Temporal Graph Database Management System
School of Computer Science and Engineering, Beihang University, China
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing, China
1. Background in the main topic areas of the workshop
I have been actively working on graph related topics since 2010, and has published more than 20 papers on referred top conferences and journals on graph analytics and management, including graph pattern matching, shortest path queries, graph mining and graph systems. Please refer to my home page for details: http://mashuai.buaa.edu.cn.
2. Topics that I would like to lead discussion
I will like to discuss temporal graph database management systems, including its temporal graph model and temporal graph languages.
Temporal graphs are a class of graphs whose nodes and edges, together with the associated properties, continuously change over time. Recently, systems have been developed to support snapshot queries over temporal graphs. However, these systems barely support aggregate time range queries. Moreover, these systems cannot guarantee ACID transactions, an important feature for data management systems as long as concurrent processing is necessary. To solve these issues, we design and develop TGraph by extending Neo4j, a temporal graph data management system, that assures the ACID transaction feature, and supports temporal graph queries. Based on our past experiences on temporal graphs, we will discuss the following tentative issues that would be needed for a temporal graph database management system.
(1) Temporal graph model and its semantics.
(2) System design on managing both static and dynamic properties.
(3) Supporting ACID transaction feature for temporal graph databases.
(4) Extending Neo4j Cypher for supporting temporal graph queries.
(5) Indexes for speeding up temporal graph queries
3. Links to related supporting resources
 Neo4j graph databases, https://neo4j.com
 Haixing Huang, Jinghe Song, Xuelian Lin, Shuai Ma, Jinpeng Huai: TGraph: A Temporal Graph Data Management System. CIKM 2016, https://dl.acm.org/citation.cfm?doid=2983323.2983335
 Shuai Ma, Renjun Hu, Luoshu Wang, Xuelian Lin, Jinpeng Huai: Fast Computation of Dense Temporal Subgraphs. ICDE 2017: 361-372, https://ieeexplore.ieee.org/document/7929991
 Shuai Ma, Renjun Hu, Luoshu Wang, Xuelian Lin, Jinpeng Huai: An Efficient Approach to Finding Dense Temporal Subgraphs. TKDE early access, https://ieeexplore.ieee.org/document/8606209