Browsing by Author "Bernardo, Marco V."
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- Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case StudyPublication . Abbasi, Maryam; Bernardo, Marco V.; Vaz, Paulo; Silva, José; Martins, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, JoséThe increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, hetero geneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.
- An Advertising Real-Time Intelligent and Scalable Framework for Profiling Customers EmotionsPublication . Alves, Leandro; Oliveira, Pedro; Henriques, João; Bernardo, Marco V.; Wanzeller, Cristina; Caldeira, FilipeThe advertising industry is continuously looking up for effective ways to communicate to customers to impact their purchasing. Usually, profiling them is a time-consuming offline activity. Therefore, it becomes necessary to reduce costs and time to address consumers’ needs. This work proposes a scalable framework enabled by a Machine Learning (ML) model to profile customers to identify their emotions to help to drive campaigns. A multi-platform mobile application continuously profiles consumers crossing the front stores. Profiling customers according to their age and hair color, the color of their eyes, and emotions (e.g. happiness, sadness, disgust, fear) will help companies to make the most suitable advertisement (e.g. to predict whether the advertising tones on the front store are the adequate ones). All that data are made available in web portal dashboards, wherein subscribers can take their analysis. Such results from the analysis data help them to identify tendencies regarding the culture and age, and drive companies to fit front stores accordingly (e.g. to discover the right tones for the season). This framework can help to develop new innovative cost-effective business models at scale by driving in real-time the advertisements to a huge number of consumers to maximize their impact and centralizing the data collected from a large number of stores to design future campaigns.
- A Cost-Effective Framework for Monitoring Disaster Recovery InfrastructuresPublication . Rocha, Júlio; Lucas, Marco; Figueiredo, Ricardo; Henriques, João; Bernardo, Marco V.; Wanzeller, Cristina; Caldeira, FilipeKeeping Disaster Recovery Infrastructures (DRI) operational is vital in case of incidents. Notwithstanding, continuously monitoring them keeps a costly activity. Therefore, it is essential to have cost-effective solutions while maintaining a continuous. In that aim, this work proposes a cost-effective framework for monitoring DRI supported by an Internet of Things (IoT) device collecting data from their sensors strategically installed in the facilities to protect. In case of incidents, the framework triggers the alerts. A mobile application presents graphically, in real-time, the collected data from sensors. The physical experimentation and achieved results demonstrate the effectiveness of the framework to protect DRI. The proposed framework enabled by software to the different layers (IoT, middleware, and mobile application), and the hardware with its schematic, can help to develop innovative business models for managing DRI. The prototype of the framework produced a large dataset that can help future research on finding anomalies.
- Playfulness and communication for children with autism spectrum disorder: guidelines for a videogamePublication . Alves, Valter; P. Duarte, Rui; Fonseca, F.; Bernardo, Marco V.; Barreto, Pedro; Silva, C.E.; Felizardo, Sara; Videira, I.; Matos, A.; Henriques, C.Autism Spectrum Disorder (ASD) affects sensory processing and conditions the development of communication skills and social interaction. Literature shows that children with ASD are fond of technologies and videogames in particular. The predictable and constant behaviour of technological components, the visual appeal, and the challenges are often highly appreciated (Zakari et al., 2014). Besides, videogames typically allow users to play alone, which is adequate to the profile of such an audience. The use of videogames by autistic children has shown to be relevant, and studies are evidencing gains in several areas (Malinverni et al., 2017; Hedges et al., 2018; Ng & Pera, 2018; Valencia et al., 2019; Baldassarri et al., 2020). Even so, existing solutions that were specifically developed for this audience have assumedly pedagogical goals, which systematically compromises their ludic dimension (Hirsh-Pasek et al., 2015). A study is being developed to design and implement a videogame that focuses on pure playfulness and provides an advantage to players who adopt specific strategies that rely on communicating with other players. This videogame is conceived for both intervention and research. The game mechanics explores the flow theory (Csikszentmihalyi, 2011), in order to dynamically adapt the challenges to the skills shown by the players, trying not to let them reach states of anxiety (due to lack of skills) or boredom (due to lack of challenge). This reasoning is extended to motor skills, as autistic people may have difficulties. In this context, it is important to clarify that the study is limited to children with ASD without associated intellectual development disorders that compromise the viability of the very act of playing. Also instrumental to the project, different scenarios are designed so that researchers can observe and collect scientific data, aiming at better understanding the related issues. Such scenarios support the analysis of the influence of physical proximity between the players, their prior level of familiarity, and their relative communicational abilities. Also under analysis is the impact of repeating the experience, both in terms of in-game performance and regarding a possible contribution to the relationship between participants and, eventually, with third parties. The core of this paper is the presentation of the design guidelines that were created to support the videogame. The guidelines result from the contributions of experts, organised according to a Delphi technique (Green, 2014). The set of experts cover the fields of ASD, game design, special education, occupational therapy, rehabilitation, and educational research. Also included is the description of the videogame development, which resorts to agile methodologies, allowing for an incremental and iterative production, supported by recurrent tests and consistently validated according to the intended objectives.