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Despite the maturity of commercial graph databases, little consensus has been reached so far on the standardization of data definition languages (DDLs) for property graphs (PG). The discussion on the characteristics of PG schemas is ongoing in many standardization and community groups. Although some basic aspects of a schema are already present in Neo4j 3.5, like in most commercial graph databases, full support is missing allowing to constraint property graphs with more or less flexibility.In this paper, we focus on two different perspectives from which a PG schema should be considered, as being descriptive or prescriptive, and we show how it would be possible to switch from one to another as the application under development gains more stability. Apart from proposing concise schema DDL inspired by Cypher syntax, we show how schema validation can be enforced through homomorphisms between PG schemas and PG instances; and how schema evolution can be described through the use of graph rewriting operations. Our prototypical implementation demonstrates feasibility and shows the need of offering high-level query primitives to accommodate flexible graph schema requirements as showcased in our work. * Currently on leave at the University of Waterloo, Canada.

Modern application domains such as Composite Event Recognition (CER) and real-time Analytics require the ability to dynamically refresh query results under high update rates. Traditional approaches to this problem are based either on the materialization of subresults (to avoid their recomputation) or on the recomputation of subresults (to avoid the space overhead of materialization). Both techniques have recently been shown suboptimal: instead of materializing results and subresults, one can maintain a data structure that supports efficient maintenance under updates and can quickly enumerate the full query output, as well as the changes produced under single updates. Unfortunately, these data structures have been developed only for aggregate-join queries composed of equi-joins, limiting their applicability in domains such as CER where temporal joins are commonplace. In this paper, we present a new approach for dynamically evaluating queries with multi-way θ-joins under updates that is M. Idris

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Ensuring the success of big graph processing for the next decade and beyond.

The paper describes the present and the future of graph updates in Cypher, the language of the Neo4j property graph database and several other products. Update features include those with clear analogs in relational databases, as well as those that do not correspond to any relational operators. Moreover, unlike SQL, Cypher updates can be arbitrarily intertwined with querying clauses. After presenting the current state of update features, we point out their shortcomings, most notably violations of atomicity and nondeterministic behavior of updates. These have not been previously known in the Cypher community. We then describe the industry-academia collaboration on designing a revised set of Cypher update operations. Based on discovered shortcomings of update features, a number of possible solutions were devised. They were presented to key Cypher users, who were given the opportunity to comment on how update features are used in real life, and on their preferences for proposed fixes. As the result of the consultation, a new set of update operations for Cypher were designed. Those led to a streamlined syntax, and eliminated the unexpected and problematic behavior that original Cypher updates exhibited.

Modern application domains such as Composite Event Recognition (CER) and real-time Analytics require the ability to dynamically refresh query results under high update rates. Traditional approaches to this problem are based either on the materialization of subresults (to avoid their recomputation) or on the recomputation of subresults (to avoid the space overhead of materialization). Both techniques have recently been shown suboptimal: instead of materializing results and subresults, one can maintain a data structure that supports efficient maintenance under updates and can quickly enumerate the full query output, as well as the changes produced under single updates. Unfortunately, these data structures have been developed only for aggregate-join queries composed of equi-joins, limiting their applicability in domains such as CER where temporal joins are commonplace. In this paper, we present a new approach for dynamically evaluating queries with multi-way θ -joins under updates that is effective in avoiding both materialization and recomputation of results, while supporting a wide range of applications. To do this we generalize Dynamic Yannakakis, an algorithm for dynamically processing acyclic equi-join queries. In tandem, and of independent interest, we generalize the notions of acyclicity and free-connexity to arbitrary θ -joins. We instantiate our framework to the case where θ -joins are only composed of equalities and inequalities (<, ≤, =, >, ≥) and experimentally compare this algorithm, called IEDyn, to state of the art CER systems as well as incremental view maintenance engines. IEDyn performs consistently better than the competitor systems with up to two orders of magnitude improvements in both time and memory consumption.