of the world around us,
AR enhances the way we acquire, understand, and display
information without distraction from the physical world [10].
Over the past few years, AR has been progressing by leaps
and bounds in terms of technology and its applications.
However, even though technologies such as sensing, track-
ing, and displaying have improved, AR application patterns
broadly follow the lines of prototypes and demonstrations,
such as virtual pop-up objects on 2D markers, and sample
data visualization. A major impediment to AR adoption is the
lack of data sources described by MacIntyreetal [15]. AR is
data-hungry and requires more data than most applications.
Apart of the application-specific content that users see and
interact with, AR needs to feed applications knowledge about
its surroundings and descriptions about how it relates to
the application data. Imperative environmental information
may include geospatial coordinates and models of nearby
buildings, features and semantic descriptions of objects, and
linkage between physical and virtual content. Previous works
acquired data from the legacy database, but the data may
be incomplete or out-of-date due to sparse sensing and the
absence of persistent maintenance. The first AR prototype was
developed by Sutherland in 1960s [21], but it only started to
draw attention in the last two decades. A typical AR includes
three characteristics defined by Azuma [1]: Combines the real
and the virtual; Interactive in real time; Registered in 3-D.
Applications such as tourism [19], advertisement1, educa-
tion [5], and assembly [7] are looking for a way to supplement
the physical world with virtual content rather than replacing
it with VR applications. Recently, AR’s popularity has grown
on mobile devices. This subset of AR is known as Mobile
AR (MAR). Pokemon GO2, for example, is a well-known
MAR application that offers location-based AR mobile game
experience. The Pokemon GO predecessor Ingress3generated
almost 2 million US dollars the first days after its release.
The high penetration of technologies spanning mobility, so-
cial networks, and the Internet of Things (IoTs) has catapulted
the world in the era of big data. Touted as a game changer, big
data has been identified by the US government as a research
frontier that is accelerating progress across a wide range of
priorities [9]. AR and big data have been around shaping their
own landscapes in various fields for a few years. However, the
intersection of two disruptive technologies has not attracted
much attention yet. The rich insight of big data and novel
display modality of AR is promoting the convergence of AR
and big data. AR has great opportunities to bring innovation to
big data in terms of visualization and interaction. In this paper,
we explore the potential opportunities from the convergence
of both disruptive technologies alongside several challenging
problems that should be addressed. According to a McKinsey
report [16], big data has the potential to reduce product de-
velopment and assembly costs by 50% and increases retailers
operating margins by 60%. It is estimated to create savings of
300 billion dollars in healthcare every year in the US alone.
Big data analysis has been widely used in healthcare4, energy
saving, and financial risk analysis [4].
Interpretation is a major phase of the big data analysis
pipeline, which is critical for users to extract actionable
knowledge from massive and highly complex datasets. As an
1 reality-ads-
intuitive interpretation, visualization transforms plain and bor-
ing numbers into compelling stories to help users understand
the data. In addition, big data analysis requires a human-in-
the-loop collaboration at all stages of the analysis pipeline
[13]. Powerful interactions help to explore and understand the
data more easily and fully, understanding the data is the key
to success in the current data-driven business ecosystem.
A. Data Visualization
With visualization, we turn the big data into a landscape
that we can explore with our eyes. A visual information
map is useful when we are drowning in information. Data
visualization has the ability to take the complex abstract
symbols and transform them into simpler visual concepts that
we can quickly understand. Visualization-based data discovery
tools have already delivered greater customer and market
insights to businesses around the world5. It is estimated by
Gartner that data visualization tools will promote a 30%
compound annual growth rate in 2015 [20]. In the past, we
have employed diverse ways of visualizing data using tabular
presentations, interactive bubble charts, treemaps, heatmaps,
3D data landscapes, and other types of graphics. The data
is generally displayed on flat mediums such as desktops and
more recent mobile screens, which separates the visualiza-
tion from the data source and user context. For instance, a
virtual array of gauges to display temperatures would fail
to fully describe the spatial distribution as the temperatures
are strongly related to real world objects in a physical user
context. In many cases, it is easier to gain insight from the
data if the visualization is embedded in the physical world. Big
data visualization can be enhanced if an AR layer is overlaid
on real-time streaming data or user context. The AR enables
users to be immersed in a world where the data is much more
easily understood if it corresponds explicitly to real content,
as shown in Figure 1. In this Scenario, since data is usually
directly connected with physical context, display simplification
which impairs user’s understanding in traditional display is no
longer required. For instance, to find a book among millions
of others in a library, traditional methods require digital 2D or
3D maps and floating bubbles for coarse positioning. Powered
with AR, users are able to see through walls and shelves
to look for indications, e.g. the highlighting contour of the
book. As an indication is usually located according to a
user’s current view, it will be much easier to find the book.
Collaborating with AR, big data is especially suitable for
in-situ visualization and field diagnosis. For example, with
rapid growth in information building information modeling
(BIM), AR steps in every phase of a building’s life cycle,
from building to maintainance [23]. This enables field workers
to view the on-site feasibility design and assist construction
with virtual simulations and clash detection. It also facilitates
assets management in daily inspection activities based on a
torrent of data from in-built sensors [12]. To achieve success
5 peer-
Fig. 1: Visualization of a numerical flow field with real
buildings makes the influence of the building on wind
movement easily understood. (source: http://emcl.iwr.uni- sv.html)
with big data visualization, we need to rethink how to mix
digital data with physical world and present the information
to users. Apart from application-specific requirements, other
considerations should be kept in mind. Floating bubbles are
widely used by many AR applications, however, it seems to be
pointless and no improvement on a 2D map [23], especially
when the data content can not be seamlessly integrated into
the real world. Content should be merged into the physical
world in a way that users perceive as a real counterpart,
just as the 1st and Ten system used by ESPN in American
professional football broadcast. To achieve this, visualization
requires immense graphic effort and additional information
for visual occlusion (e.g. something hidden behind a physical
building) and ambient lighting.
B. User Interaction
Today’s world is becoming a canvas for big data from a
wide range of sources, which profoundly influences people’s
perception and understanding of the environment around them.
It requires a user-friendly interface to interact with the digital
world. Traditional user interface design is constrained by
finite physical dimensions. It becomes especially severe when
mobile devices are preferred as a medium to interact with
torrents of data from social media, online transactions, and
The ability to associate data with the physical world dis-
closes the causality between data and reality. In addition,
mashing up data from various sources dramatically increases
the probability of discovering relevant and interesting things.
Furthermore, not only the user interface (UI) is a key factor in
users engagement for AR applications, but the user experience
(UX), which involves user behaviour and emotions towards
a specific artefact, needs to be considered in any current
application design.
A few works [8] further employed AR as user interface
to redefine and attach new functions to physical objects. It is
even more compelling when AR is integrated with accessories
such as AR glasses and AR contact lenses. Wearable and
invisible accessories reduce device intrusion, which provides
a hand-free interaction experience. Smart glasses and contact
Fig. 2: Left: Sight, a short futuristic film by Eran May-raz
and Daniel Lazo to look into a future AR with retinal lenses.
Data from sensors, apps, and Internet augment current views
(source:; Right: A prototype of
bionic contact lenses with a built-in LED for virtual display
lenses6, such as Google Glass, made huge strides in this area.
With implanted and invisible accessories, AR allows users
to interact with data securely. As the user interface is only
seen and manipulated by the user alone, it reduces the risk
of data and privacy leakage. Data can be viewed through
the AR interface as personal media or by multiple users
in a collaborative way. As a personal information center, it
collects data from various sources, and displays it on intangible
interface without physical constraints. In the collaborative
mode, multiple users share the same data set and view it from
their own angle.
AR applications can enable a new approach to provide
data visualization of complex Big Data structures in an easier
manner and provide a better experience for users to interact
with it. Besides, AR can be a very usable canvas to visualize
big data sets as we can see from Sci-fi movies such as Avatar
(Figures 2, 3).
Big data and AR have shaped new business regimes that
were irrelevant in the past, but the landscape is undergoing
a seismic shift with advances in technology convergence and
connectivity. A majority of AR applications are constrained
in an enclosed or pre-compiled environment due to a limited
dataset that is either not available or too sparse to use. As
rapid penetration of mobile devices, social networks, and IoT
are generating considerable amounts of data, it makes sense
for big data to enable AR to be more feasible for practical
A. Retail
AR has been in use in the retail business for the last 5 years.
A majority of applications such as Junaio and Wikitude AR
browsers overlay geospatial-related data on current view to
offer general information. A few AR apps promote virtual ad-
vertisements to catch customers’ eyes. However, it is frustrat-
ing to boost customers’ interest in products if their behaviors
and preferences are not taken into account. Without adequate
6 contactlens.
Fig. 3: Large data visualization and interaction among multiple
users in the science fiction movie Avatar directed by James
Cameron, which may be portrayal of future user interface with
AR. (source:
information from customers, AR is less attractive for practical
use and more like a gaudy, flashy technology. The mobility
trend preferred for purchase and social communication has
led to the birth of digital consumers8, which also brings us
to the era of product digitalization. Digitally active consumers
have changed their mode of transaction, communication, and
purchase decision making. Consumers use mobile devices for
online transactions, and social media for advice on what to
buy and where to shop, leaving trails of their performances,
states, and decision. It is obviously valuable to explore the
large amounts of data to understand consumers’ shopping
preferences and behavior, which helps to tailor promotions and
recommendations to customers. However, it can be even more
promising when it is combined with AR technology. Harnessed
with big data, AR promotes vertical retail to individual con-
sumers. A conscious and activated shopping context have an
impact on customers’ mental presentation [2], helping them to
be active, informed and assertive in their shopping decisions.
When the new eye gazing and facial expression technolo-
gies, and other physiological measurements eventually gain
a tangible presence, it will enable us to better understand
customers’ focus and emotions to provide more accurate
recommendations and advertisements. A few works have used
eye-tracking glasses to collect customer point of gaze in-
formation for shopping behavior analysis. According to a
Marks & Spencer report [24], people using mobile shopping
channels spend eight times as much as people shopping in
stores. Meeting consumers where they are is the key to
future consumer engagement [22]. With the advantages of high
mobility and consistent virtual content provided by big data,
AR breaks physical constrains to enhance the virtual shopping
experience anywhere and any time.
B. Tourism
In terms of environmental awareness, AR is concerned about
presenting contextual information and assisting in daily activ-
ities, which is particularly helpful when people are unfamiliar
with the environment around them. By highlighting interesting
8 reality-ads-
features or bringing history to life, AR provides intuitive
means to enhance a touring experience. As travel is normally
associated with geo-spatial exploration, most AR applications
for travel guides are based on geo-spatial information. A
user’s position is tracked using GPS and built-in sensors, and
it is then used to search and locate multimedia information
from data sources such as point of interest (POI) databases,
geocoded Tweets, and Flickr.
Trends such as the fast deployment of sensor networks and
the growing use of mobile devices and social networks are
generating large amounts of both structured and unstructured
data. Aggregating and compiling the redundant fragmented
data helps us to build a detailed and complete environmental
model, which enables AR to understand users’ surroundings
better even in an open and unfamiliar environment. According
to the Business Insider’s survey9, intelligent recommendation
is regarded as the most attractive expectation of tourism. As
the world is getting smarter with big data, it comes with the
ability of tracking and measuring tourists’ needs and behaviors
to ensure a responsive and intelligent trip experience. For
instance, personalized travel guide information is overlaid on
the tourists’ current view to avoid being distracted from tourist
spots and getting lost. Native language signs are automatically
translated into readable words which are overlaid on the orig-
inal places. To go a step further, information such as locations
of nearby rest sites and restaurants can be recommended
according to tourists’ needs based on walking distance and
C. Health Care
When we are making life and death decisions, immediate
access to relevant and necessary information is of the utmost
importance. AR importance in healthcare industry is attributed
to its ability to instantly in-situ display relevant information
when required. AR has been used in the medical field for
nearly ten years. We have seen AR’s powerful ability of x-ray
vision, which has been used to provide contextual cues for
diagnosing patients and learning tools for medical students by
projecting computerized tomography (CT) scans or medical
images on a current view10. In one example, images of veins
are overlaid on a nurse’s view of a patient’s hand to help
the nurse insert the IV in one painless attempt. In another AR
application developed by German research institute Fraunhofer
11, a digital overlay of key blood vessels are displayed on
the iPad when the doctor holds the embedded camera on a
patient’s body to avoid accidentally cutting them. Although the
early examples have proven AR’s ability to change the health-
care landscape, AR’s great lifesaving potential for healthcare
industry can not be fully presented without big data support.
Without adequate data sets, AR is merely for medical
education purposes and a bedside manner test. A decision
is strongly made based on doctors’ hunches and experience
9 will-
eliminate-travel-frustration- 2012-8
11 future-of- user-interfaces/
Fig. 4: Corning’s vision of a future operation
room augmented with remote presence. (source:
rather that the data itself. According to a survey by Man-
hattan Research12, 72% of physicians routinely use computer
tablets every day. With the substantially increasing usage
of tablets among physicians and digital care devices among
patients, paper prescriptions and manual health records are
being replaced by electronic health records (EHRs). Patient
data is being digitalized, leading to a flood of digital data
from which medical decisions that previously were based on
guesswork and experience can be made based on data itself
(Figure 4). Creating a virtual viewfinder to display a pertinent
health record enables the doctor to quickly access valuable
information in the context of patients. In-suit visualization of
historical illnesses or tissue damage over patients themselves
helps the doctor understand his or her patients better. In future,
AR can even visualize a virtual operating room for doctors at
different places to diagnose a patient in a collaborative way.
Working closer with ourselves in daily life, AR can be
significantly important for self-tracking health, as wearable
devices that could sense heart rate, blood oxygen, or even
cholesterol level become available to everybody. With each
one of us becoming a walking data generator [17], we would
be able to keep track of our own basic health statistics. AR
displays real-time notifications to allow us to immediately
understand our own health condition, and it may even make
suggestions based on health statistics and diet.
D. Public Services
The government is responsible for providing public services,
which both produce and consume large amounts of data. As
the largest spender in any economy, the government has the
most diverse channels for collecting data from public services,
which includes transportation, social services, national secu-
rity, defense, and environmental stewardship, among others.
According to a joint global survey by Bloomberg Business-
week and SAP [18], 81% of the questioned believe that public
sector will inevitably be transformed by big data. The govern-
ment can use big data to provide better services to citizens.
Public services, such as transportation and security protection,
physicians-embrace- patient-self- tracking-202522041.html
will be more efficient and productive if they are delivered di-
rectly to individuals in their own contexts with AR technology.
For instance, the upcoming traffic flow displayed on a screen
will help drivers avoid traffic accident. Personal information
overlaid on passengers will enable security specialists to
very quickly verify identification and reduce screening traffic.
Augmented government [3] has been proposed to improve
government services with AR technology in a few US public
sectors. The AR strategy for government service delivery
will be more promising if it is supported by big data from
surveillance systems, mobile and sensor networks, and social
media. As sensors and wireless networks continue covering
vehicles, it is much easier to collect massive traffic statistics
to make us aware of the traffic situation. For example, cars
present within a range of distance can share GPS positions,
speed, and direction information with a vehicular Ad-hoc
network (VANET). AR can display the information in front
of drivers for performing thread assessment and predicting
any potential car crashes. In particular, by harnessing the x-
ray vision capability, drivers can see through buildings or
vehicles to watch for vehicles positioned in their blind spots.
Overlaying essential information such as driver’s license and
vehicle’s location and speed directly over the vehicle will also
help traffic police to quickly determine whether the driver
violated any traffic rules. A similar strategy can be employed
by security agencies to rapidly identify suspects. The city is
being digitalized by ubiquitous sensor and social networks,
which makes it transparent for city managers to look into.
To give an example, in a civil engineering maintenance work
scenario, a virtual image of a subsurface infrastructure can
be superimposed on a field workers’ vision of the site to
enable them rapid perception of the underground network
layout. Field workers can collaborate from different aspects by
giving contextualized views to each supporting role. Individual
view is personalized and annotated for each worker’s context,
such as electrical-line view for the electrician and plumbing-
line view for the plumber, which harnesses the collective
intelligence of all roles to improve efficiency. In another
example, a virtual bird’s eye view directly overlaid on an
emergency staff’s vision will greatly assist in the search and
rescue of persons trapped in a burning or collapsed building.
As civil infrastructure such as electrical and water supply
networks are getting smarter with IoT, torrents of data can
be used for in-suit visual analysis without field damage by
using AR technology.
We have seen an emerging shift in mindsets of big data and
AR from industry and business, but there are still significant
barriers to overcome. Several barriers, such as intrusive dis-
play, battery life, and highly fragmented data, are practical.
Some barriers are conceptual. We should first address these
challenges before we can see their full potential in action.
Converging big data and AR brings practical technical chal-
lenges from both sides. Heterogeneity, scale, and complexity
are problems with big data that impede the process of all
phases from data acquisition to aggregation and analysis.
AR applications are also constrained to extensive calibration,
incomplete reference modelling, and poor environmental sens-
ing. A comprehensive discussion of the technical problems
from each side is out of the scope of this paper. Interested
readers can instead refer to Huang’s [10] and Labrindis’ [13]
papers for details. Herein we explore emerging practical tech-
nological problems and common conceptual barriers induced
by converging both technologies.
A. Timelines
AR applications generally require real-time performance to
guarantee fluent user interaction, which requires immediate
analysis results (20 to 7 ms) from Valve software study13.
However, large-scale data analysis usually takes longer times
due to the voluminous and highly fragmented data. The
problem becomes considerably more challenging when the
trend of minimization in AR devices conflicts with the growing
volume and fragmentation of big data. The cloud is able
to store a large amount of data and handle computationally
intensive tasks within a fixed time cap. A few applications [11]
have proven cloud’s ability to meet requirements in a timely
fashion. In addition, offloading computation and data storage
enables client-side AR devices to be small and sustainable
enough without intrusion.
B. Interpretation
By integrating big data with AR, we acquire data analysis
ability. However, the ability to analyze data is of limited value
if the results can not be understood by AR. Users of big
data analytical systems are data scientists while AR users are
customers without much technical background. The present
data analysis pipeline has rather been designed explicitly to
have a human in the loop because many patterns are obvious
for humans but difficult for computer to understand [13], how-
ever AR prefers to be intuitive and used without interruption.
Analysis results require interpretation in the context of AR. For
instance, the output of a customer behavior analysis system is
normally customer stats, but AR is responsible for how to
use the stats. AR should be able to interpret the results as
preferential information so as to provide a recommendation
to a customer’s specific context. Although big data is good at
discovering correlations, especially subtle corrections that are
not possible from a small data set, it does not tell us which
correlations are meaningful, while AR requires semantically
meaningful information to relate to the users’ context. There
is no easy way for big data and AR to intelligently interpret
for each other. However, a collaborative effort to embrace
AR content and provide native APIs for AR to interpret
semantically-tagged data from all data generators is a possible
solution. A standard data format such as Augmented Reality
Markeup Language (ARML) [15] is an essential step in the
right direction.
13 latency-the-sine-qua-non-of-ar-
C. Privacy
There is a growing concern about privacy in the context of
both AR and big data. In order to provide a personalized rec-
ommendation, AR generally requires to access users’ personal
information and record their location. Studies show that users’
identities and their movement patterns have a close correlation
[6]. Even though people pay attention to personal information,
an attacker can infer private information from their location
information [13]. Although the data is fragmented and incom-
plete from individual sources, it is interrelated and includes
frequent patterns and redundant knowledge. When data is
aggregated from numerous data sources, hidden relationships
and models can be extracted by data analysis. Privacy is
both a technological and sociological problem. Forceful laws
and regulations may be required to avoid inappropriate use
of personal data and malicious AR applications. From the
technical standpoint, differential privacy is a possible way
of accessing data with a limited privacy risk, however the
information is reduced too far to be useful in practice. Not
only that, it is ill-suited for dynamically changing data.
In this paper rather than just an up-to-date survey of big data
and AR technologies, we have broadened our outlook to merge
them to breed new applications. The factors for promoting
the convergence of the two technologies also created some
challenges not previously experienced by either technology.
The key principle is to combine the main features of each
technology as we devise novel applications using both. Al-
though the technologies are still in their infancy, attention has
always been a necessary component of the convergent strategy
of AR and big data. While the concepts are not unfamiliar to
us, most participants have not yet harnessed their full potential.
Only when we take full action can we get a real picture of
what it will take for big data and AR to move from being just
hype to becoming real game changers.
This research has been supported, in part, by General
Research Fund 26211515 from the Research Grants Council
of Hong Kong and the Innovation and Technology Fund
ITS/369/14FP from the Hong Kong Innovation and Technol-
ogy Commission.
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from: www. theguardian. com/business/2012/sep/02/marks-andspencer-
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... As AR supposes the interplay between real and virtual worlds, the technological demands and challenges in AR are higher than in pure virtual reality (VR); thus it needs longer time to mature as a technology compared to VR (Krevelen & Poelman, 2010a). Any realisation of AR requires some sort of output device (usually display or projector), sensors (for input and registration), processing unit and possibly other technologies, depending on the type of AR offered (Chatzopoulos, Bermejo, Huang, & Hui, 2017;Krevelen & Poelman, 2010a). While first AR prototype appeared in 1960s it took fifty years for truly mass-market technology to be developed (Krevelen & Poelman, 2010b;Tamura, 2002). ...
... Broadly speaking, AR can be used in e-Government in two different ways: as a mean of reducing complexity of large amounts of information through better visual representation of the data, and as a way of making the services more interactive and user-friendly by providing a user with a natural way to interact with the application. Huang et al. (2014) and Bermejo et al. (2017) explore possible uses of AR technologies in big data visualisation and suggest that in public services, AR will be particularly useful in healthcare, urban planning, transportation, policing, surveillance and more effective collaboration between public workers. In each of these areas, AR can help to make sense of large amounts of available data and make the public services more efficient. ...
... Healthcare provision can be improved by visualising patient data in AR and improving diagnosing and treatment. Quicker access to patient data through augmented views can give doctors an access to the relevant information about the patient (such as patient's history, tomography and X-ray scans and other images) and when necessary even overlay these data over the patient's view (Bermejo et al., 2017;Rosenthal et al., 2002). ...
... One timely research topic in the smart city vision is facilitating the interaction between the city and its citizens via Augmented Reality (AR) [1,10,194,195], enabling citizens to access various smart city services conveniently, e.g. through wearable computers [190]. To this end, wearable AR headsets and smartglasses are enablers for user interaction with the city-system. ...
... Scaling up to thousands of square kilometres areas will only increase the issue. In the case of smart cities, the amount of data to interact with is so large that large-scale context-awareness is required for intelligently selecting the data to display to the user [190]. For instance, connected vehicles may share vision to improve the awareness of the driver [203,204]. ...
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Interaction design for Augmented Reality (AR) is gaining increasing attention from both academia and industry. This survey discusses 205 articles (75% of articles published between 2015 - 2019) to review the field of human interaction in connected cities with emphasis on augmented reality-driven interaction. We provide an overview of Human-City Interaction and related technological approaches, followed by a review of the latest trends of information visualization, constrained interfaces, and embodied interaction for AR headsets. We highlight under-explored issues in interface design and input techniques that warrant further research, and conjecture that AR with complementary Conversational User Interfaces (CUIs) is a key enabler for ubiquitous interaction with immersive systems in smart cities. Our work helps researchers understand the current potential and future needs of AR in Human-City Interaction.
... AR is about enriching the view of the real world with virtual elements (Lee, 2012). Both AR and VR are primarily used for visualisation, for example in planning, transportation, surveillance, etc. (Bermejo et al., 2017;Huang et al., 2014). Potentially, these technologies can contribute to deliver public services remotely, which is particularly relevant in pandemic times. ...
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The broad diffusion of so-called disruptive technologies in the public sector is expected to heavily impact and give a strong digital boost to public service provisioning. To ensure acceptance and sustainability, the benefits and challenges of using disruptive technologies in public service provisioning need to be well researched. This chapter applies scenario-based science and technology roadmapping to outline potential future uses of disruptive technologies. It develops a roadmap of research for Government 3.0. Based on a literature review of disruptive technologies in Government 3.0, thirteen scenariosScenarios sketch possible use of internet of things, artificial intelligence, machine learning, virtual and augmented reality, big data and other disruptive technologies in public service provisioning. Subsequently, gap analysis is applied to derive a roadmap of research, which outlines nineteen research actions to boost innovation in public serviceInnovation in public service with the use of disruptive technologies, thereby building on engagement of and interaction with expert stakeholders from different fields. We conclude with recommendations for a broader and more informed discussion about how such new (disruptive) technologies can be successfully deployed in the public sector—leveraging the expected benefits of these technologies while at the same time mitigating the drawbacks affiliated with them.
... Beyond the relational databases that are primarily used in data acquisition, big data and crowdsourcing methods can be utilized to acquire data [30], [31], [32], [33]. Big data can analyze the data that has a location information without any data classification. ...
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This study presents a model that aims to enable better understanding of local site-specific data in the context of architectural design using augmented reality. It is developed by considering the necessities of local data in architectural design and the potentials of the location based augmented reality. The classification of the local data is provided with the use of a framework, and the structure of the model is based on researches on location based augmented reality applications. The operability of the model is described by an integrated workflow that is explained under the stages of data acquisition, data query and data display. Lastly, it is conceptually presented by use cases that are focus on the necessities of local data from the architectural perspective. By bringing the architect into more direct contact with the site, the model facilitates to understand local data in situ and supports the reasoning process to design.
... Beyond the relational databases that are primarily used in data acquisition, big data and crowdsourcing methods can be utilized to acquire data (Bermejo et al., 2017;Huang et al., 2014;Vico et al., 2011;Ioannidi et al.,2017). Big data can analyze the data that has a location information without any data classification. ...
... Further networking challenges pertaining to AR applications are studied in [7], [11], where it is verified that indeed, the most challenging processes in terms of resource consumption are object detection and recognition. Opportunities and research challenges that big data brings to AR are presented in [12]. ...
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This work presents a novel approach that adopts content caching techniques towards reducing computation and communication costs of Augmented Reality (AR) services. The application scenario under investigation assumes an environment of static objects, each one associated to a holographic content. The goal is to devise practical low-overhead methods so as to reduce the amount of resources above that are needed for the most resource-demanding AR process, namely object recognition. The proposed method is based on caching images using a combination of metrics to rank them such as: (i) an object popularity index which favours objects that are most probable to be requested for recognition, (ii) the percentage of times when the object label has been encountered in the past, (iii) the probability that an image is similar enough with already encountered past images with the same label. The aforementioned image caching method drastically reduces database searches and returns the matched object that satisfies the needs of object recognition. We also devise a binary decision operator that initiates the object recognition process only upon comparison of spatial data of the AR device with the targeted object. The resulting performance is measured using a client-server architecture and components such as Wireshark, Unity Profiler, and Python. For our proposed architecture we deploy an edge server to satisfy the demands of the AR service. Results indicate that the proposed methods can significantly reduce both the computational resources and the induced network traffic, thus improving user experience.
In today’s fast moving world, the growth in technologies is irrefutable. Especially in health care, the need of technology advancement is meticulous. The emerging technologies include Internet of things (IoT), Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), and Robotics. Each and every gadget is made up of some sensors. With the day-to-day advancements in manufacturing and optimization technology, sensors are becoming a powerful supporting device in every field which gives ease in the collection of data. The collection of information is done with the help of sensors like MEMS sensor, position tracking sensors, etc. for the respective network technology from the real world at various locations in a distributed physical environment. Apart from the advancement in technologies, there is a trade-off between the data collection and data handling. Big data analytics itself an extreme part of these emerging technologies since all these advancements are based on data. Before entering into the data analytics, in this chapter, the essential informatics of various sensors used in distinct applications based on emerging technology such as IoT, AR/VR, and Mixed Reality in healthcare applications is conferred here.
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The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of "Big Data," including the recent announcement from the White House about new funding initiatives across different agencies, that target research for Big Data. While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is no clear consensus on what is Big Data. In fact, there have been many controversial statements about Big Data, such as "Size is the only thing that matters." In this panel we will try to explore the controversies and debunk the myths surrounding Big Data.
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The Architecture, Engineering, Construction, and Owner/Operator (AECO) industry is constantly searching for new methods for increasing efficiency and productivity. Facility Managers (FMs), as a part of the owner/operator role, work in complex and dynamic environments where critical decisions are constantly made. This decision-making process and its consequent performance can be improved by enhancing Situation Awareness (SA) of the FMs through new digital technologies. In this paper, InfoSPOT (Information Surveyed Point for Observation and Tracking), is recommended to FMs as a mobile Augmented Reality (AR) tool for accessing information about the facilities they maintain. AR has been considered as a viable option to reduce inefficiencies of data overload by providing FMs with a SA-based tool for visualizing their “real-world” environment with added interactive data. A prototype of the AR application was developed and a user participation experiment and analysis conducted to evaluate the features of InfoSPOT. This innovative application of AR has the potential to improve construction practices, and in this case, facility management.
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Augmented Reality (AR) is becoming mobile. Mobile devices have many constraints but also rich new features that traditional desktop computers do not have. There are several survey papers on AR, but none is dedicated to Mobile Augmented Reality (MAR). Our work serves the purpose of closing this gap. The contents are organized with a bottom-up approach. We first present the state-of-the-art in system components including hardware platforms, software frameworks and display devices, follows with enabling technologies such as tracking and data management. We then survey the latest technologies and methods to improve run-time performance and energy efficiency for practical implementation. On top of these, we further introduce the application fields and several typical MAR applications. Finally we conclude the survey with several challenge problems, which are under exploration and require great research efforts in the future.
Conference Paper
Graphical User Interfaces (GUI) offers a very flexible interface but require the user's complete visual attention, whereby Tangible User Interfaces (TUI) can be operated with minimal visual attention. To prevent visual overload and provide a flexible yet intuitive user interface, Smarter Objects combines the best of both styles by using a GUI for understanding and programming and a TUI for day to day operation. Smarter Objects uses Augmented Reality technology to provide a flexible GUI for objects when that is needed.
For augmented reality (AR) to reach its potential, AR content from multiple distinct sources must be simultaneously displayed in a more unified manner than is possible given today's application-centric environments. AR browsers and AR-enabled Web browsers point toward the functionalities that OSs must incorporate to fully support AR content. Also, application developers need richer forms of content describing the physical world and the objects in it. Standards such as ARML (Augmented Reality Markup Language) 2.0 have begun providing the glue needed to bind AR content to the physical world.
The fundamental idea behind the three-dimensional display is to present the user with a perspective image which changes as he moves. The retinal image of the real objects which we see is, after all, only two-dimensional. Thus if we can place suitable two-dimensional images on the observer's retinas, we can create the illusion that he is seeing a three-dimensional object. Although stereo presentation is important to the three-dimensional illusion, it is less important than the change that takes place in the image when the observer moves his head. The image presented by the three-dimensional display must change in exactly the way that the image of a real object would change for similar motions of the user's head. Psychologists have long known that moving perspective images appear strikingly three-dimensional even without stereo presentation; the three-dimensional display described in this paper depends heavily on this "kinetic depth effect."