A model and query language for temporal graph databases

Abstract

Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real-world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database.This paper introduces a temporal graph data model, where nodes and relationships contain attributes (properties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like "Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this happened". As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side

Temporal graph patterns by timed automata

“…For example, durable matchings [50,34] count the number of snapshots in which a matching exists. Much attention has also been paid to tracing unbounded paths in temporal graphs, under various semantics, e.g., fastest, earliest arrival, latest departure, time-forward, time travel, or continuous [9,19,27,57,58,59]. A focus on unbounded paths is complementary to our work on patterns without path variables, but with powerful temporal constraints.…”

Querying Temporal Property Graphs

“…To support temporal evolution, some works propose to extend current graph query languages, based on temporal property graph models, to support basic graph operations and add expressions for time dimension querying [10,11,15]. However, graph query languages are database-dependent but not user-oriented.…”

“…The first one we used is the Social experiment dataset presented in Example 1. The second one is a dataset generated from a reference benchmark available online, namely TPC-DS benchmark 11 . We transformed both datasets into the temporal property graph.…”

“…Some works propose different extensions of classic graph data models to integrate time dimension [27]. In parallel, some works have developed their own management systems for temporal graph data [10,11,15,20]. The overall observation is thus that there is no unified framework for both modelling and querying temporal interconnected data [6,24].…”

Clock-G: A temporal graph management system with space-efficient storage technique

“…It equally maintains a thorough structural and semantic description of the environments of these objects, such as cities or buildings, in which they are located. The clients of this platform are companies and public administrations developing services around smart cities, building or factories, as well as private object owners and developers building 1 https://www.thinginthefuture.com/ 2 Orange is a French multinational telecommunication operator analytical IoT applications. This graph of objects is maintained by a commercial graph database that do not account for the temporal dimension.…”