1. What is new? STAR MARS MSA PDM Mobile Data Stream Mining
(Advances) Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University 21 May 2015 Dr
Mohamed Medhat Gaber Reader, School of Computing Science and
Digital Media Robert Gordon University Mobile Data Stream Mining
(Advances) 2. What is new? STAR MARS MSA PDM 1 What is new? 2 STAR
3 MARS 4 MSA 5 PDM Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 3. What is new? STAR MARS MSA PDM 1
What is new? 2 STAR 3 MARS 4 MSA 5 PDM Dr Mohamed Medhat Gaber
Reader, School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 4. What is new?
STAR MARS MSA PDM Recent Advances Smartphones have become more
ubiquitous and powerful Streaming sensory data led to the Internet
of Things (IoT) Human activity recognition has emerged as an
important application for mobile users (STAR and MARS) With
reliance on social media through smartphones, applications like
mobile sentiment analysis have emerged (MSA) Distributed and
autonomous mobile computing has become a necessity (PDM) Dr Mohamed
Medhat Gaber Reader, School of Computing Science and Digital Media
Robert Gordon University Mobile Data Stream Mining (Advances) 5.
What is new? STAR MARS MSA PDM 1 What is new? 2 STAR 3 MARS 4 MSA 5
PDM Dr Mohamed Medhat Gaber Reader, School of Computing Science and
Digital Media Robert Gordon University Mobile Data Stream Mining
(Advances) 6. What is new? STAR MARS MSA PDM STAR: Overview It
stands for STream learning for mobile Activity Recognition It
provides Dynamic incremental learning from evolving data stream
Eective active learning with lowest cost Mobile real-time AR
application Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University Mobile Data
Stream Mining (Advances) 7. What is new? STAR MARS MSA PDM STAR
System Architecture Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 8. What is new? STAR MARS MSA PDM
Modelling Component of STAR Dr Mohamed Medhat Gaber Reader, School
of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Advances) 9. What is new? STAR MARS MSA
PDM Recognition Component of STAR Dr Mohamed Medhat Gaber Reader,
School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 10. What is new?
STAR MARS MSA PDM Four Measures for Activity Recognition Distance
Gravity Density Deviation Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 11. What is new? STAR MARS MSA PDM
Adaptation Component of STAR Dr Mohamed Medhat Gaber Reader, School
of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Advances) 12. What is new? STAR MARS MSA
PDM OPPORTUNITY Dataset Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 13. What is new? STAR MARS MSA PDM
STAR Performance Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 14. What is new? STAR MARS MSA PDM 1
What is new? 2 STAR 3 MARS 4 MSA 5 PDM Dr Mohamed Medhat Gaber
Reader, School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 15. What is new?
STAR MARS MSA PDM MARS: Mobile Activity Recognition System Unlike
earlier approaches, the classier is built/updated on-board the
mobile device itself utilising data stream mining techniques. The
advantages of on-board data stream mining for mobile activity
recognition are: personalisation of models built to individual
users; increased privacy as the data is not sent to an external
site; and adaptation of the model as the users activity prole
changes. Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University Mobile Data
Stream Mining (Advances) 16. What is new? STAR MARS MSA PDM MARS
Training Process Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 17. What is new? STAR MARS MSA PDM
MARS Performance We used incremental Naive Bayes classication The
OPPORTUNITY dataset has been used Dr Mohamed Medhat Gaber Reader,
School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 18. What is new?
STAR MARS MSA PDM MARS Interface Dr Mohamed Medhat Gaber Reader,
School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 19. What is new?
STAR MARS MSA PDM 1 What is new? 2 STAR 3 MARS 4 MSA 5 PDM Dr
Mohamed Medhat Gaber Reader, School of Computing Science and
Digital Media Robert Gordon University Mobile Data Stream Mining
(Advances) 20. What is new? STAR MARS MSA PDM Mobile Sentiment
Analysis It is based on the SentiCorr system which performs
multi-lingual sentiment analysis of personal correspondence, and
correlate the inferred sentiment to stress level. Dr Mohamed Medhat
Gaber Reader, School of Computing Science and Digital Media Robert
Gordon University Mobile Data Stream Mining (Advances) 21. What is
new? STAR MARS MSA PDM Mobile Sentiment Analysis: A Wide Media
Coverage A wide media coverage including articles in the BBC, CNN,
the Independent, the DailyMail, Los Angeles Times, The Age,
Computer Weekly, Times Higher Education, and several radio
interviews. Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University Mobile Data
Stream Mining (Advances) 22. What is new? STAR MARS MSA PDM MSA
Workow SentiCorr achieves sentiment classication at the sentence
level by using POS (Position Of Speech) tagging to identify the
types of the words in the sentence; the subjectivity detection
stage then uses the POS tags to identify opinion lexicon and hence
if the sentence is subjective or objective; the polarity detection
stage also utilises the POS tags to search for patterns in the
sentence that indicate positive or negative expressions. Dr Mohamed
Medhat Gaber Reader, School of Computing Science and Digital Media
Robert Gordon University Mobile Data Stream Mining (Advances) 23.
What is new? STAR MARS MSA PDM Subjectivity Detection The principle
employed for subjectivity detection is boosting, by use of the Ada-
Boost (adaptive boosting algorithm) Features utilised are POS tags,
pre-dened lexicons that contain positive, negative and negation
words, the presence of exactly one positive word, the presence of
multiple positive words, the presence of exactly one negative word
and the presence of multiple negative words, and whenever a
positive or negative word is directly preceded by a word from the
negation list, its polarity is ipped. Dr Mohamed Medhat Gaber
Reader, School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 24. What is new?
STAR MARS MSA PDM Polarity Detection The principle employed for
polarity detection is RBEM which uses rules to dene an emissive
model. Eight rules are applied (e.g., for each positive pattern an
emission value is calculated based on the distance of the elements
in the sentence from the centre of the positive pattern) Once the
rules have been applied, every element of the sentence has an
emission value and the nal polarity of the message is calculated by
summing the emission values for each element. If the nal polarity
of the sentence is greater than zero, the sentence is positive; if
it is less than zero the sentence is negative, if it is zero the
polarity of the sentence is unknown due to insucient patterns in
the sentence model. Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 25. What is new? STAR MARS MSA PDM
MSA Performance Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University Mobile Data
Stream Mining (Advances) 26. What is new? STAR MARS MSA PDM 1 What
is new? 2 STAR 3 MARS 4 MSA 5 PDM Dr Mohamed Medhat Gaber Reader,
School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 27. What is new?
STAR MARS MSA PDM Pocket Data Mining Pocket Data Mining (PDM) is
the term we coined in 2010 to describe the collaborative mining of
streaming data in mobile and distributed computing environments.
Three technological enablers: data stream mining; mobile software
agents; and programming for small devices. Dr Mohamed Medhat Gaber
Reader, School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 28. What is new?
STAR MARS MSA PDM What is a Mobile Agent? A software program Moves
from machine to machine under its own control Suspends execution at
any point in time, transport itself to a new machine and resume
execution Once created, a mobile agent autonomously decides which
locations to visit and what instructions to perform Continuous
interaction with the agents originating source is not required
Implicitly specied through the agent code Specied through a
run-time modiable itinerary Dr Mohamed Medhat Gaber Reader, School
of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Advances) 29. What is new? STAR MARS MSA
PDM PDM Agents and Architecture PDM Agents (Mobile) agent miners
(AM): are either distributed over the network when the mining task
is initiated or are already located on the mobile device. Mobile
data stream mining Mobile agent resource discoverers (MRD): are
used to explore the available resources. Mobile cloud Mobile agent
decision makers (MADM): roam the network consulting the mobile
agent miners to collaborate in reaching the nal decision. Ensemble
learning PDM Architecture Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 30. What is new? STAR MARS MSA PDM
PDM Flowchart Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University Mobile Data
Stream Mining (Advances) 31. What is new? STAR MARS MSA PDM Simple
Weighted Majority Voting of the MADM Y = 1.75 (0.55+0.65+0.55) X =
1.80 (0.95+0.85) Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 32. What is new? STAR MARS MSA PDM
PDM Performance Each AM has access to 20%, 30%, or 40% of the
features (random vertical partitioning). Dr Mohamed Medhat Gaber
Reader, School of Computing Science and Digital Media Robert Gordon
University Mobile Data Stream Mining (Advances) 33. What is new?
STAR MARS MSA PDM PDM Performance Hoeding Trees Dr Mohamed Medhat
Gaber Reader, School of Computing Science and Digital Media Robert
Gordon University Mobile Data Stream Mining (Advances) 34. What is
new? STAR MARS MSA PDM PDM Performance Naive Bayes Dr Mohamed
Medhat Gaber Reader, School of Computing Science and Digital Media
Robert Gordon University Mobile Data Stream Mining (Advances) 35.
What is new? STAR MARS MSA PDM PDM Performance: HT and NB
(Heterogeneous) Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University Mobile Data
Stream Mining (Advances) 36. What is new? STAR MARS MSA PDM Model
Selection for MADM (Coll-Stream) It addresses concept drift issues
The Coll-Stream is a selection method that partitions the instance
space X into a set of regions R. For each region, an estimate of
the models accuracy is maintained over a sliding window. This
estimated value is updated incrementally as new labelled records
are observed in the data stream or new models are available. Dr
Mohamed Medhat Gaber Reader, School of Computing Science and
Digital Media Robert Gordon University Mobile Data Stream Mining
(Advances) 37. What is new? STAR MARS MSA PDM Coll-Stream
Performance Dr Mohamed Medhat Gaber Reader, School of Computing
Science and Digital Media Robert Gordon University Mobile Data
Stream Mining (Advances) 38. What is new? STAR MARS MSA PDM PDM
Demo Dr Mohamed Medhat Gaber Reader, School of Computing Science
and Digital Media Robert Gordon University Mobile Data Stream
Mining (Advances) 39. What is new? STAR MARS MSA PDM PDM Book Dr
Mohamed Medhat Gaber Reader, School of Computing Science and
Digital Media Robert Gordon University Mobile Data Stream Mining
(Advances) 40. What is new? STAR MARS MSA PDM Summary Smartphones
and tablets have become ubiquitous computing devices Human activity
recognition can serve many important applications in the era of IoT
MARS and STARS are two systems that provide eective and ecient
activity recognition PDM is a framework for distributed data stream
mining in the mobile environment, potentially serving a large
number of applications The eld is opening up for more contributions
Dr Mohamed Medhat Gaber Reader, School of Computing Science and
Digital Media Robert Gordon University Mobile Data Stream Mining
(Advances) 41. What is new? STAR MARS MSA PDM Some References
Gaber, M. M., Gomes, J. B., & Stahl, F. (2014). Pocket data
mining. Springer. Abdallah, Z. S., Gaber, M. M., Srinivasan, B.,
& Krishnaswamy, S. (2015). Adaptive mobile activity recognition
system with evolving data streams. Neurocomputing, 150, 304-317.
Gomes, J. B., Gaber, M. M., Sousa, P. A., & Menasalvas, E.
(2013). Collaborative data stream mining in ubiquitous environments
using dynamic classier selection. International Journal of
Information Technology & Decision Making, 12(06), 1287-1308.
Chambers, L., Tromp, E., Pechenizkiy, M., & Gaber, M. (2012,
September). Mobile sentiment analysis. In Proceedings of the 16th
International Conference on Knowledge-Based and Intelligent
Information & Engineering Systems. Gomes, J. B., Krishnaswamy,
S., Gaber, M. M., Sousa, P. A., & Menasalvas, E. (2012). Mobile
activity recognition using ubiquitous data stream mining (pp.
130-141). DaWaK. Springer Berlin Heidelberg. Dr Mohamed Medhat
Gaber Reader, School of Computing Science and Digital Media Robert
Gordon University Mobile Data Stream Mining (Advances) 42. What is
new? STAR MARS MSA PDM Acknowledgements Dr Frederic Stahl Dr Joao
Gomes Dr Zahraa Said Abdallah Dr Mykola Pechenizkiy Lorraine
Chambers Erik Tromp Prof. Philip Yu Prof. Max Bramer Prof.
Ernestina Menasalvas Dr Mohamed Medhat Gaber Reader, School of
Computing Science and Digital Media Robert Gordon University Mobile
Data Stream Mining (Advances) 43. What is new? STAR MARS MSA PDM Q
& A Thanks for listening! Contact Details Dr Mohamed Medhat
Gaber E-mail: [email protected] Webpage:
http://mohamedmgaber.weebly.com/ LinkedIn:
https://www.linkedin.com/prole/view?id=21808352 Twitter:
https://twitter.com/mmmgaber ResearchGate:
https://www.researchgate.net/prole/Mohamed Gaber16?ev=prf highl Dr
Mohamed Medhat Gaber Reader, School of Computing Science and
Digital Media Robert Gordon University Mobile Data Stream Mining
(Advances)