Gabriel G. Dos Santos
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Gabriel G. Dos Santos has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Cesar Augusto Fonticielha De Rose
PUCRS
1 shared publication Kartik Lakhotia
Intel Laboratories, Santa Clara, CA, USA
1 shared publication
Latest Publications
- Open Access
- 7 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 13, 140364-140381
The Current Landscape of Scalable Dynamic Graph Processing
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.
- 7 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 13, 140364-140381
Open AccessThe Current Landscape of Scalable Dynamic Graph Processing
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.