Mirrors! Gathering, Querying, and Analyzing Fashion
and Medical Data
First Author1[0000-1111-2222-3333] and Second Author2[1111-2222-3333-4444]
1
2
Princeton University, Princeton NJ 08544, USA
Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, Germany
lncs@springer.com
Abstract. Fashion industry and market represents a paramount domain touching people all over the world; however, it lacks of transactional and analytical
tools allowing ordinary people and fashion actors to get either real time information about their fashion life trajectory and/or analytical ones about the fashion market and industry evolving. Inspired from the Snow-White fairy tale, the
objective of this work is to design a fashion pervasive system related to multiple
participating persons providing their multimedia data captured through smart
mirrors. Captured data, related essentially to fashion images and video augmented by users’ annotation, is then organized and stored locally for users’
transactional querying and secured and centralized into a data warehouse and/or
data mart for enhanced analysis and mining. Transactional querying permits to
ordinary users to get information about their clothing habits and history. Analysis and mining allow fashion enterprises to get insights and predictions about
the fashion market and industry evolution.
Keywords: Ubiquitous Computing, Data Analytics, Data Science.
1
Introduction
In the very Snow-White famous fairy tale [1], “every morning, the Evil Queen asked
the magic mirror the question ‘Magic mirror in my hand, who is the fairest in the
land?’. The mirror continuously answers: ‘My Queen, you are the fairest in the land.’
The Queen is constantly satisfied with that, since the magic mirror never tricks. But,
when Snow White ranges the age of seven, she becomes good-looking and even more
attractive than the Queen and when the Queen enquires her mirror, it replies: ‘My
Queen, you are the fairest here so true; nonetheless Snow White is a thousand times
more beautiful than you.’.
Nowadays, the mirror is considered as our inseparable companion; it is present, not
only, in a static way, in our bed, bath and living rooms, but also, in a mobile manner,
in our bags, in elevators, in transportation means, etc. Mirrors evolved with the advent
of pervasive and positioning technologies. In fact, equipped by sensors and locating
technologies, they may capture a huge conventional and context data about us and our
surrounding environment, and spatio-temporal data describing our positions in our
perpetual motions. These data can be then cleaned, organized, stored, analyzed, and
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mined to serve as an interesting platform useful for applications going from fashion to
medical decision-making purposes.
If we go back to the tale presented in the beginning of this document, we can deduce
that at least two persons were involved (The Evil Queen and Snow White). The query
issued by Evil Queen to the mirror (‘Magic mirror in my hand, who is the fairest in
the land?’), indicates that the previous mirror is mobile (‘Magic mirror in my hand
…’), and include a spatial information (‘… who is the fairest in the land?’). The mirror provides two concise answers stating that the Evil Queen is the fairest (‘My
Queen, you are the fairest here so true’), and Snow White is the most beautiful (‘But
Snow White is a thousand times more beautiful than you.’). The first answer, of the
previous query, also, include a spatial information (‘… here …’); the word ‘here’
refers to the land where the Evil Queen and Snow White, live.
The information provided by Internet of Things (IoT) technologies [3][4] connected
with nowadays smart and ubiquitous mirrors or present in their close environment
will permit to better understand customers’ needs and fashion and/or medical product
usage. This will allow to improve customer service; optimize product assortment and
customize appropriate recommendations; to improve design, pattern and shape; to
have more insights to innovate; to track and trace. Tracking clienteles’ activities allows the creation of clothes according where do you go (e.g. fancy restaurant, famous
night-club, neighborhood, marriage ceremony, etc.), and weather conditions, and
recommend additional products such as diamonds, a modern style, a specific color,
etc. Generated data can be, also, integrated into healthcare applications, allowing
users to have a better understanding of their own health while providing doctors with
valuable insights.
The aim of this work is multiple; it aims to design a pervasive mirrors’ network related to multiple participating persons, to gather information from the previous network,
and to query and visualize transactional and analytic information related to the previous gathered data.
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Ubiquitous Mirrors’ System Design
The ubiquitous mirrors’ system, we propose in this paper, is composed, mainly, by
mobile and static smart mirrors connected through various kinds of networks such as
personal, classical or wireless local, metropolitan, wide, storage, campus area, system
area networks and/or virtual or enterprise internal private. Smart mirrors are assigned
to volunteers accepting to keep their information in the system, according some privacy rules, in order to be managed, analyzed and mined. Each smart or aware mirror [2]
is designed to capture and store texts, images and videos about the participating users.
Moreover, a mirror is equipped with sensors able to detect regular and irregular vital
signs such as person temperature, heart rate, diabetes, skin characteristics, arthritis
symptoms, etc. Mirrors are also connected to things of everyday life such as smart
lipsticks, hairbrush, dermo care microneedle etc., and furnished with sensors able to
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provide and exchange various and useful information for fashion and medical domains and associated stakeholders.
The proposed system gather data, captured from mirrors’ network and connected
objects, about the participating users’ activities and contexts, and store them into local
static and/or mobile databases depending on users’ varying location. Information
stored in the previous databases are, then, integrated into a single repository or a data
warehouse for analysis and mining.
Enhanced by wearable technology, health and wellness have become a fashionable
lifestyle. Smart technology is embedded into clothing and, also, in cosmetics. The
huge amount of information gained from these connected objects, combined with
contextual data can provide useful perceptions for consumers and other stakeholders
such as retailers and designers. Smart fashion objects generate interesting data that
can be used by analytics performed by retailers and designers to create innovative
operations to improve the lifestyle choices.
In our work, we focus on two kinds of data: fashion and health data but can be extended to other various domains such as nutrition, habits, etc. For this purpose, two
ontologies domain can be created; one dealing with fashion domain, including clothing, haircuts, and make up concepts, and the other dealing with medical domain including specific diseases such as facial paralysis, obesity, hair, heart and skin problems.
Two query types are, mainly, proposed: one is transactional (see Algorithm 1), and
the other is analytic (see Algorithm 1). The first type of queries aims to authorize to
operate, in order, to allow users, participating in our ubiquitous mirror’s network, to
quickly gain access to the most significant raw information about them. Obviously,
they will be able to consult their medical and fashion history; for example, they can
ask what they wore last week, last month last year, etc., for a special event such as a
meeting, a marriage, ceremony, etc. Members can, also, enquire about their medical
state for a punctual date or during a defined period; they can display information
about their temperature, heart rate, etc.
Furthermore, we can provide to our participating users tools permitting elegance assistance that incudes style check, second opinions about their outfit, angle checks to
see themselves from various angles, viewers to visualize photos and videos of their
outfits and appearances, selectors of their favorites photos and videos, organizers of
their appearances into collections, and share with friends. Other tools can allow to
discover new brands and styles and see what colors they did wear most often. All the
above tools can be enhanced with textual and/graphical annotations to be exploited
late on by analytical tools such as machine and deep learning algorithms.
Algorithm 1. /* Transactional queries.
Input: Time-Range, Event;
Begin
1. Display what my outfit at $Time_Range in $Event;
2. Interact with the system: Display Images and Videos of $Time_Range & $Event
Begin /* Interaction
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2.1 Second opinion;
2.2 Angle check;
2.3 View videos and photos;
2.4 Select favorites;
2.5 Organize collections;
2.6 …
End /* Interaction;
End.
The second type of queries targets analytic, mining and visualization tasks; it permits
to operate, in order, to allow stakeholders to, efficiently, access to the most relevant
summarized information about people participating to the mirrors’ network and provide valuable information for medical and/or fashion decision making.
Algorithm 2. /* Analytical Queries.
Input: Time-Range, Trajectory_Type;
Begin
1. Display $Trajectory_Type at $Time_Range;
2. Interact with the system: Displays Images and Videos related to $Time_Range
& $Trajectory_Type
Begin /* Interaction
2.1 See my evolving;
2.2 See the associated dashboard;
2.3 Zoom on special trajectories;
2.4 Select favorite trajectories;
2.5 Organize trajectories;
End /* Interaction;
End.
In both types of querying systems, the visualization of the partial and/or complete life
trajectories of participating members’ fashion and/or medical is guaranteed. A trajectory describes, through spatial and temporal characteristics, the users’ evolving thanks
to multimedia data stored in distributed databases or into the single repository integrating their associated data. Both are equipped with visualization tools and arranged
according to user needs.
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Applying Analytics to Mirror’ Data
By exploiting data stored in our system and in connected environment such as social
networks, data analytics [] can play an important role in helping ordinary people and
various innovators to identify and to deal with the ever-evolving changes existing in
fashion and medical domains.
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Fashion stakeholders, participating in our system, and getting access to our data repository can collect, analyze, and mine data to have more insights about the field.
They can, also, predict customers’ needs locally and globally, manage costs, and improve shop organization and supervision, marketing strategies, and supply chain management, etc.
3.1
Data collection and Analysis
The proposed system permits, not only, the collection of data about the participating
members, but also the analyze and/or the mining of these data in order the detect their
preferences and their advancement in the fashion and/or the medical fields on which
data were collected. This analyzed and mined data will used as a paramount support
for, decision makers, in general, and designers, in particular. Those later will use
preferences’ information to create and suggest items that customers may love according to the analyses performed on their choices materialized by the available multimedia data in the system.
Retailers can use our huge repository with the help of cognitive computing to simulate
human thought process in order to discover more about what customers might want.
More than tracking customer behavior and understanding the patterns, data analytics
may, also, improve the design and management of shops and department stores.
Companies may conduct habits analytics according to geo-spatial information such as
participants’ locations combined with label fashions based on type, style, color fabric
and other details during the analysis and mining of our data warehouse. Analyzing
this data may reveal the current runway trends such as season and/or style dominance
of clothes.
3.2
Predictions from Data
Even before designing clothes, designers can predict styles that customers would like.
For this purpose, specific algorithms, taking as input various, customers identified
behaviors and other appropriate parameters, can go through the system’ multimedia
organized data and its associated annotations, and generate recommendations based
on extensive style types associated with members’ preferences. Currently, major
investors practice predictive analytics to produce, what is known, as an actionable
product intelligence []. This help to determine the impact of different changes in
product manufacturing and design features on the customer response to an object of
fashion. According to customer responses, fashion investors produce similar products
with higher rapidity and accuracy and minimize failures with the targeted clienteles.
Data science helps the fashion industry with various predictive algorithms to make
cleverer business decisions; for example, fashion tendencies can be forecasted to tell
customers whether the up-to-date lines will get a decent response or not.
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3.3
Costs’ Management
To manage costs fashion participants are, more and more, whirling to data analytics
and data science to stay beside with the latest tendencies and consumer demands. In
fact, retailers must value clothes correctly, know when to reduce prices, stock adequate amounts of the right styles, colors, materials, sizes, etc., and make certain that
stores are well supplied and operate efficiently. The data made available thanks to our
participating users can be used, by data science techniques [], to understand not only
customer needs and shopping behaviors, and to estimate merchandises’ shelf-time on
e-commerce platforms or in everyday life shopping centers, but also, to back retailers
and manufacturers to evaluate various costs and to organize their manufacturing and
supply chain within suitable space and time.
3.4
Supply Chain Management and Marketing Strategies Improvement
Marketing strategies can be improved, through the mining of available data generated
by the members participation, with the use of advanced models and the conversion of
the output into ways easily understandable by normal people. Data Science uses collected data, processes it and gives actionable insights that can potentially advance the
fashion industry. In fact, a lot of data is made available, not only from our system, but
also, from other sources such as on social media posts, tendencies on Instagram, Facebook, Twitter, etc., where we can find out what celebrities are wearing, what people
are buying and much more.
By using analytics, fashion merchants may have more elasticity in their supply chain
awareness. Better precision with in-season management can be facilitated by using
modern analytics solutions to derive insights and optimize the product, promotion,
pricing, placement, and people chains.
3.5
Geographically Awareness
Enormous volumes of clothing are manufactured for different demographics worldwide varying in cultures and consequently in lifestyles. When something is at such a
huge scale, something as simple as size, body shape, color preferences and quantity
will vary greatly from one geographic space to another. Data science support such
kind of enterprises to produce the precise quantity and types of clothes while minimizing surplus. This is performed thanks to the gathering of members’ purchases, in
various locations such as specific cities, countries or regions, and classifying them in
different groupings such as age, gender, price range, popular and trending colors, and
other specific parameters important to the specific business. Moreover, in any season,
enterprises will be able to predict the quantities, associated to the types of clothing,
which will be sold in any specific geography in the world. This model can save them
enormous costs by reducing the number of overproduced products and by outsourcing
those latter in the appropriate locations. These previous tasks and many others are
performed through the mining, of the large amounts of structured and unstructured
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data, stored in our system, in order to identify patterns leading to well up-to-date decisions.
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Conclusion
Fashion industry and market represents a paramount domain touching people all over
the world; however it lacks of transactional and analytical tools allowing ordinary
people and fashion actors to get either real time information about their fashion life
trajectory and/or analytical ones about the fashion market and industry evolving. Inspired from the Snow-White fairy tale, we proposed in this work a design a fashion
pervasive system related to multiple participating persons providing their multimedia
data captured through smart mirrors. Captured data, related essentially to fashion
images and video augmented by users’ annotation, was then organized and stored
locally for users’ transactional querying and secured and centralized into data warehouse and/or data marts for enhanced analysis and mining. Transactional querying
permits to ordinary users to get information about their clothing habits trajectories.
Analysis and mining allow fashion enterprises to get insights and predictions about
the fashion market and industry evolution.
Various data models could be created to address many other specific problems in the
fashion industry to cover topics ranging from predictive algorithms, visual search,
capturing structured data from photographs, natural language processing and many
more.
References
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