Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

About

Currently I'm a Query Engine Technical Director  at LeanXcale  and associate researcher at HASLab, one of the INESC TEC research units. I obtained a degree in Computer Science and Systems Engineering from University do Minho in 2005 and the Ph.D in the MAP-i Doctoral Programme in Computer Science in 2012. I have a strong background in distributed systems and large scale data management and more than 10 years of experience in national and international research projects in distributed systems: Big Data, Large scale query processing, Cloud computing, NoSQL and SQL databases,  and database replication.
Currently, I'm working in FP7 European research projects LeanBigData and CloudDBAppliance. I had co-supervise several research grant holders and master thesis. I had published research papers on large scale and dependable distributed systems and has served as reviewer for several highly reputed conferences and workshops such as SRDS, Middleware, LADC, DAIS and MW4SOC. He serve as PC of the MW4NG workshop and DADS conference. I also created and served as chair of the WPSDS workshop.

Interest
Topics
Details

Details

  • Name

    Ricardo Pereira Vilaça
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st November 2011
Publications

2017

Performance trade-offs on a secure multi-party relational database

Authors
Pontes, R; Pinto, M; Barbosa, M; Vilaça, R; Matos, M; Oliveira, R;

Publication
Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017

Abstract

2017

Prepared scan: efficient retrieval of structured data from HBase

Authors
Neves, F; Vilaça, R; Pereira, JO; Oliveira, R;

Publication
Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017

Abstract
The ability of NoSQL systems to scale better than traditional relational databases motivates a large set of applications to migrate their data to NoSQL systems, even without aiming to exploit the provided schema exibility. However, accessing structured data is costly due to such exibility, incurring in a lot of bandwidth and processing unit usage. In this paper, we analyse this cost in Apache HBase and propose a new scan operation, named Prepared Scan, that optimizes the access to data structured in a regular manner by taking advantage of a well-known schema by application. Using an industry standard benchmark, we show that Prepared Scan improves throughput up to 29% and decreases network bandwidth consumption up to 20%. © 2017 ACM.

2017

HTAPBench: Hybrid Transactional and Analytical Processing Benchmark

Authors
Coelho, F; Paulo, J; Vilaça, R; Pereira, JO; Oliveira, R;

Publication
Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017, L'Aquila, Italy, April 22-26, 2017

Abstract
The increasing demand for real-time analytics requires the fusion of Transactional (OLTP) and Analytical (OLAP) systems, eschewing ETL processes and introducing a plethora of proposals for the so-called Hybrid Analytical and Trans-actional Processing (HTAP) systems. Unfortunately, current benchmarking approaches are not able to comprehensively produce a unified metric from the assessment of an HTAP system. The evaluation of both engine types is done separately, leading to the use of disjoint sets of benchmarks such as TPC-C or TPC-H. In this paper we propose a new benchmark, HTAPBench, providing a unified metric for HTAP systems geared toward the execution of constantly increasing OLAP requests limited by an admissible impact on OLTP performance. To achieve this, a load balancer within HTAPBench regulates the coexistence of OLTP and OLAP workloads, proposing a method for the generation of both new data and requests, so that OLAP requests over freshly modified data are comparable across runs. We demonstrate the merit of our approach by validating it with different types of systems: OLTP, OLAP and HTAP; showing that the benchmark is able to highlight the differences between them, while producing queries with comparable complexity across experiments with negligible variability. © 2017 ACM.

2016

Holistic Shuffler for the Parallel Processing of SQL Window Functions

Authors
Coelho, F; Pereira, J; Vilaca, R; Oliveira, R;

Publication
DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2016

Abstract
Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. Currently, systems bypass parallelization opportunities which become especially relevant when considering Big Data as data is naturally partitioned. We present a shuffling technique to improve the parallel execution of window functions when data is naturally partitioned when the query holds a partitioning clause that does not match the natural partitioning of the relation. We evaluated this technique with a non-cumulative ranking function and we were able to reduce data transfer among parallel workers in 85% when compared to a naive approach.

2016

Reducing Data Transfer in Parallel Processing of SQL Window Functions

Authors
Coelho, F; Pereira, J; Vilaca, R; Oliveira, R;

Publication
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, VOL 1 (CLOSER)

Abstract
Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. We propose a technique that can be used in the parallel execution of this operator when data is naturally partitioned. The proposed method benefits the cases where the required partitioning is not the natural partitioning employed. Preliminary evaluation shows that we are able to limit data transfer among parallel workers to 14% of the registered transfer when using a naive approach.

Supervised
thesis

2016

Holistic performance and scalability analysis for large scale distributed systems

Author
Francisco Nuno Teixeira Neves

Institution
UM

2016

Secure multi party computation trade-offs in distritubed databases

Author
Rogério António da Costa Pontes

Institution
UP-FCUP

2015

Análise de desempenho e otimização do Apache HBase para dados relacionais

Author
Francisco Nuno Teixeira Neves

Institution
UM

2015

Replicação de base de dados baseada em comunicação em grupo

Author
João Pedro Lopes Miranda

Institution
UM