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Background Resource-awareness Context-awareness Screen Clutter-awareness
Mobile Data Stream Mining
(Foundations)
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 (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
1 Background
2 Resource-awareness
3 Context-awareness
4 Screen Clutter-awareness
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
1 Background
2 Resource-awareness
3 Context-awareness
4 Screen Clutter-awareness
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Introduction to Data Streams - Big Data
The advances in data acquisition hardware, and the emergence
of applications that process continuous flow of high ‘velocity’
data records have led to the data stream phenomenon.
A data stream is a continuous, rapid flow of data that
challenge our state-of-the-art processing and communication
infrastructure.
Data streams have led to the emergence of Big Data
The general features of data streams are:
Very high rate input data
Read only once by an algorithm
Real time processing demand
Unbounded
Time varying
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Smartphones are really smart!
With continuous advances in
computational power and communication
abilities for smartphones and tablet
computers; and
The sheer amounts of data streams that
we subscribe to or acquire using the
onboard sensing capabilities
There is an unprecedented opportunity to
perform complex data analysis tasks that
can benefit mobile users
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Internet of Things – Sensors Are Everywhere
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Mobile Data Mining: A Definition
Running data mining techniques onboard smartphones
utilising the continuous increase of the processing power of
these devices.
Two generations for Mobile Data Mining:
In a client server architecture, the mobile phone is used as a
knowledge presenter (MobiMine 2002)
Data mining is performed onboard the mobile phone (VEDAS
2004, OMM 2009, PDM 2010, MARS 2012, and STAR 2015)
Two computational modes according to the application:
Single-node (centralised)
Multiple-node (distributed)
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Compelling Applications
Environment Monitoring and Emergency/Disaster Management
Enabling real-time decision making
Healthcare
Patient monitoring
Emergency/Triage management
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Compelling Applications
Intelligent Transportation Systems
Intersection safety
Safety at curves
Driver vigilence decline detection
Real-Time Business Intelligence
Dynamic management of courier pick up/drop off
Mobile policing
Task allocation in taxis
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Challenges for Data Mining in Mobile/Embedded
Environments
Data as a continuous stream
Resource constraints
Iterative nature of learning algorithms
Application constraints
Real-time decision making needs
Intermittent connectivity
Different applications, different types of analysis
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Data Stream Mining in Mobile/Pervasive Environments
Cost-efficient, Intelligent and Real-time Data Stream Mining
techniques that can:
adapt to the context of diverse applications
cope with and leverage distributed computational platforms
take into account available resources
take into account presentation needs
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Systems and Architectures - for Mobile/Embedded Data
Stream Mining
MobiMine @ UMBC
VEDAS Vehicle Data Stream Mining @ UMBC / Agnik
Situation-Aware Adaptive Data Stream Processing @ Monash
University + Collaborators
Resource-awareness
Context-awareness
Screen clutter-awareness
Pocket Data Mining @ University of Portsmouth
Distributed
Mobile agent-based
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Situation-awareness – The Big Picture
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
1 Background
2 Resource-awareness
3 Context-awareness
4 Screen Clutter-awareness
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Algorithm Granularity / Resource-awareness
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Algorithm Granularity – Notation
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Algorithm Granularity – Rule
The Master Rule for Algorithm Granularity
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Algorithm Granularity – Procedure
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
AIG and AOG Demonstrated
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Algorithm Granularity in Action (r = ‘memory’)
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Algorithm Granularity in Action (r = ‘battery’)
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Algorithm Granularity-based Techniques
Clusterers
Light-Weight Clustering
RA-Cluster and DRA-Cluster
RA-VFKM
Change Detection
CHANGE-DETECT
Classifiers
Light-Weight Class (LWClass)
RA-Class and DRA-Class
Frequent Items and Associations
LWF (Light-Weight Frequent Items)
HiCoRE (Highly Correlated Energy-Efficient Rules)
Time-Series Analysis
RA-SAX
RA-HOT SAX
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
RA-Cluster – Example of an Algorithm Granularity-based
Technique
RA-Cluster is an incremental online clustering algorithm that
has all the required parameters to enable resource-awareness.
Memory adaptation is done through threshold adaptation and
outlier and inactive cluster elimination.
CPU adaptation is done through randomised assignment.
Battery adaptation is done through the change in sampling
rate.
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
RA-Cluster Algorithm
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Memory Adaptation in RA-Cluster
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Battery Adaptation in RA-Cluster
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Processor Adaptation in RA-Cluster
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
1 Background
2 Resource-awareness
3 Context-awareness
4 Screen Clutter-awareness
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
What is Context?
The interrelated conditions in which something exists or
occurs (Merriam Webster)
The situation within which something exists or happens, and
that can help explain it (Cambridge Dictionary)
Any information that can be used to characterise the
situation of an entity (Dey, 1999)
The set of environmental states and settings that either
determines an applications behaviour or in which an
application event occurs and is interesting to the user (Chen,
Kotz, 2000)
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Fuzzy Situation Inference – Context-awareness
Capture applications ‘Situation’
Fuzzy Context Spaces
Enhance probabilistic situation inferencing with fuzziness
Cope with changing situations
Cope with unknown situations
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Context Spaces
The CS model provides heuristics developed specifically for
addressing context-awareness under uncertainty
Individual significance (i.e. weight) and contribution of context
attributes in the situation space
Inaccuracies of sensory originated information
Characteristics of context attributes and their effect on
reasoning
Partial and complete containment of context-attributes values
in the situation space
These heuristics are integrated into reasoning formulae that
are utility-based data fusion algorithms and compute the
confidence level in the occurrence of a situation
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Fuzzy Situation Inference
FSI model integrates fuzzy logic principles into the Context
Spaces (CS) model using the benefits of fuzzy logic for
modeling and reasoning about vague and uncertain situations
while incorporating the CS models underlying theoretical basis
for supporting context-aware and pervasive computing
environments
Example (CS vs. FSI)
CS:
SBP > 85and ≤ 135, DBP > 60and ≤ 110, HR > 45and ≤ 85
FSI: If SBP is normal and DBP is normal and HR is normal then
situation is healthy
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Situation-awareness
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
The Concept of Criticality
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Adaptation Cases
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Situation-aware Strategy
Adjusting Parameters
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Integrated Strategy
Adjusting Parameters
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Open Mobile Miner (OMM)
Easy deployment of mobile data mining applications on a
range of mobile devices
Facilitation integration of new and existing data stream
mining algorithms
Interface with a range of input sources for data streams
Allow flexible, application specific visualisations to be
developed.
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
OMM – Extensible Design
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
OMM – Demo
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
1 Background
2 Resource-awareness
3 Context-awareness
4 Screen Clutter-awareness
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Adaptive Clutter Reduction (ACR)
Corollary I
Corollary II
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Intuition Behind ACR
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Clutter-Aware Clustering Visualiser (CACV) – Notation
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
CACV Algorithm
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
CACV in Action
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
CACV – Demo
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Interactivity – iCACV
Dynamic setting of visualisation thresholds
Selective focusing
Controlling the clusters growth
Audio feedback for off-screen objects
Screen fencing of clusters
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
iCACV Demo I
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
iCACV Demo II
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
CACV for Stock Market Visualisation Demo
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Summary
Mobile data mining has emerged due to technological
advances and application needs.
Resource constraints have been a stumbling block
Developments followed the following path
Resource-awareness (Algorithm Granularity)
Context/Situation-awareness (Fuzzy Situation Inference)
Integration between RA and SA.
Adaptive Clutter Reduction (ACR)
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Some References
Gaber, M. M., Gama, J., Krishnaswamy, S., Gomes, J. B., & Stahl, F. (2014).
Data stream mining in ubiquitous environments: state-of-the-art and current
directions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge
Discovery, 4(2), 116-138.
Haghighi, P. D., Krishnaswamy, S., Zaslavsky, A., Gaber, M. M., Sinha, A., &
Gillick, B. (2013). Open mobile miner: a toolkit for building situation-aware
data mining applications. Journal of Organizational Computing and Electronic
Commerce, 23(3), 224-248.
Gaber, M. M., & Philip, S. Y. (2006). A holistic approach for resource-aware
adaptive data stream mining. New Generation Computing, 25(1), 95-115.
Gaber, M. M., Krishnaswamy, S., Gillick, B., AlTaiar, H., Nicoloudis, N., Liono,
J., & Zaslavsky, A. (2013). Interactive self-adaptive clutter-aware visualisation
for mobile data mining. Journal of Computer and System Sciences, 79(3),
369-382.
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Acknowledgements
Prof. Arkady Zaslavsky
Dr. Shonali Krishnaswamy
Prof. Philip S. Yu
Dr Suan Khai Chong
Dr Pari Delir Haghighi
and many other researcher assistants
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)
Background Resource-awareness Context-awareness Screen Clutter-awareness
Q & A
Thanks for listening!
Contact Details
Dr Mohamed Medhat Gaber
E-mail: m.gaber1@rgu.ac.uk
Webpage: http://mohamedmgaber.weebly.com/
LinkedIn: https://www.linkedin.com/profile/view?id=21808352
Twitter: https://twitter.com/mmmgaber
ResearchGate:
https://www.researchgate.net/profile/Mohamed Gaber16?ev=prf highl
Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University
Mobile Data Stream Mining (Foundations)

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Mobile Data Stream Mining (Foundations)

  • 1. Background Resource-awareness Context-awareness Screen Clutter-awareness Mobile Data Stream Mining (Foundations) 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 (Foundations)
  • 2. Background Resource-awareness Context-awareness Screen Clutter-awareness 1 Background 2 Resource-awareness 3 Context-awareness 4 Screen Clutter-awareness Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 3. Background Resource-awareness Context-awareness Screen Clutter-awareness 1 Background 2 Resource-awareness 3 Context-awareness 4 Screen Clutter-awareness Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 4. Background Resource-awareness Context-awareness Screen Clutter-awareness Introduction to Data Streams - Big Data The advances in data acquisition hardware, and the emergence of applications that process continuous flow of high ‘velocity’ data records have led to the data stream phenomenon. A data stream is a continuous, rapid flow of data that challenge our state-of-the-art processing and communication infrastructure. Data streams have led to the emergence of Big Data The general features of data streams are: Very high rate input data Read only once by an algorithm Real time processing demand Unbounded Time varying Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 5. Background Resource-awareness Context-awareness Screen Clutter-awareness Smartphones are really smart! With continuous advances in computational power and communication abilities for smartphones and tablet computers; and The sheer amounts of data streams that we subscribe to or acquire using the onboard sensing capabilities There is an unprecedented opportunity to perform complex data analysis tasks that can benefit mobile users Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 6. Background Resource-awareness Context-awareness Screen Clutter-awareness Internet of Things – Sensors Are Everywhere Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 7. Background Resource-awareness Context-awareness Screen Clutter-awareness Mobile Data Mining: A Definition Running data mining techniques onboard smartphones utilising the continuous increase of the processing power of these devices. Two generations for Mobile Data Mining: In a client server architecture, the mobile phone is used as a knowledge presenter (MobiMine 2002) Data mining is performed onboard the mobile phone (VEDAS 2004, OMM 2009, PDM 2010, MARS 2012, and STAR 2015) Two computational modes according to the application: Single-node (centralised) Multiple-node (distributed) Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 8. Background Resource-awareness Context-awareness Screen Clutter-awareness Compelling Applications Environment Monitoring and Emergency/Disaster Management Enabling real-time decision making Healthcare Patient monitoring Emergency/Triage management Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 9. Background Resource-awareness Context-awareness Screen Clutter-awareness Compelling Applications Intelligent Transportation Systems Intersection safety Safety at curves Driver vigilence decline detection Real-Time Business Intelligence Dynamic management of courier pick up/drop off Mobile policing Task allocation in taxis Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 10. Background Resource-awareness Context-awareness Screen Clutter-awareness Challenges for Data Mining in Mobile/Embedded Environments Data as a continuous stream Resource constraints Iterative nature of learning algorithms Application constraints Real-time decision making needs Intermittent connectivity Different applications, different types of analysis Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 11. Background Resource-awareness Context-awareness Screen Clutter-awareness Data Stream Mining in Mobile/Pervasive Environments Cost-efficient, Intelligent and Real-time Data Stream Mining techniques that can: adapt to the context of diverse applications cope with and leverage distributed computational platforms take into account available resources take into account presentation needs Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 12. Background Resource-awareness Context-awareness Screen Clutter-awareness Systems and Architectures - for Mobile/Embedded Data Stream Mining MobiMine @ UMBC VEDAS Vehicle Data Stream Mining @ UMBC / Agnik Situation-Aware Adaptive Data Stream Processing @ Monash University + Collaborators Resource-awareness Context-awareness Screen clutter-awareness Pocket Data Mining @ University of Portsmouth Distributed Mobile agent-based Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 13. Background Resource-awareness Context-awareness Screen Clutter-awareness Situation-awareness – The Big Picture Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 14. Background Resource-awareness Context-awareness Screen Clutter-awareness 1 Background 2 Resource-awareness 3 Context-awareness 4 Screen Clutter-awareness Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 15. Background Resource-awareness Context-awareness Screen Clutter-awareness Algorithm Granularity / Resource-awareness Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 16. Background Resource-awareness Context-awareness Screen Clutter-awareness Algorithm Granularity – Notation Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 17. Background Resource-awareness Context-awareness Screen Clutter-awareness Algorithm Granularity – Rule The Master Rule for Algorithm Granularity Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 18. Background Resource-awareness Context-awareness Screen Clutter-awareness Algorithm Granularity – Procedure Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 19. Background Resource-awareness Context-awareness Screen Clutter-awareness AIG and AOG Demonstrated Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 20. Background Resource-awareness Context-awareness Screen Clutter-awareness Algorithm Granularity in Action (r = ‘memory’) Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 21. Background Resource-awareness Context-awareness Screen Clutter-awareness Algorithm Granularity in Action (r = ‘battery’) Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 22. Background Resource-awareness Context-awareness Screen Clutter-awareness Algorithm Granularity-based Techniques Clusterers Light-Weight Clustering RA-Cluster and DRA-Cluster RA-VFKM Change Detection CHANGE-DETECT Classifiers Light-Weight Class (LWClass) RA-Class and DRA-Class Frequent Items and Associations LWF (Light-Weight Frequent Items) HiCoRE (Highly Correlated Energy-Efficient Rules) Time-Series Analysis RA-SAX RA-HOT SAX Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 23. Background Resource-awareness Context-awareness Screen Clutter-awareness RA-Cluster – Example of an Algorithm Granularity-based Technique RA-Cluster is an incremental online clustering algorithm that has all the required parameters to enable resource-awareness. Memory adaptation is done through threshold adaptation and outlier and inactive cluster elimination. CPU adaptation is done through randomised assignment. Battery adaptation is done through the change in sampling rate. Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 24. Background Resource-awareness Context-awareness Screen Clutter-awareness RA-Cluster Algorithm Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 25. Background Resource-awareness Context-awareness Screen Clutter-awareness Memory Adaptation in RA-Cluster Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 26. Background Resource-awareness Context-awareness Screen Clutter-awareness Battery Adaptation in RA-Cluster Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 27. Background Resource-awareness Context-awareness Screen Clutter-awareness Processor Adaptation in RA-Cluster Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 28. Background Resource-awareness Context-awareness Screen Clutter-awareness 1 Background 2 Resource-awareness 3 Context-awareness 4 Screen Clutter-awareness Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 29. Background Resource-awareness Context-awareness Screen Clutter-awareness What is Context? The interrelated conditions in which something exists or occurs (Merriam Webster) The situation within which something exists or happens, and that can help explain it (Cambridge Dictionary) Any information that can be used to characterise the situation of an entity (Dey, 1999) The set of environmental states and settings that either determines an applications behaviour or in which an application event occurs and is interesting to the user (Chen, Kotz, 2000) Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 30. Background Resource-awareness Context-awareness Screen Clutter-awareness Fuzzy Situation Inference – Context-awareness Capture applications ‘Situation’ Fuzzy Context Spaces Enhance probabilistic situation inferencing with fuzziness Cope with changing situations Cope with unknown situations Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 31. Background Resource-awareness Context-awareness Screen Clutter-awareness Context Spaces The CS model provides heuristics developed specifically for addressing context-awareness under uncertainty Individual significance (i.e. weight) and contribution of context attributes in the situation space Inaccuracies of sensory originated information Characteristics of context attributes and their effect on reasoning Partial and complete containment of context-attributes values in the situation space These heuristics are integrated into reasoning formulae that are utility-based data fusion algorithms and compute the confidence level in the occurrence of a situation Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 32. Background Resource-awareness Context-awareness Screen Clutter-awareness Fuzzy Situation Inference FSI model integrates fuzzy logic principles into the Context Spaces (CS) model using the benefits of fuzzy logic for modeling and reasoning about vague and uncertain situations while incorporating the CS models underlying theoretical basis for supporting context-aware and pervasive computing environments Example (CS vs. FSI) CS: SBP > 85and ≤ 135, DBP > 60and ≤ 110, HR > 45and ≤ 85 FSI: If SBP is normal and DBP is normal and HR is normal then situation is healthy Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 33. Background Resource-awareness Context-awareness Screen Clutter-awareness Situation-awareness Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 34. Background Resource-awareness Context-awareness Screen Clutter-awareness The Concept of Criticality Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 35. Background Resource-awareness Context-awareness Screen Clutter-awareness Adaptation Cases Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 36. Background Resource-awareness Context-awareness Screen Clutter-awareness Situation-aware Strategy Adjusting Parameters Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 37. Background Resource-awareness Context-awareness Screen Clutter-awareness Integrated Strategy Adjusting Parameters Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 38. Background Resource-awareness Context-awareness Screen Clutter-awareness Open Mobile Miner (OMM) Easy deployment of mobile data mining applications on a range of mobile devices Facilitation integration of new and existing data stream mining algorithms Interface with a range of input sources for data streams Allow flexible, application specific visualisations to be developed. Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 39. Background Resource-awareness Context-awareness Screen Clutter-awareness OMM – Extensible Design Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 40. Background Resource-awareness Context-awareness Screen Clutter-awareness OMM – Demo Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 41. Background Resource-awareness Context-awareness Screen Clutter-awareness 1 Background 2 Resource-awareness 3 Context-awareness 4 Screen Clutter-awareness Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 42. Background Resource-awareness Context-awareness Screen Clutter-awareness Adaptive Clutter Reduction (ACR) Corollary I Corollary II Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 43. Background Resource-awareness Context-awareness Screen Clutter-awareness Intuition Behind ACR Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 44. Background Resource-awareness Context-awareness Screen Clutter-awareness Clutter-Aware Clustering Visualiser (CACV) – Notation Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 45. Background Resource-awareness Context-awareness Screen Clutter-awareness CACV Algorithm Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 46. Background Resource-awareness Context-awareness Screen Clutter-awareness CACV in Action Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 47. Background Resource-awareness Context-awareness Screen Clutter-awareness CACV – Demo Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 48. Background Resource-awareness Context-awareness Screen Clutter-awareness Interactivity – iCACV Dynamic setting of visualisation thresholds Selective focusing Controlling the clusters growth Audio feedback for off-screen objects Screen fencing of clusters Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 49. Background Resource-awareness Context-awareness Screen Clutter-awareness iCACV Demo I Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 50. Background Resource-awareness Context-awareness Screen Clutter-awareness iCACV Demo II Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 51. Background Resource-awareness Context-awareness Screen Clutter-awareness CACV for Stock Market Visualisation Demo Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 52. Background Resource-awareness Context-awareness Screen Clutter-awareness Summary Mobile data mining has emerged due to technological advances and application needs. Resource constraints have been a stumbling block Developments followed the following path Resource-awareness (Algorithm Granularity) Context/Situation-awareness (Fuzzy Situation Inference) Integration between RA and SA. Adaptive Clutter Reduction (ACR) Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 53. Background Resource-awareness Context-awareness Screen Clutter-awareness Some References Gaber, M. M., Gama, J., Krishnaswamy, S., Gomes, J. B., & Stahl, F. (2014). Data stream mining in ubiquitous environments: state-of-the-art and current directions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(2), 116-138. Haghighi, P. D., Krishnaswamy, S., Zaslavsky, A., Gaber, M. M., Sinha, A., & Gillick, B. (2013). Open mobile miner: a toolkit for building situation-aware data mining applications. Journal of Organizational Computing and Electronic Commerce, 23(3), 224-248. Gaber, M. M., & Philip, S. Y. (2006). A holistic approach for resource-aware adaptive data stream mining. New Generation Computing, 25(1), 95-115. Gaber, M. M., Krishnaswamy, S., Gillick, B., AlTaiar, H., Nicoloudis, N., Liono, J., & Zaslavsky, A. (2013). Interactive self-adaptive clutter-aware visualisation for mobile data mining. Journal of Computer and System Sciences, 79(3), 369-382. Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 54. Background Resource-awareness Context-awareness Screen Clutter-awareness Acknowledgements Prof. Arkady Zaslavsky Dr. Shonali Krishnaswamy Prof. Philip S. Yu Dr Suan Khai Chong Dr Pari Delir Haghighi and many other researcher assistants Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)
  • 55. Background Resource-awareness Context-awareness Screen Clutter-awareness Q & A Thanks for listening! Contact Details Dr Mohamed Medhat Gaber E-mail: m.gaber1@rgu.ac.uk Webpage: http://mohamedmgaber.weebly.com/ LinkedIn: https://www.linkedin.com/profile/view?id=21808352 Twitter: https://twitter.com/mmmgaber ResearchGate: https://www.researchgate.net/profile/Mohamed Gaber16?ev=prf highl Dr Mohamed Medhat Gaber Reader, School of Computing Science and Digital Media Robert Gordon University Mobile Data Stream Mining (Foundations)