What is it about?
In my study entitled "On Efficiently Equi-Joining Graphs" I show novel algorithms for combining graphs with data, where the result of such combination might either retain only the information shared between different graphs (conjunctive join) or also include the non-shared information. Such algorithms are justified by the fact that Relational Database Management Systems and Graph Databases are either too slow or use too much memory. This solution shows to be both time and memory efficient.
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Why is it important?
The graph join operation comes in two flavours, conjunctive and disjunctive. The former will make protein-protein similarity measures based on graph kernels quite efficient, as well as allow faster schema-matching algorithms. The latter allows a faster integration of large communication networks. This also has exciting fallbacks in theoretical fields such as Business Process Management and Language Theory, as the conjunctive join rapidly calculates the automaton exhibiting the shared behaviour of two other given automata (e.g., DFA).
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This page is a summary of: On Efficiently Equi-Joining Graphs, July 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3472163.3472269.
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