Explore the words cloud of the FADAMS project. It provides you a very rough idea of what is the project "FADAMS" about.
The following table provides information about the project.
Coordinator |
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Organization address contact info |
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 |
Take a look of project's partnership.
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1 | THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD | UK (OXFORD) | coordinator | 1˙980˙966.00 |
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.
year | authors and title | journal | last update |
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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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