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 applica...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11119499/ |
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| author | Gabriel G. Dos Santos Cesar A. F. de Rose Kartik Lakhotia |
| author_facet | Gabriel G. Dos Santos Cesar A. F. de Rose Kartik Lakhotia |
| author_sort | Gabriel G. Dos Santos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a68c9b16c6b483b9912575b5a27c7f5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9a68c9b16c6b483b9912575b5a27c7f52025-08-20T03:41:40ZengIEEEIEEE Access2169-35362025-01-011314036414038110.1109/ACCESS.2025.359687111119499The Current Landscape of Scalable Dynamic Graph ProcessingGabriel G. Dos Santos0https://orcid.org/0009-0001-5328-4671Cesar A. F. de Rose1https://orcid.org/0000-0003-0070-0157Kartik Lakhotia2Polytechnic School, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, BrazilPolytechnic School, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, BrazilIntel Laboratories, Santa Clara, CA, USAWith 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.https://ieeexplore.ieee.org/document/11119499/Distributed computingdynamic graphsgraph algorithmsgraph streamsparallel computing |
| spellingShingle | Gabriel G. Dos Santos Cesar A. F. de Rose Kartik Lakhotia The Current Landscape of Scalable Dynamic Graph Processing IEEE Access Distributed computing dynamic graphs graph algorithms graph streams parallel computing |
| title | The Current Landscape of Scalable Dynamic Graph Processing |
| title_full | The Current Landscape of Scalable Dynamic Graph Processing |
| title_fullStr | The Current Landscape of Scalable Dynamic Graph Processing |
| title_full_unstemmed | The Current Landscape of Scalable Dynamic Graph Processing |
| title_short | The Current Landscape of Scalable Dynamic Graph Processing |
| title_sort | current landscape of scalable dynamic graph processing |
| topic | Distributed computing dynamic graphs graph algorithms graph streams parallel computing |
| url | https://ieeexplore.ieee.org/document/11119499/ |
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