Essence@UoG

Pervasive & Distributed Intelligence

Essence (Essential Science) Research Lab is affiliated with the Knowledge and Data Engineering Systems within the Section: Information, Data and Analysis (IDA), School of Computing Science. The lab brings together the fundamental research areas: (i) Predictive Computing, (ii) Network-centric Information Processing, and (iii) Distributed Intelligence.

Essence lab's strength lies in the spectrum of theoretical backgrounds and applications ranging from Context-awareness, Networked Intelligent Systems, to Distributed Processing & Machine Learning. Essence focuses on building innovative context-aware networked data systems and applications. Essence Lab undertakes research ubder the School's themes: Autonomous Systems and Data Science.

Essence lab's members collaborate with the Inference, Dynamics and Interaction (IDI) as a source of Machine Learning expertise, the Networked Systems Research Laboratory (Netlab) as a source of Networking expertise, and the Intelligent Pervasive Systems (iPRISM) as a source of Intelligent Systems expertise.

Research Areas

Essence in a Nutshell

Networked Data Systems: in-network contextual data processing; wireless/mobile sensors; resource-constrained networks.

Fund:GNFUV (H2020); INNOVATE (H2020)

Predictive Computing: large-scale inferential analytics; predictive intelligence at the Edge.

Fund:CLDS (UK EPSRC)

Distributed Intelligence & Learning: context-aware computing; federated learning.

Fund:INNOVATE (H2020); FedLearn (BMW Research)

Ongoing Projects

Distributed Learning & Computing

Distributed Machine Learning; Optimal Stopping Theory. [Presentations & Tutorials]

Edge Computing

Stochastic Optimization; Distributed Decision Making. [Presentations & Tutorials]

Explainable Predictive Analytics

Query-driven Analytics; Exploratory Analytics; Learning Automata. [Presentations & Tutorials]

In-network Processing

Federated/Local Machine Learning; Adaptive & Control Theory; Contextual Information Processing. [Presentations & Tutorials]

Check for Postgraduate Research & PhD Opportunities. Contact: Dr Christos Anagnostopoulos

NEWS

F. Savva, C. Anagnostopoulos, P. Triantafillou, K. Kolomvatsos, 'Large-scale Data Exploration using Explanatory Regression Functions', ACM Transactions on Knowledge Discovery from Data (ACM TKDD).

Funding: EPSRC EP/R018634/1, EU/H2020/MSCA-IF INNOVATE

"A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction", T Anagnostopoulos; T Xanthopoulos; Y Psaromiligkos

SI: Recent Advancement in Edge/Fog Computing for Intelligent IoT Applications (Sensors; MDPI IF 3.275)

"A Demand-driven Proactive Tasks Management Model at the Edge", A. Karanika, P. Oikonomou, K. Kolomvatsos, T. Loukopoulos

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2020, Glasgow, UK
Session: Human-in-the-Loop Interactions in Fuzzy Reasoning and Machine Learning. (EU/MSCA-IF/INNOVATE)

Scaling kNN queries using statistical learning

Cahsai, Atoshum Samuel (2020). PhD Thesis, University of Glasgow [online]

3 Book Chapters authored by: E. Aleksandrova, H. Ivanov, N. Shehab, K. Kolomvatsos, C. Anagnostopoulos

Convergence of Artificial Intelligence and the Internet of Things

Join our presentation (IEEE ISCC 2020) Session 5B: Cloud and Edge Computing (Session I)
Ali, A. F. T., Anagnostopoulos, C. and Pezaros, D. In-Network Placement of Security VNFs in Multi-Tenant Data Centers

PhD Defense

Congratulations to A. S. Cahsai for the PhD Defense: Scaling kNN Queries Using Statistical Learning Link: [Enlighten]

PhD Defense

Congratulations to A. F. T. Ali for the PhD Defense: On the Placement of Security-related Virtualized Netwrok Functions over Data Center Networks Link: [Enlighten]

Special Issue: Recent Advancement in Edge/Fog Computing for Intelligent IoT Applications

Deadline: 31 JULY 2020

Special Issue: Intelligent Edge Computing for Smart Cities

Deadline: 31 DEC 2020

10th Workshop on Management of Cloud and Smart City Systems (MoCS 2020; IEEE ISCC 2020)

P. Fountas, K. Kolomvatsos, A Continuous Data Imputation Mechanism based on Streams Correlation, IEEE ISCC Rennes, France, 7-10 July 2020.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE World Congress on Computational Intelligence (WCCI)

Karanika, A., Oikonomou, P., Kolomvatsos, K., Loukopoulos, T., A Demand-driven, Proactive Tasks Management Model at the Edge, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Glasgow, UK, 2020

CD-MAKE Cross Domain Conference for Machine Learning and Knowledge Extraction, All-Digital Conference, 25-28 Aug 2020

Karanika, A., Oikonomou, P., Kolomvatsos, K. and Anagnostopoulos, C. An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge. (EU INNOVATE)
Interpretable ML in Edge Computing!

Future Generation Computer Systems

Savva, F. , Anagnostopoulos, C. and Triantafillou, P. Adaptive learning of aggregate analytics under dynamic workloads Future Generation Computer Systems [PDF] (UK EPSRC CLDC)
Adaptive Query-driven Analytics at the Edge!

Journal of Data Science and Analytics

Anagnostopoulos, C. and Triantafillou, P. Large-scale predictive modeling and analytics through regression queries in data management systems. Journal of Data Science and Analytics, 9(1), pp. 17-55. [PDF] (UK EPSRC CLDC)

Journal of Network and Computer Applications

Anagnostopoulos, C. Edge-centric inferential modeling and analytics. [PDF], Journal of Network and Computer Applications (UK EPSRC CLDC)

IEEE Conference on Data Engineering (IEEE ICDE 2020)

F. Savva, C. Anagnostopoulos, P. Triantafillou (2020) SuRF: Identification of Interesting Data Regions with Surrogate Models.36th IEEE International Conference on Data Engineering (IEEE ICDE), Dallas, TX, USA, 20-24 April 2020
A succesfull blend of Swarm Intelligence with Machine Learning!

IEEE Symposium on Computers and Communications (IEEE ISCC 2020)

Ali, A. F. T., Anagnostopoulos, C. and Pezaros, D. In-Network Placement of Security VNFs in Multi-Tenant Data Centers, IEEE ISCC Rennes, France, 7-10 July 2020.

Applied Intelligence

Anagnostopoulos, C. and Kolomvatsos, K. Predictive intelligence of reliable analytics in distributed computing environments, Applied Intelligence [PDF] (UK EPSRC CLDC)

Recruiting: Post Doctoral Data Analyst/Research Associate/Research Fellow!

EPSRC-funded Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics (Closes: 17 JUNE 2020)

ACM Transactions on Internet Technology (ACM TOIT)

Panagidi, K., Anagnostopoulos, C. , Chalvatzaras, A. and Hadjietfthymiades, S. (2019) To transmit or not to transmit: controlling the communications in the mobile IoT domain. ACM Transactions on Internet Technology (TOITs)

We are recruiting!

Permanent faculty openings (Lecturer, Senior Lecturer, Reader) at Glasgow's School of Computing Science: Advanced networking; Cyber Security; FinTech; Trustworthy Autonomous Systems
Deadline: 08.01.2020

IEEE WCCI/ECCI 2020

Our First International Workshop on Edge Computing & Computational Intelligence (ECCI 2020) is held in conjunction with the IEEE WCCI 2020, 19 - 24 July 2020, Glasgow, UK

Recruiting: Research Associate/Fellow!

EPSRC-funded Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics (Closes: 6th February 2020)

Journal article in Infromation. MDPI

Alghamdi, I. A. I., Anagnostopoulos, C. and Pezaros, D. P. 'Delay-tolerant sequential decision making for task offloading in mobile edge computing environments'.

Journal article in Network and Computer Applications, Elsevier (IF: 5.273; JCR/2018)

'An intelligent, time-optimized monitoring scheme for edge nodes'
Anagnostopoulos, C. and Kolomvatsos, K.

IEEE Big Data 2019; LA, 9-12 DEC 2019

'Aggregate Query Prediction under DynamicWorkloads'
F. Savva, C. Anagnostopoulos, P. Triantafillou

Article in Computing (Springer)

'A probabilistic model for assigning queries at the edge'
K. Kolomvatsos, C. Anagnostopoulos

IEEE Global Communications Conference 9-13 DEC 2019, Waikoloa, HI, USA Revolutionizing Communications

Join our full paper presentation in GLOBECOM 2019 @ Hawaii!
Alghamdi, I. A. I., Anagnostopoulos, C. and Pezaros, D. P. 'On the Optimality of Task Offloading in Mobile Edge Computing Environments'.

12th International Conference on Internet and Distributed Computing Systems (IDCS 2019)

Join our full paper presentation in IDCS 2019 @ Italy
Karanika, A., Soula, M., Anagnostopoulos, C. , Kolomvatsos, K. and Stamoulis, G. 'Optimized Analytics Query Allocation at the Edge of the Network'.

IEEE West European Student and Young Professional Congress (WESYP 2019)

Join my Plenary Talk in IEEE WESYP 2019 @ Glasgow, Scotland
Anagnostopoulos, C. 'Next Generation Networked Systems: Virtualization, Programmability, Adaptability, Resilence, Intelligence'.

[Call for Papers] International Journal of Distributed Sensor Network

Special Collection on Distributed Artificial and Machine Intelligence in Evolving Sensor Networks
Deadline: January 31, 2020

SIGMOD Student Research Competition

Congratulations to F. Savva on winning the 2nd place in SIGMOD 2019 SRC!

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

Applied Intelligence (Springer; Editorial News 2019)

Certificate awarded to C. Anagnostopoulos in recognition of continued commitment, prompt reviews, and excellent service to the International Journal of Applied Intelligence as an Editorial Board Member.

UCI ML Repository Dataset

[New] Query Analytics Workloads Dataset in UCI ML Repository [Dataset]

Special Issue Journal: Sensors (IF: 3.031)

Recent Advancement in Edge/Fog Computing for Intelligent IoT Applications Guest Editors: C Anagnostopoulos; K Kolomvatsos; D Pezaros [CFP]

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]

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

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

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

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 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 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.

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.

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]

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.

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.

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]

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 @ 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 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.

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

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

Guest Talk: Dr Eiko Yoneki; Uni of Cambridge

Title:Performance Tuning with Structured Bayesian Optimisation and Reinforcement Learning. [More]

H2020/INNOVATE Outcome@FGCS Journal

Kostas Kolomvatsos, Christos AnagnostopoulosMulti-criteria optimal task allocation at the edge, Future Generation Computer Systems,Volume 93,2019,Pages 358-372, [paper]

UCI ML Repository: USV Sensor Data Set 2

H2020/GNFUV@Download & Experiment: GNFUV Unmanned Surface Vehicles Sensor DataSet 2 [UCI ML Rep.]

Book Chapter Ed. I-S. Comşa (Brunel University London, UK); R. Trestian (Middlesex University, UK)

E. Aleksandrova, C. Anagnostopoulos. Adaptive Principal Component Analysis-based Outliers Detection through Neighbourhood Voting in Wireless Sensor Networks.Book: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications; Funded by: H2020/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]