Opendata, web and dolomites


Next gEneration Sequence sTORage

Total Cost €


EC-Contrib. €






Project "NESTOR" data sheet

The following table provides information about the project.


There are not information about this coordinator. Please contact Fabio for more information, thanks.

 Coordinator Country France [FR]
 Total cost 246˙668 €
 EC max contribution 246˙668 € (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-GF
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2020-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITE DE PARIS FR (PARIS) coordinator 246˙668.00


 Project objective

Sequential data are everywhere, from DNA sequences to astronomical light curves, and from aircraft engine monitoring data to the prices of stock options. Recent advances in various fields such as those of data storage, networking and sensing technologies, have allowed organizations to gather overwhelming amounts of sequential data at unprecedented speeds. This wealth of information enables analysts to identify patterns, find abnormalities, and extract knowledge. It is noteworthy that common practice in various domains is to use custom data analysis solutions, usually built using higher level programming languages, such as R/Python. Such techniques, however, while commonly acceptable in small data processing scenarios, are unfit for larger scale data management and exploration. This is because they come in contrast to all previous database research, not taking advantage of indexes, physical data independence, query optimization, and data processing methods, designed for scalability. In these domains, database systems are used merely for storing and retrieving data and not as the sophisticated query processing systems they are. Current relational storage layers cannot handle the access patterns that analysts of sequential data are interested in, without scanning large amounts of unnecessary data or without large processing overhead. Thus, making complex analytics inefficient. In order to exploit this new opportunity, we plan to develop specialized data series storage and retrieval systems, which will allow analysts – across different fields – to efficiently manipulate the sequences of interest. The proposed research project, named NESTOR (Next gEneration Sequence sTORage), has the potential of great economic and social impact in Europe as multiple scientific and industrial fields are currently in need of the right tools, in order to handle their massive collections of data series.


year authors and title journal last update
List of publications.
2019 Anthony Bagnall; Richard L. Cole; Themis Palpanas; Kostas Zoumpatianos
Data Series Management (Dagstuhl Seminar 19282)
published pages: 24--39, ISSN: , DOI: 10.4230/dagrep.9.7.24
Dagstuhl Reports 9/7 2020-04-09
2019 Kostas Zoumpatianos, Stratos Idreos, Themis Palpanas
T-Store: Tunable Storage for Large Sequential Data
published pages: , ISSN: , DOI:
North East Database Day 2019 2020-04-09
2019 Karima Echihabi; Kostas Zoumpatianos; Themis Palpanas; Houda Benbrahim
Return of the Lernaean Hydra: experimental evaluation of data series approximate similarity search
published pages: 403-420, ISSN: 2150-8097, DOI:
Proceedings of the VLDB Endowment 13/2 2020-04-09
2018 Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas, Houda Benbrahim
The lernaean hydra of data series similarity search
published pages: 112-127, ISSN: 2150-8097, DOI: 10.14778/3282495.3282498
Proceedings of the VLDB Endowment 12/2 2020-04-09
2018 Haridimos Kondylakis; Niv Dayan; Kostas Zoumpatianos; Themis Palpanas
Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes
published pages: 677-690, ISSN: 2150-8097, DOI:
Proceedings of the VLDB Endownment 11/6 2020-04-09
2019 Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos, Themis Palpanas
Coconut: sortable summarizations for scalable indexes over static and streaming data series
published pages: 847-869, ISSN: 1066-8888, DOI: 10.1007/s00778-019-00573-w
The VLDB Journal 28/6 2020-04-09

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The information about "NESTOR" are provided by the European Opendata Portal: CORDIS opendata.

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