Essence: Pervasive & Distributed Intelligence

Pervasive & Distributed Intelligence

The Pervasive & Distributed Intelligence (Essence) research team is affiliated with the research section Information, Data and Analysis (IDA) and Networked Systems Research Laboratory (NETLAB) in the School of Computing Science. The team brings together the fundamental research areas: (i) Predictive Computing, (ii) Network-centric Information Systems, and (iii) Distributed Intelligence.

The team's strength lies in the spectrum of theoretical backgrounds and applications ranging from Stochastic Optimization, Intelligent Systems, Distributed Statistical Learning to Mobile/Wireless Networking. Essence focuses on building innovative context-aware networked data systems and applications.

The team's expertise is in mobile computing, context-awareness, real-time information processing, and distributed stochastic optimization. Essence vision is to build network-centric data systems and applications reasoning about knowledge.


Note: The Essence's logo is a regular dodecahedron, i.e., a polyhedron with twelve flat faces (Platonic solid), whose coordinates derive from the multiplicative inverse of Pythagora's golden ratio.

Research Areas & Outcome

Essence in a Nutshell

  • Network-centric Information Systems: in-network contextual data processing from information captured by wireless/mobile sensor networks and resource-constrained networks.

  • Predictive Computing: large-scale inferential analytics and predictive intelligence at the network edge.

  • Distributed Intelligence: distributed context-/situation-aware computing adopting real-time stochastic optimization.

    • Ongoing Projects

  • Predict the Answer: Large-scale Data-less Computing
  • Data Relevance: Thinning the Big Data
  • Edge Computing: Optimal Task Offload & Allocation
  • Edge-centric Analytics: Time-optimized Statistical Modelling
  • Bio-inspired Predictive Analytics: Swarm Intelligence in Large-scale Regression

    • Check for Postgraduate Research & PhD Opportunities. Contact: Dr Christos Anagnostopoulos and Dr Dimitrios Pezaros

    NEWS



    • EU MSCA 2019

      European Research Council MSCA Meeting

      Join our talk in ERC/MSCA Brussels 17-18, June 2019: 'AI in Europe'.

      • Kolomvatsos, K., Anagnostopoulos, C., INNOVATE [PDF]

    • ACM SIGMOD 2019

      2019 ACM SIGMOD/POD; Amsterdam, NL

      Poster presentation at ACM SIGMOD 2019.

      • F. Savva Query-Driven Learning for Next Generation Predictive Modelling and Analytics [PDF]

    • SICSA PhD Conference 2019

      SICSA PhD Conference 2019

      Poster presentation at SICSA PhD Conference 2019, University of Stirling, 18-19 June 2019.

      • I. Alghamdi, C. Anagnostopoulos, D. Pezaros. Time-Optimized Task Offloading Decision Making in Mobile Edge Computing

    • Grenoble INP Ensimag

      Internship: Grenoble INP Ensimag

      We welcome Mr V Orru from U Grenoble Ensimag in Essence!

      • Internship Research Topic:Application of Optimal Stopping Time in Large-scale Data Streams

    • IEEE ISCC 2019

      2 x IEEE ISCC 2019 Papers

      Join our paper presentations at IEEE ISCC 2019 Conference, Barcelona, Spain 2019.

      • Sagkriotis, S., Anagnostopoulos, C., Pezaros, D. Energy Usage Profiling for Virtualized Single Board Computer Clusters.

      • Sagkriotis, S., Kolomvatsos, K., Anagnostopoulos, C., Pezaros, D., Hadjiefthymiades, S. Knowledge-centric Analytics Queries Allocation in Edge Computing Environments.

    • IEEE ISPDC 2019

      IEEE ISPDC 2019 Paper

      Join our paper presentations at IEEE ISPDC 2019 Conference, Amsterdam, 2019 (18th International Symposium on Parallel and Distributed Computing).

      • E. Aleksandrova, C. Anagnostopoulos, K. Kolomvatsos, Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach Funded by: H2020/INNOVATE.

    • IEEE WD 2019

      IEEE WD 2019 Best Paper Runner Up Award

      In recognition of the technical merit and significance of our paper: Alghamdi, I., Anagnostopoulos, C., Pezaros, D., 'Time-optimized Task Offloading Decision Making in Mobile Edge Computing' @ 11th IEEE Wireless Days 2019, Manchester.

    • MobiUK 2019

      2nd MobiUK 2019

      We are presenting: Alghamdi, I., Anagnostopoulos, C., Pezaros, D., 'Time-optimized Task Offloading Decision Making in Mobile Edge Computing' @2nd UK Mobile, Wearable and Ubiquitous Systems Research Symposium 1st-2nd July 2019, Dept of Computer Science, University of Oxford, UK.

    • INFOCOM 2018

      IEEE INFOCOM

      Join our paper presentation at IEEE INFOCOM 2018 Conference, Honolulu, HI, USA.

      • R. Cziva, C. Anagnostopoulos, D. Pezaros 'Dynamic, Latency-Optimal vNF Placement at the Network Edge'.EPSRC,EU/COST;EU/GNFUV [paper]

    • Wirelss Days 2019

      2 x IEEE WD'19 Papers

      Join our paper presentations at IEEE WD'19 Conference, Manchester, UK, 24-24 April 2019.

      • Alghamdi, I., Anagnostopoulos, C., Pezaros, D. 'Time-Optimized Task Offloading Decision Making in Mobile Edge Computing'.

      • Nikolaou, S., Anagnostopoulos, C., Pezaros, D. In-network Predictive Analytics in Edge Computing.

    • Paper

      Journal article in J. Parallel and Distirbuted Computing

      Kolomvatsos, K. An efficient scheme for applying software updates in pervasive computing applications. Funded by: H2020/INNOVATE.

    • Paper

      Tutorial: K Aleksandrova; Title: Statistical Models Update in Edge Computing using Optimal Stopping Theory & Concept Drift

      Essence Research Group Tutorial [12th April 2019@SoCS/SAWB 303]



    • IEEE BigData 2018

      2 x IEEE BigData 2018 Papers

      Join our paper presentations at IEEE BigData 2018 Conference, Seattle, US; Dec 10-13, 2018. Submissions: 1100 papers; Acceptance rate 18%.

      • Cahsai, A. S., Anagnostopoulos, C. , Ntarmos, N. and Triantafillou, P. Revisiting Exact kNN Query Processing with Probabilistic Data Space Transformations.

      • Savva, F. , Anagnostopoulos, C. and Triantafillou, P. Explaining Aggregates for Exploratory Analytics.

    • Best Student Paper Award

      Best Student Paper Award @ IEEE BigData 2018

      Awarded Paper: Cahsai, A. S., Anagnostopoulos, C. , Ntarmos, N. and Triantafillou, P. Revisiting Exact kNN Query Processing with Probabilistic Data Space Transformations.

    • ACM SIGMOD 2019

      ACM SIGMOD SRC 2019 Award

      Awarded Paper: Savva, F. Query-Driven Learning for Next Generation Predictive Modeling & Analytics at SIGMOD 2019 ACM Student Research Competition (SRC) sponsored by Microsoft.

    • Paper

      Journal Article in J Data Science & Analytics

      Anagnostopoulos, C. and Triantafillou, P. Large-scale predictive modelling & analytics through regression queries in data management systems. Funded by: H2020/GNFUV.



    • IEEE WiMob 2018

      IEEE WiMob 2018

      Join our paper presentation at IEEE WiMob 2018 Conference, Cyprus, 2018.

      • Panagidi, K., Galanis, I., Anagnostopoulos, C., Hadjiefthymiades, S. Time-Optimized Contextual Information Flow on Unmanned Vehicles.; EU/RAWFIE;EU/GNFUV

    • IEEE ICTAI 2018

      Join our paper presentation at IEEE ICTAI 2018 Conference, Greece, 2018.

      • K. kolomvatsos, C. Anagnostopoulos An Edge-Centric Ensemble Scheme for Queries Assignment.; EU/INNOVATE

    • IEEE EDGE 2018

      Join our paper presentation at IEEE EDGE 2018 Conference, CA, USA, 2018.

      • N. Harth, C. Anagnostopoulos Edge-centric Efficient Regression Analytics.; EU/GNFUV

    • IEEE ICC 2018

      Join our paper presentation at IEEE ICC 2018 Conference, Kansas City, MO, USA, 2018.

      • A. Ali, C. Anagnostopoulos. D. Pezaros On the Optimality of Virtualized Security Function Placement in Multi-Tenant Data Centers .; EPSRC; EU/COST; EU/GNFUV



    • Journal article in Wireless Networks; H2020/RAWFIE

      T. Kontos, C. Anagnostopoulos, E. Zervas, S. Hadjiefthymiades, 'Adaptive epidemic dissemination as a finite-horizon optimal stopping problem'. [paper]

    • Journal article in Computer Communications; H2020/RAWFIE

      K.Panagidi, C.Anagnostopoulos, S.Hadjiefthymiades'Optimal Grouping-of-Pictures in IoT video streams'. [paper]

    • Journal article in Applied Intelligence; H2020/GNFUV

      C. Anagnostopoulos, K. Kolomvatsos, 'Predictive intelligence to the edge through approximate collaborative context reasoning'. [paper]

    • Journal article in Applied Intelligence; H2020/GNFUV

      C. Anagnostopoulos, F. Savva, P. Triantafillou, Scalable aggregation predictive analytics: A query-driven machine learning approach. [paper]