In most of the cases, data items and events are considered instantaneous, i.e., they are single time points in a discrete temporal domain. The former enables stateful computation on infinite sequences of data items while the latter focuses on the detection of events pattern. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). Spark, Flink) are working towards being the ultimate framework for streaming analytics. Nowadays, modern Big Stream Processing Solutions (e.g. Specifically, we define Seraph semantics, we propose a first version of Seraph syntax, and we discuss the potential impacts from a user perspective. In this work, we propose Seraph, an extension of Cypher, as a first attempt to introduce streaming features in the context of property graph query languages. Indeed, Cypher lacks the features for dealing with streams of (graph) data and continuous query evaluation. A growing number of Cypher's users show interest in continuously querying graph data to act in a timely fashion. However, we are living in a streaming world where data continuously flows. Practitioners find Cypher useful and applicable in many scenarios. Recently, property graphs and, in particular, Cypher 9 (the first open version of the well-known Neo4j Inc.'s language) are gaining popularity. Their high expressiveness and elasticity led the scientific community to design a variety of graph data models and graph query languages, and the practitioners to use them to model real-world cases and extract useful information. The scientific community has been studying graph data models for decades.
0 kommentar(er)
