Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries
Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …
even trillions of edges. Often, their structure changes over time and they have domain …
A survey on the evolution of stream processing systems
Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …
witnessing its prime time due to recent successful efforts by the research community and …
DZiG: Sparsity-aware incremental processing of streaming graphs
M Mariappan, J Che, K Vora - … of the sixteenth European conference on …, 2021 - dl.acm.org
State-of-the-art streaming graph processing systems that provide Bulk Synchronous Parallel
(BSP) guarantees remain oblivious to the computation sparsity present in iterative graph …
(BSP) guarantees remain oblivious to the computation sparsity present in iterative graph …
Teseo and the analysis of structural dynamic graphs
D De Leo, P Boncz - Proceedings of the VLDB Endowment, 2021 - dl.acm.org
We present Teseo, a new system for the storage and analysis of dynamic structural graphs
in main-memory and the addition of transactional support. Teseo introduces a novel design …
in main-memory and the addition of transactional support. Teseo introduces a novel design …
The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores
Graph databases (GDBs) are crucial in academic and industry applications. The key
challenges in develo** GDBs are achieving high performance, scalability …
challenges in develo** GDBs are achieving high performance, scalability …
Sortledton: a universal, transactional graph data structure
Despite the wide adoption of graph processing across many different application domains,
there is no underlying data structure that can serve a variety of graph workloads (analytics …
there is no underlying data structure that can serve a variety of graph workloads (analytics …
Bridging the Gap between Relational {OLTP} and Graph-based {OLAP}
Recently, many applications have required the ability to perform dynamic graph analytical
processing (GAP) tasks on the datasets generated by relational OLTP in real time. To meet …
processing (GAP) tasks on the datasets generated by relational OLTP in real time. To meet …
Efficient Large Graph Processing with {Chunk-Based} Graph Representation Model
Existing external graph processing systems face challenges in terms of low I/O efficiency,
expensive computation overhead, and high graph algorithm development costs when …
expensive computation overhead, and high graph algorithm development costs when …
Practice of streaming processing of dynamic graphs: Concepts, models, and systems
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …
machine learning, medical applications, social network analysis, computational sciences …
Practice of streaming processing of dynamic graphs: Concepts, models, and systems
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …
machine learning, medical applications, social network analysis, computational sciences …