Abstract
Graph data management has revealed beneficial characteristics in terms of flexibility and scalability by differently balancing between query expressivity and schema flexibility. This has resulted into an rapid developing new task specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present day graph query languages are focused towards flexible graph pattern matching (aka sub-graph matching), where as graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language and machine, provides a common platform for supporting any graph computing system (such as an OLTP graph database or OLAP graph processors). We present a formalization of graph pattern matching for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra.
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Notes
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set of strings (\(\varSigma ^{*}\)).
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The act of visiting of vertices (\(v \in V\)) and edges (\(e \in E\)) in a graph in an alternating manner (in some algorithmic fashion) [11].
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Here, (\(\gamma ^{-}, \gamma ^{+}\)) denote the first and last elements of a path respectively.
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The Kleene star notation (\(A^{*}, B^{*}\)) denotes presence of multiple traversers in (A,B).
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Rodriguez and Neubauer [11] refer to step modulators as ‘syntactic sugar’.
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This work is supported by the EU H2020 WDAqua ITN (GA: 642795).
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Thakkar, H., Punjani, D., Auer, S., Vidal, ME. (2017). Towards an Integrated Graph Algebra for Graph Pattern Matching with Gremlin. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_6
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