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About

About

Tiago is an invited professor at the Faculty of Engineering University of Porto (FEUP), teaching and researching software engineering, mainly focusing on web development, software processes, and tools. He holds a Ph.D. (Cum Laude) in Software Engineering from FEUP as part of his research on architectures for efficient cloud computing.


In the industry, Tiago leads product development at Kevel, a US company helping brands optimize their digital marketing initiatives in a data-conscious way. He arrived at this position after the acquisition of ShiftForward by Velocidi in 2018 and Kevel in 2022. 


Furthermore, Tiago is an external researcher at INESC-TEC, where he has previously held several full-time research grants. He organized several editions of the EuroPLoP conference and is a Hillside Europe non-profit organization board member. 


Interest
Topics
Details

Details

  • Name

    Tiago Boldt Sousa
  • Role

    External Research Collaborator
  • Since

    01st October 2011
Publications

2022

A Survey on the Adoption of Patterns for Engineering Software for the Cloud

Authors
Sousa, TB; Ferreira, HS; Correia, FF;

Publication
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Abstract
This work takes as a starting point a collection of patterns for engineering software for the cloud and tries to find how they are regarded and adopted by professionals. Existing literature assesses the adoption of cloud computing with a focus on business and technological aspects and falls short in grasping a holistic view of the underlying approaches. Other authors delve into how independent patterns can be discovered (mined) and verified, but do not provide insights on their adoption. We investigate (1) the relevance of the patterns for professional software developers, (2) the extent to which product and company characteristics influence their adoption, and (3) how adopting some patterns might correlate with the likelihood of adopting others. For this purpose, we survey practitioners using an online questionnaire (n = 102). Among other findings, we conclude that most companies use these patterns, with the overwhelming majority (97 percent) using at least one. We observe that the mean pattern adoption tends to increase as companies mature, namely when varying the product operation complexity, active monthly users, and company size. Finally, we search for correlations in the adoption of specific patterns and attempt to infer causation, providing further clues on how some practices depend or influence the adoption of others. We conclude that the adoption of some practices correlates with specific company and product characteristics, and find relationships between the patterns that were not covered by the original pattern language and which might deserve further investigation.

2022

Preface

Authors
Sousa T.B.;

Publication
ACM International Conference Proceeding Series

Abstract

2022

Customer Data Platforms: A Pattern Language for Digital Marketing Optimization with First-Party Data

Authors
Boldt Sousa, T;

Publication
ACM International Conference Proceeding Series

Abstract
The internet is used by the majority of the world's population. Many of its contents are free for consumers, supported by digital marketing investment. The current large online population can render digital marketing campaigns inefficient for brands buying ads if the right message is not reaching the right audience. Customer Data Platforms (CDPs) enables brands to collect first-party data about their customers and leverage it to reach the right audience, without sharing private customer data with third parties. This paper documents how CDPs work, by detailing their main components as patterns and relating them as a pattern language. The language is composed of five patterns: Event Tracking, ID Matching, User Profile Storage, Segmentation, and Activation. The pattern language can be used by marketeers and engineers in digital marketing as an introduction or reference to how CDPs are designed and work. © 2022 ACM.

2021

Preface

Authors
Boldt T.;

Publication
ACM International Conference Proceeding Series

Abstract

2020

A Pattern-Language for Self-Healing Internet-of-Things Systems

Authors
Dias, JP; Sousa, TB; Restivo, A; Ferreira, HS;

Publication
EuroPLoP '20: European Conference on Pattern Languages of Programs 2020, Virtual Event, Germany, 1-4 July, 2020

Abstract
Internet-of-Things systems are assemblies of highly-distributed and heterogeneous parts that, in orchestration, work to provide valuable services to end-users in many scenarios. These systems depend on the correct operation of sensors, actuators, and third-party services, and the failure of a single one can hinder the proper functioning of the whole system, making error detection and recovery of paramount importance, but often overlooked. By drawing inspiration from other research areas, such as cloud, embedded, and mission-critical systems, we present a set of patterns for self-healing IoT systems. We discuss how their implementation can improve system reliability by providing error detection, error recovery, and health mechanisms maintenance. © 2020 ACM.