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| Title | Predicting SPARQL Query Dynamics |
| Authors | Alberto Moya Loustaunau, Aidan Hogan |
| Publication date | 2021 |
| Abstract | Given historical versions of an RDF graph, we propose and compare several methods to predict whether or not the results of a SPARQL query will change for the next version. Unsurprisingly, we find that the best results for this task are achievable by considering the full history of results for the query over previous versions of the graph. However, given a previously unseen query, producing historical results requires costly offline maintenance of previous versions of the data, and costly online computation of the query results over these previous versions. This prompts us to explore more lightweight alternatives that rely on features computed from the query and statistical summaries of historical versions of the graph. We evaluate the quality of the predictions produced over weekly snapshots of Wikidata and daily snapshots of DBpedia. Our results provide insights into the trade-offs for predicting SPARQL query dynamics, where we find that a detailed history of changes for a query's results enables much more accurate predictions, but has higher overhead versus more lightweight alternatives. |
| Downloaded | 3 times |
| Pages | 161-168 |
| Conference name | International Conference on Knowledge Capture |
| Publisher | ACM Press (New York, NY, USA) |
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