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Lecture Notes in Computer Science
Dissatisfaction with relational databases for large-scale graph processing has motivated a new class of graph databases that offer fast graph processing but sacrifice the ability to express basic relational idioms. However, we hypothesize that the performance benefits amount to implementation details, not a fundamental limitation of the relational model. To evaluate this hypothesis, we are exploring code-generation to produce fast in-memory algorithms and data structures for graph patterns thatdoi:10.1007/978-3-319-13960-9_3 fatcat:irr2plv7rrgyba6pehlaqyuqhe