Context-aware data access
models
(Declaration of Intent Draft)
Dmitry Namiot [email protected]
Lomonosov Moscow State University
Proposal: SkTech.RC/IT/Madnick
Contact info
• Email: [email protected]
• Phone: +7-495-939-23-59
• Address: Russia, 119991, Moscow,
Leninskie Gory, MSU, Faculty of
Computational Mathematics and
Cybernetics 2nd educational building,
room 360
Position & educationation
• Position: senior scientist of Faculty of
Computational Mathematics and
Cybernetics, Open Information Systems
Lab
• Educational background: Applied
Mathematics (B.S. and M.S.) – Moscow
Aviation Institute, Ph.D in Computer
Science – Lomonosov Moscow State
University
Research areas and past projects
• discrete simulation and statistical methods,
• compilation, grammars, domain specific languages
• knowledge management, logical chains, production systems,
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artificial intelligence, expert systems,
real time operational systems, distributed systems: CORBA, then
EJB,
telecom development: open interfaces for telco (Parlay etc.),
telecom protocols and services,
web programming APIs and internet applications,
location based systems and geo programming,
distributed databases (Hadoop etc.),
web services and semantic web,
data mining, data curation in social networks
Accomplishments and recognition
• Author or co-author of over 60 journal articles
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and 4 books.
Innovation Award at World Wide Java Cards
Development contest (3GSM World), Best on
Technology Award at World Wide Java Cards
Development contest (3GSM World),
several readers Choice Awards from computer
magazines,
several Java Developers challenges awards
Leadership and collaboration
• participates in European research projects (together with
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Rigas Technical University and Ventspils University
College - Latvia),
prepares and provides educational for European telecom
firms (Iskratel, Slovenia), reviewer for several
international conferences (ICST, IARIA),
co-founder (as technical director) of several high-tech
firms
MSU teaching: database programming, Java
programming for Internet applications.
Intentions for R&D
• Theme: “BIG DATA”: large-scale data
gathering & mining
• Research issue: data mining services that
let define context-aware actions for
delivering (discovering) data to mobile
subscribers
• Context-aware data access, context aware
browsing
Intentions for R&D
• Nowadays mobile phones are becoming
the primary source for possible data
collections.
• “phone as a sensor” concept
• It is the typical example of schema-less
big data.
• For example: environmental sensing and
behavioral.
Intentions for R&D
• What kind of information snippets could be
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shown (delivered) for mobile subscribers based
on various metrics that could be introduced for
that vast amount of data?.
The goal: provide a set of tools that let define
(develop) some actions/triggers (e.g. delivering
information to mobile phone) depending on the
collected context data in the real time.
In general it leads to building richer and more
personalized mobile experiences.
Intentions for R&D
• Elements are (at least):
• data collection (gathering) modules
• data persistence mechanisms
• new metrics for collected data (e.g.
proximity as a service, fuzzy logic for data
estimation etc.)
• developers API for using collected data in
applications
Example: Spot Expert
• Collected data: Wi-Fi networks info
• Metric: Wi-Fi proximity
• Result: context-aware browser where
available content if defined by the
proximity rules
• Big data processing for the next steps:
collect more sensing data, analyze data
for several subscribers, add more metrics
Relevancy
• This project addresses the following hot
areas in computing:
• M2M applications,
• mobile computing in the real word
• context-aware (ubiquitous) computing.
Novel & scope
• Context-aware computing for mobile devices is
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highly fragmented.
The amount of practical applications is very low.
There are no (almost no) development tools that
cover context-aware applications.
It covers multiple research areas: mobile OS and
SDK, big data stores for data persistence, realtime analysis for big data, modern programming
development tools and APIs, telecom standards.
Entrepreneurially promising
• Areas for the possible commercialization:
• Smart Cities projects
• distributing hyper-local news data to
mobile subscribers (e.g. commercial info
in malls, news data in campuses and office
centers),
• real world games
Education
• Educational courses that could be provided in
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the connection with this project:
mobile OS, mobile SDK,
NoSQL databases,
data patterns recognition,
big data processing.
We can develop new multi-disciplinary core
courses for sensing data analysis. These courses
will serve also as a basic point for PhD students
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Future platform for Internet of things