Cesar Augusto Fonticielha De Rose

Este trabalho apresenta um estudo de caso com aplicação de mineração de dados educacionais para analisar as matrículas dos acadêmicos do curso de Bacharelado em Engenharia de Software da UDESC. O objetivo é coletar indicadores do curso sobre matrículas, conclusões e evasões, e verificar combinações de disciplinas cursadas simultaneamente que possam maximizar o potencial de aprovação dos acadêmicos. O estudo utilizou dados obtidos do sistema acadêmico da UDESC contendo registros de matrículas desde a implantação do curso em 2014. Gráficos interativos foram criados para visualizar os indicadores do curso. O algoritmo Apriori foi utilizado para obter conjuntos de matrículas frequentes e regras de associação. Uma prova de conceito de sistema foi desenvolvida para evidenciar a viabilidade de recomendar disciplinas a partir das regras de associação.

With the rapid growth in data volume, workloads from various domains have undergone drastic changes in recent years. Today, streaming workloads are commonplace. This generates the need for systems and algorithms that can receive and process streams of data with high throughput. Various graph applications are shifting away from the static graph model and incorporating a dynamic model, where updates to the graph can be received. In a dynamic setting, solutions to algorithms need to be updated alongside the graph. However, re-processing the whole graph every time can be infeasible given the size of current graphs. This raises a series of questions regarding how to process dynamic graph algorithms in a reasonable amount of time. In this paper, we explore the existing methods in the literature used to achieve scalable dynamic graph processing. We define different aspects and abstractions used for dynamic graph processing and categorize all approaches on the basis of their scalability.

Cloud computing has become increasingly popular among organizations. As a result, security has become a major concern in the adoption of cloud computing environments. To ensure confidentiality and prevent data leakage, organizations have adopted various security measures, including sophisticated authentication methods and strong cryptography algorithms. However, implementing these measures generates additional overhead that could impact resource consumption and performance at user level. This paper proposes a modular architecture for a full-stack confidentiality cloud and a model to estimate implementation costs for each component that can be used as a blueprint to implement the needed confidentiality in a particular cloud scenario and estimate the resulting overhead. It contributes to the literature by enabling cloud administrators and users to leverage confidentiality based on their security needs and budget. Preliminary experiments show that our cost model achieves a high level of accuracy, up to 95%.

Detecção de comunidades em grafos é um tipo de análise amplamente utilizada por aplicações de diversas áreas do conhecimento. Com o crescente aumento do volume de dados surge a necessidade de implementações paralelas e distribuídas para que o tempo de processamento não aumente a ponto de limitar sua aplicabilidade. Este estudo analisa a escalabilidade e a acurácia dos métodos para detecção de comunidades mais utilizados atualmente em ambientes paralelos e distribuídos. A partir de uma análise detalhada do impacto que a comunicação entre os processos teve no tempo de execução de cada implementação são feitas recomendações em relação aos algoritmos mais promissores em relação a melhoria da escalabilidade nestes ambientes.

As High Performance Computing (HPC) becomes a tool used in many different workflows, Quality of Service (QoS) becomes increasingly important. In many cases, this includes the reliable execution of an HPC job and the generation of the results by a certain deadline. The Resource and Job Management System (RJMS or simply RMS) is responsible for receiving the job requests and executing the jobs with a deadline-oriented policy to support the workflows. In this paper, we evaluate how well static resource management policies cope with deadline constrained HPC jobs, and explore two variations of a dynamic policy in this context. Our preliminary results clearly show that a dynamic policy is needed to meet the requirements of a modern deadline-oriented RMS scenario.

Tip decomposition is a crucial kernel for mining dense subgraphs in bipartite networks, with applications in spam detection, analysis of affiliation networks etc. It creates a hierarchy of vertex-induced subgraphs with varying densities determined by the participation of vertices in butterflies (2,2-bicliques). To build the hierarchy, existing algorithms iteratively follow a delete-update(peeling) process: deleting vertices with the minimum number of butterflies and correspondingly updating the butterfly count of their 2-hop neighbors. The need to explore 2-hop neighborhood renders tip-decomposition computationally very expensive. Furthermore, the inherent sequentiality in peeling only minimum butterfly vertices makes derived parallel algorithms prone to heavy synchronization. In this paper, we propose a novel parallel tip-decomposition algorithm -- REfine CoarsE-grained Independent Tasks (RECEIPT) that relaxes the peeling order restrictions by partitioning the vertices into multiple independent subsets that can be concurrently peeled. This enables RECEIPT to simultaneously achieve a high degree of parallelism and dramatic reduction in synchronizations. Further, RECEIPT employs a hybrid peeling strategy along with other optimizations that drastically reduce the amount of wedge exploration and execution time. We perform detailed experimental evaluation of RECEIPT on a shared-memory multicore server. It can process some of the largest publicly available bipartite datasets orders of magnitude faster than the state-of-the-art algorithms -- achieving up to 1100x and 64x reduction in the number of thread synchronizations and traversed wedges, respectively. Using 36 threads, RECEIPT can provide up to 17.1x self-relative speedup. Our implementation of RECEIPT is available at https://github.com/kartiklakhotia/RECEIPT.