- Data analytics for processing dynamic real world data
This research theme is focused on developing adaptable machine learning
algorithms and data analytics methods for processing sensory data from
the Internet of Things and social media (citizen sensing). The research
resulted in a toolkit for analysing Internet of Things data streams and
an open-source software for extracting city events from social media
data (in collaboration with the Kno.e.sis
team at the Wright State University).
- Selected publications:
- P. Anantharam, P. Barnaghi, K. Thirunarayan, A.
city events from social streams," ACM Transactions on Intelligent
Systems & Technology, January 2015.
- F. Ganz, D. Puschmann, P. Barnaghi, F. Carrez, "A Practical
Evaluation of Information Processing and Abstraction Techniques for the
Internet of Things", IEEE Internet of Things Journal, Accepted for
publication, January 2015.
- F. Ganz, P. Barnaghi, F. Carrez, "Automated semantic knowledge
acquisition from sensor data", IEEE Systems Journal, September 2014.
- P. Barnaghi, A. Sheth, C. Henson, "From
Data to Actionable Knowledge: Big Data Challenges in the Web of Things,”
IEEE Intelligent Systems, vol.28, no.6, pp.6,11, Nov/Dec. 2013.
- F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for
Heterogeneous Real World Internet Data", IEEE Sensors Journal, vol.
13, no. 10, pp. 3793-3805, 2013.
- Semantic annotation and linked-data for the Internet of
This research theme focuses on developing interoperability models and
semantic annotation frameworks for the Internet of Things (IoT). We
have designed and developed a linked-data platform for describing IoT
data. This has been one of the early works in using linked-data models
for the IoT. We have also been involved in the W3C incubator group on
Semantic Sensor Networks. Some of the key results and publications from
this research are presented in recent publications and software
- Selected publications
- P. Barnaghi, W. Wang, C. Henson, K. Taylor, "Semantics for the Internet of
Things: early progress and back to the future", Int. Journal on
Semantic Web and Information Systems, vol. 8, issue 1, 2012.
- M. Compton, P. Barnaghi, L. Bermudez, R. Garcia-Castro,
O. Corcho, S. Cox, J. Graybeal, M. Hauswirth, C. Henson, A. Herzog, V.
Huang, K. Janowicz, W. D. Kelsey, D. Le Phuoc, L. Lefort, M. Leggieri,
H. Neuhaus, A. Nikolov, K. Page, A. Passant, A. Sheth, K. Taylor. "The SSN Ontology of the W3C
Semantic Sensor Network Incubator Group"', Journal of Web
Semantics, Elsevier, 2012.
- S. De, T. Elsaleh, P. Barnaghi, S. Meissner, "An Internet of Things
Platform for Real-World and Digital Objects", Journal of Scalable
Computing: Practice and Experience, vol 13, no.1, May 2012.
- Large-scale Service and Data Discovery
This research is focused on developed novel methods for large-scale
data and service discovery. We have developed a simulation environment
for large-scale Internet of Things networks and distributed service
platforms. The service discovery method was developed using a
probabilistic machine learning approach that is able to identify
implicit relations between the service descriptions. This method relies
on an unsupervised learning method and can improve the performance of
discovery of large-scale semantically annotated services with a
significant rate. This probabilistic method outperformed the existing
graph based and logical query methods that are used for search and
discovery of semantic services in the existing solutions. For the data
discovery, we used a probabilistic machine learning approach to index
very large-scale semantically described IoT data. We have also
developed a compensation mechanism for updating the indexing model
which made the solution significantly scalable and computationally
effective for dynamic environments such as the IoT.
- Selected publications
- G. Cassar, P. Barnaghi, K. Moessner, "Probabilistic Matchmaking
Methods for Automated Service Discovery", IEEE Transactions on
Services Computing, 2013.
- A. HosseiniTabatabaie, P. Barnaghi, C. Wang, L. Dong,
R. Tafazolli, "Method and Apparatus for Scalable Data Discovery in IoT
Systems", Patent filed (provisional), US Patents, May 2014.
- G. Cassar, P. Barnaghi, W. Wang, K. Moessner, "Composition
of Services in Pervasive Environments: A Divide and Conquer Approach",
Proceedings of the 18th IEEE Symposium on Computers and Communications,
- IoT Configuration Manager and Resource Search and
Discovery (open source),
this component is a part of the EU Future Internet Test-bed (FI-Lab)
- Simulator for large-scale distributed sensor networks
and distributed indexing and discovery (the software is designed based
on an industry collaboration and the core algorithm is currently filed
for a patent).
- Ontology Learning and Semantic Search
This work focused on automated creation of ontologies from unstructured
text. The terminological ontologies created by our proposed method are
significantly accurate and can be directly used for semantic search and
reasoning purposes. This work also led to further development of a
semantic knowledge-base creation and search engine for discovering and
ranking scientific papers. Another interesting aspect of the work is
being domain independent that makes it applicable to different text
libraries and online resources. We also developed a ranking algorithm,
called RareRank, that uses the provided knowledge to determine
inter-relations between different entities (such as authors and topics
and organisations, etc.) to provide more meaningful search results. The
evaluation results show that RareRank outperforms Google’s PageRank in
searching scientific articles. The proposed method can also rank
journals, conferences and researchers according to research topics.
Some of the key results and publications from this research are
- Selected publication
- W. Wang, P. Barnaghi, A. Bargiela, "Rational Research Model for
Ranking Semantic Entities", Information Sciences Journal -
Elsevier, Volume 181, Issue 13, pp. 2823-2840, July 2011.
- W. Wang, P. Barnaghi, A. Bargiela, "Probabilistic
Topic Models for Learning Terminological Ontologies," IEEE
Transactions on Knowledge and Data Engineering, vol. 22, no. 7, pp.
1028-1040, April 2010.
semantic search engine (developed when I worked at the University
of Nottingham, Malaysia Campus)
(on campus access link: http://iot1.ee.surrey.ac.uk/iris2/)
- Smart City Applications and use-cases
This part of our research mainly integrates the ideas and developments
from our other research activities and is focused on demonstrating the
applications of the research in smart city and the Internet of Things
domains. The results shown below are mainly from the activities in the
EU FP7 CityPulse and previous collaborative projects and some of them
have been developed in collaboration with other partners in the
projects. Some of the key results and publications from this research
are reported in:
- Selected publications
- P. Barnaghi, M. Bermudez-Edo, R. Toenjes, "Challenges for Quality of Data in Smart Cities".
ACM Journal of Data and Information Quality, challenge paper, to
- P. Barnaghi, A. Sheth, "Internet
of Things: the story so far", IEEE IoT Newsletter, September 2014.
- P Barnaghi et al, "Digital
Technology Adoption in the Smart Built Environment: Challenges and
Opportunities of Data‐driven Systems for Building-,Community‐ and
City‐Scale Applications," IET Sector Technical Briefing, The
Institution of Engineering and Technology (IET), Technical report,
- Use-case: The CityPulse Project 101 Smart City use-cases
- Dataset: The CityPulse Project Smart City Dataset collection
- Software: CKAN to
HyperCAT data wrapper
- Software: SAOPY, Semantic annotation libraries for
sensory data (will be available soon)
- Ontology: Stream
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