  Our answer-graph method to evaluate SPARQL conjunctive queries (CQs) finds a
factorized answer set first, an answer graph, and then finds the embedding
tuples from this. This approach can reduce greatly the cost to evaluate CQs.
This affords a second advantage: we can construct a cost-based planner. We
present the answer-graph approach, and overview our prototype system,
Wireframe. We then offer proof of concept via a micro-benchmark over the YAGO2s
dataset with two prevalent shapes of queries, snowflake and diamond. We compare
Wireframe's performance over these against PostgreSQL, Virtuoso, MonetDB, and
Neo4J to illustrate the performance advantages of our answer-graph approach.
