1. Context and Application-awareness.
Context-Aware Data Discovery
Dmitry Namiot Lomonosov Moscow State
University
Manfred Sneps-Sneppe Ventspils
University College
2. • Agenda:
• Introduction
• Data sets in Wi-Fi connectivity
• SpotEx (Spot Expert) approach
• SpotEx examples
• Reality mining in SpotEx
• SpotEx vs. Foursquare, etc
• The future development
• Conclusion
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3. Introduction
• Smart phone & Wi-Fi access
point
• How to provide data to
mobile subscribers nearby Wi-
Fi access point?
• How to use wireless sensors
on smart phone for data Our initial case:
discovery?
Smart phone as a wireless sensor
• It is not about the Wi-Fi network is visible on the mobile
connectivity. It is about data There is no connection to this AP yet
discovery.
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4. Data set
• AP: SSID, MAC-address,
channel, signal strength
• Phone: MAC-address,
history of using this MAC-
address, time
• How can we link the visibility
for our data to the above
mentioned measurements? Wi-Fi network is visible on the mobile
There is no connection to this AP yet
• The simialar questions could What kind of data can we use?
be raised for Bluetooth
All this info is available for mobile
application
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5. SpotEx approach
• External database with rules
(productions)
• IF IS_VISIBLE (‘SSID1’) and
TIME_WITHIN (1pm, 2pm)
THEN {show coupon for
lunch}
• Expert systems for spots
• Mobile application requests
DB via HTTP Important note: access point, our rules
linked to, could be opened on the
• RETE algorithm for making
another mobile phone. It is like
conclusions Dynamic LBS
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6. Proximity marketing Chat for mobile visitors nearby
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7. Reality mining
• Reality mining: use logged mac-
address for dicovering mass
behavior indoor
• Extract patterns from logged
mac-addresses
• Web statistics analogue for real
places
• Use statistics in proximity
rules:
Mobile application can record
IF IS_VISIBLE(‘myshop’) AND mac-address from mobile devices and
FIRST_VISIT() THEN { ... } use this information in rules and for
Different data for the first time the discovery users behavior
and follow-up visitors
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8. SpotEx vs. Foursquare, etc
• Analogue: automatic check-in
• Mobile application
automatically check-in user’s
state against wireless
environment
• They are customized check-ins.
Rules for “badges” (as in
Foursquare) are user-defined
• “Places” could be dynamic (just
Important note: access point our rules
another phone)
linked to could be opened on the
• Check-ins in SpotEx are another mobile phone. It is like
anonymous Dynamic LBS
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9. The future development
• Replace external database with
markup in our context: encode
rules as custom attributes for
HTML5. Web intents for
processing
• Add developers API
• Use network proximity
principles from SpotEx vs.
location for social streams
integration (e.g. Twitter, etc.) and
Mobile application can record
augmented reality mac-address from mobile devices and
• Wi-Fi direct support use this information for the discovery
users behavior
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10. Conclusion
• A new model for context-aware data discovery for mobile users
developed on the ideas of Wi-Fi and Bluetooth proximity.
• Uses smart-phone as a proximity sensor. Service can use any
network node as presence trigger for delivering user-defined content
right to mobile subscribers.
• Completely software based approach. A novel implementation of
context-aware browser for mobile subscribers. Presented as mobile
application for Android platform and HTML5 web site.
•This service could be used for delivering commercial information
(deals, discounts, coupons) in malls, hyper-local news data, data
discovery in Smart City projects, personal news, etc.
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