Aion: Efficient Temporal Graph Data Management

Wei Lu Zhouyu Wang Guodong Jin + 4 lainnya

24 April 2023

Real-world graphs are often dynamic and evolve over time. It is crucial for storing and querying a graph's evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support, or both. In this paper, we propose AeonG, a new graph database with built-in temporal support. AeonG is based on a novel temporal graph model. To fit this model, we design a storage engine and a query engine. Our storage engine is hybrid, with one current storage to manage the most recent versions of graph objects, and another historical storage to manage the previous versions of graph objects. This separation makes the performance degradation of querying the most recent graph object versions as slight as possible. To reduce the historical storage overhead, we propose a novel anchor+delta strategy, in which we periodically create a complete version (namely anchor) of a graph object, and maintain every change (namely delta) between two adjacent anchors of the same object. To boost temporal query processing, we propose an anchor-based version retrieval technique in the query engine to skip unnecessary historical version traversals. Extensive experiments are conducted on both real and synthetic datasets. The results show that AeonG achieves up to 5.73× lower storage consumption and 2.57× lower temporal query latency against state-of-the-art approaches, while introducing only 9.74% performance degradation for supporting temporal features.

Michael J. Carey Glenn Galvizo

13 Mei 2024

The increasing prevalence of large graph data has produced a variety of research and applications tailored toward graph data management. Users aiming to perform graph analytics will typically start by importing existing data into a separate graph-purposed storage engine. The cost of maintaining a separate system (e.g., the data copy, the associated queries, etc …) just for graph analytics may be prohibitive for users with Big Data. In this paper, we introduce Graphix and show how it enables property graph views of existing document data in AsterixDB, a Big Data management system boasting a partitioned-parallel query execution engine. We explain a) the graph view user model of Graphix, b) $\text{gSQL}^{++}$, a novel query language extension for synergistic document-based navigational pattern matching, and c) how edge hops are evaluated in a parallel fashion. We then compare queries authored in $\text{gSQL}^{++}$ against versions in other leading query languages. Finally, we evaluate our approach against a leading native graph database, Neo4j, and show that Graphix is appropriate for operational and analytical workloads, especially at scale.

Shipeng Qi Kaiwei Li Xingdi Wei + 5 lainnya

28 November 2024

Graph databases are getting more and more attention in the highly interconnected data domain, and the demand for efficient querying of big data is increasing. We noticed that there are duplicate patterns in graph database queries, and the results of these patterns can be stored as materialized views first, which can speed up the query rate. So we propose materialized views on property graphs, including three parts: view creation, view maintenance, and query optimization using views, and we propose for the first time an efficient templated view maintenance method for containing variable-length edges, which can be applied to multiple graph databases. In order to verify the effect of materialized views, we prototype on TuGraph and experiment on both TuGraph and Neo4j. The experiment results show that our query optimization on read statements is much higher than the additional view maintenance cost brought by write statements. The speedup ratio of the whole workload reaches up to 28.71x, and the speedup ratio of a single query reaches up to nearly 100x.

Veronica Santos Bruno Cuconato

24 Desember 2024

Graphs are the most suitable structures for modeling objects and interactions in applications where component inter-connectivity is a key feature. There has been increased interest in graphs to represent domains such as social networks, web site link structures, and biology. Graph stores recently rose to prominence along the NoSQL movement. In this work we will focus on NOSQL graph databases, describing their peculiarities that sets them apart from other data storage and management solutions, and how they differ among themselves. We will also analyze in-depth two different graph database management systems - AllegroGraph and Neo4j that uses the most popular graph models used by NoSQL stores in practice: the resource description framework (RDF) and the labeled property graph (LPG), respectively.

Robert Pavliš

20 Mei 2024

As the global volume of data continues to rise at an unprecedented rate, the challenges of storing and analyzing data are becoming more and more highlighted. This is especially apparent when the data are heavily interconnected. The traditional methods of storing and analyzing data such as relational databases often encounter difficulties when dealing with large amounts of data and this is even more pronounced when the data exhibits intricate interconnections. This paper examines graph databases as an alternative to relational databases in an interconnected Big Data environment. It will also show the theoretical basis behind graph databases and how they outperform relational databases in such an environment, but also why they are better suited for this kind of environment than other NoSQL alternatives. A state of the art in graph databases and how they compare to relational databases in various scenarios will also be presented in this paper.