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INNOVATE SIGNED

INtelligeNt ApplicatiOns oVer Large ScAle DaTa StrEams

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Project "INNOVATE" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF GLASGOW 

Organization address
address: UNIVERSITY AVENUE
city: GLASGOW
postcode: G12 8QQ
website: www.gla.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 195˙454 €
 EC max contribution 195˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-04-01   to  2020-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF GLASGOW UK (GLASGOW) coordinator 195˙454.00

Map

 Project objective

Large scale data analytics is the key research domain for future data driven applications as numerous of devices produce huge volumes of data in the form of streams. Analytics services can offer the necessary basis for building intelligent decision making mechanisms to support novel applications. Due to the huge volumes of data, analytics should be based on efficient schemes for querying large scale data partitions. Partitions contain only a piece of data and a dedicated processor manages the incoming queries. The management of continuous queries over data streams is a challenging research issue requiring intelligent methods to derive the final outcome (i.e., query response) in limited time with maximum performance. The management process of continuous queries involves their assignment to specific processors and the processing of the derived responses. We focus on a group of query controllers serving the incoming queries and, thus, becoming the connection of big data systems with the real world. INNOVATE proposes solutions for the management of the controllers behavior. We propose an intelligent decision making process for each controller in three axes: (i) top-down, by realizing a mechanism that assigns queries to the underlying processors; (ii) bottom-up, by proposing decision making mechanisms for returning responses to users/applications on top of early results; (iii) horizontal, by proposing optimization schemes for queries management. We adopt a pool of learning schemes and an ensemble learning model dealing with how and on which processors each query should be assigned. We also propose specific schemes for combining processors responses. Intelligent and optimization techniques are adopted for the controllers group management. Machine learning, Computational Intelligence and optimization are the key adopted technologies that, when combined, provide efficient solutions to a challenging problem like the support of intelligent analytics over big data streams.

 Publications

year authors and title journal last update
List of publications.
2020 Yiannis Kathidjiotis, Kostas Kolomvatsos, Christos Anagnostopoulos
Predictive Intelligence of Reliable Analytics in DistributedComputing Environments
published pages: , ISSN: 0924-669X, DOI:
Applied Intelligence, Springer 2020-04-07
2020 Karanika, A., Oikonomou, P., Kolomvatsos, K., Loukopoulos, T.
A Demand-driven, Proactive Tasks Management Model at the Edge
published pages: , ISSN: , DOI:
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) July 19-24th, 2020 2020-04-01
2020 K. Kolomvatsos, C. Anagnostopoulos
A Probabilistic Model for Assigning Queries at the Edge
published pages: , ISSN: 0010-485X, DOI:
Computing 2019-11-07
2019 K. Kolomvatsos, C. Anagnostopoulos
Multi-criteria Optimal Task Allocation at the Edge
published pages: , ISSN: 0167-739X, DOI:
Elsevier Future Generation Computer Systems 2019-10-09
2019 K. Kolomvatsos, C. Anagnostopoulos
Edge-Centric Queries Stream Management based on an Ensemble Model
published pages: , ISSN: , DOI:
Advances in Integration of Intelligent Methods 2019-09-11
2019 Karanika, A., Soula, M., Anagnostopoulos, C., Kolomvatsos, K., Stamoulis, G.
Optimized Analytics Query Allocation at the Edge of the Network
published pages: , ISSN: , DOI:
12th International Conference on Internet and Distributed Computing Systems October 10-12, 2019 2019-08-29
2018 K. Kolomvatsos, C. Anagnostopoulos
Intelligent Applications over Large-Scale Data Streams
published pages: , ISSN: , DOI:
DemoFest 2018 2019-06-11
2018 K. Kolomvatsos, C. Anagnostopoulos
In-Network Edge Intelligence for Optimal Task Allocation
published pages: , ISSN: , DOI:
30th International Conference on Tools with Artificial Intelligence November 5-7 2018 2019-06-11
2019 E. Aleksandrova, C. Anagnostopoulos, K. Kolomvatsos
Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach
published pages: , ISSN: , DOI:
18th IEEE International Symposium on Parallel and Distributed Computing June 5-7, 2019 2019-05-27
2018 K. Kolomvatsos, C. Anagnostopoulos
An Edge-Centric Ensemble Scheme for Queries Assignment
published pages: , ISSN: , DOI:
8th International Workshop on Combinations of Intelligent Methods and Applications November 5-7, 2018 2019-05-29
2019 K. Kolomvatsos
A Distributed, Proactive Intelligent Scheme for Securing Quality in Large Scale Data Processing
published pages: , ISSN: 0010-485X, DOI:
Springer Computing 2019-05-17
2019 Dr Kostas Kolomvatsos
An Efficient Scheme for Applying Software Updates in Pervasive Computing Applications
published pages: , ISSN: 0743-7315, DOI:
Journal of Parallel and Distributed Computing 2019-05-17

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