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FADAMS SIGNED

Foundations of Factorized Data Management Systems

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EC-Contrib. €

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Project "FADAMS" data sheet

The following table provides information about the project.

Coordinator
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD 

Organization address
address: WELLINGTON SQUARE UNIVERSITY OFFICES
city: OXFORD
postcode: OX1 2JD
website: www.ox.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]
 Project website https://fdbresearch.github.io/
 Total cost 1˙980˙966 €
 EC max contribution 1˙980˙966 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-CoG
 Funding Scheme ERC-COG
 Starting year 2016
 Duration (year-month-day) from 2016-06-01   to  2021-05-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD UK (OXFORD) coordinator 1˙980˙966.00

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 Project objective

The objective of this project is to investigate scalability questions arising with a new wave of smart relational data management systems that integrate analytics and query processing. These questions will be addressed by a fundamental shift from centralized processing on tabular data representation, as supported by traditional systems and analytics software packages, to distributed and approximate processing on factorized data representation.

Factorized representations exploit algebraic properties of relational algebra and the structure of queries and analytics to achieve radically better data compression than generic compression schemes, while at the same time allowing processing in the compressed domain. They can effectively boost the performance of relational processing by avoiding redundant computation in the one-server setting, yet they can also be naturally exploited for approximate and distributed processing. Large relations can be approximated by their subsets and supersets, i.e., lower and upper bounds, that factorize much better than the relations themselves. Factorizing relations, which represent intermediate results shuffled between servers in distributed processing, can effectively reduce the communication cost and improve the latency of the system.

The key deliverables will be novel algorithms that combine distribution, approximation, and factorization for computing mixed loads of queries and predictive and descriptive analytics on large-scale data. This research will result in fundamental theoretical contributions, such as complexity results for large-scale processing and tractable algorithms, and also in a scalable factorized data management system that will exploit these theoretical insights. We will collaborate with industrial partners, who are committed to assist in providing datasets and realistic workloads, infrastructure for large-scale distributed systems, and support for transferring the products of the research to industrial users.

 Publications

year authors and title journal last update
List of publications.
2019 Ahmet Kara, Milos Nikolic, Dan Olteanu, Haozhe Zhang
Trade-offs in Static and Dynamic Evaluation of Hierarchical Queries
published pages: , ISSN: , DOI:
under submission to ACM PODS 2020 2019-09-02
2019 Ryan Curtin, Benjamin Moseley, Hung Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
Rk-means: Fast Coreset Construction for Clustering Relational Data
published pages: , ISSN: , DOI:
under submission for ICML\'19, not public due to double-blind reviewing 2019-02-26
2019 Mahmoud Abo Khamis, Hung Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
A Layered Aggregate Engine for Analytics Workloads
published pages: , ISSN: , DOI:
under submission since 2018 for SIGMOD\'19, not public yet due to double-bling reviewing policy 2019-02-26
2018 Mahmoud Abo Khamis, Hung Q. Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
published pages: , ISSN: , DOI:
under submission since 2018, invited to special ACM TODS issue of best papers in ACM PODS 2018 2019-02-26
2018 Mahmoud Abo Khamis, Ryan Curtin, Benjamin Moseley, Hung Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
On Functional Aggregate Queries with Additive Inequalities
published pages: , ISSN: , DOI:
under submission since 2018 for PODS\'19 2019-02-28

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