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About

About

Bruno Veloso holds a M.Sc. in Electrical and Computers Engineering, Major in Telecommunications from the Polytechnic Institute of Porto (School of Engineering) and a Ph.D. degree in Telematics from the University of Vigo, Spain. He is a researcher at the INESC TEC (Laboratory of Artificial Intelligence and Decision Support (LIAAD)). His interests include distributed artificial intelligence, multi-agent systems, personalisation, recommendation systems and data streams.

Interest
Topics
Details

Details

  • Name

    Bruno Miguel Veloso
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st March 2013
Publications

2018

APASail—An Agent-Based Platform for Autonomous Sailing Research and Competition

Authors
Alves, B; Veloso, B; Malheiro, B;

Publication
Robotic Sailing 2017

Abstract

2018

Personalised Dynamic Viewer Profiling for Streamed Data

Authors
Veloso, B; Malheiro, B; Burguillo, JC; Foss, JD; Gama, J;

Publication
Advances in Intelligent Systems and Computing - Trends and Advances in Information Systems and Technologies

Abstract

2018

Scalable data analytics using crowdsourced repositories and streams

Authors
Veloso, B; Leal, F; Gonzalez Velez, H; Malheiro, B; Carlos Burguillo, JC;

Publication
Journal of Parallel and Distributed Computing

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master–worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances. © 2018 Elsevier Inc.

2018

Self Hyper-Parameter Tuning for Data Streams

Authors
Veloso, B; Gama, J; Malheiro, B;

Publication
Discovery Science - Lecture Notes in Computer Science

Abstract

2017

Renegotiation of Electronic Brokerage Contracts

Authors
Cunha, R; Veloso, B; Malheiro, B;

Publication
Recent Advances in Information Systems and Technologies - Volume 2 [WorldCIST'17, Porto Santo Island, Madeira, Portugal, April 11-13, 2017].

Abstract
CloudAnchor is a multiagent e-commerce platform which offers brokerage and resource trading services to Infrastructure as a Service (IaaS) providers and consumers. The access to these services requires the prior negotiation of Service Level Agreements (SLA) between the parties. In particular, the brokerage SLA (bSLA), which is mandatory for a business to have access to the platform, specifies the brokerage fee the business will pay every time it successfully trades a resource within the platform. However, while the negotiation of the resource SLA (rSLA) includes the uptime of the service, the brokerage SLA was negotiated for an unspecified time span. Since the commercial relationship – defined through the bSLA – between a business and the platform can be long lasting, it is essential for businesses to be able to renegotiate the bSLA terms, i.e., renegotiate the brokerage fee. To address this issue, we designed a bSLA renegotiation mechanism, which takes into account the duration of the bSLA as well as the past behaviour (trust) and success (transactions) of the business in the CloudAnchor platform. The results show that the implemented bSLA renegotiation mechanism privileges, first, the most reliable businesses, and, then, those with higher volume of transactions, ensuring that the most reliable businesses get the best brokerage fees and resource prices. The proposed renegotiation mechanism promotes the fulfilment of SLA by all parties and increases the satisfaction of the trustworthy businesses in the CloudAnchor platform. © Springer International Publishing AG 2017.