1. Berlin SPARQL Benchmark (BSBM) Presented by: Nikhil Rajguru Christian Bizer and Andreas Schultz

2. Agenda Need for a benchmark for RDF stores Existing benchmarks Design of BSBM, Dataset generator and query mixes Evaluation results Contributions My work Q&A

3. Motivation A large number of Semantic web applications represent their data as RDF Many RDF stores support the SPARQL query language and SPARQL protocol Need to compare performance of various RDF stores and also traditional Relational DB solutions (SPARQL wrappers)

4. Existing benchmarks SP 2 Bench Uses a synthetic, scalable version of the DBLP bibliography dataset Queries designed for comparison of different RDF Store layouts - Not designed towards realistic workloads, no parameterized queries and no warmup DBPedia Bechmark Uses DBPedia as the benchmark dataset - Very specific queries and dataset not scalable Lehigh University Benchmark (LUBM) Compares OWL reasoning engines - Does not cover SPARQL specific features like OPTIONAL filters, UNION, DESCRIBE, etc. - Does not employ parameterized queries, concurrent clients and warm- up

5. Main Goals of BSBM Compare different stores that expose SPARQL endpoints Have realistic use case motivated data sets and Query mixes Test query performance (integration and visualization) against large RDF datasets rather than complex reasoning

6. BSBM Dataset Built around an e-commerce use case Dataset generator Scales to arbitrary sizes (scale factor = # of products) Data generation is deterministic Dataset objects: Product, ProductType, ProductFeature, Producer, Vendor, Offer, Review, Reviewer and ReviewingSite.

7. BSBM Data set sizes

8. BSBM Query Mix Simulates how customers browse, review and select items online Operations include Look for products with some generic features Look for products without some specific features Look for similar products Look for reviews and offers Pull up all information about a specific product Find the best deal for a product

9. BSBM Query Mix

10. BSBM Queries

12. BSBM Query Characteristics

13. Experimental Setup RDF Stores tested – Jena SDB – Virtuoso – Sesame – DR2 Server (with MySQL as underlying RDBMS) DELL workstation Processor: Intel Core 2 Quad Q9450 2.66GHz Memory: 8GB DDR2 667 Hard disks: 160GB (10,000 rpm)SATA2, 750GB (7,200 rpm) SATA2) OS: Ubuntu 8.04 64-bit

14. Load times (sec) Data loaded as, D2R server: Relational representation of BSBM dataset (MySQL dumps) Triple Stores: N-triples representation of BSBM Dataset 3.6 hr 7.7 hr 13.6 hr 3.3 min

15. Overall Run Time 50 query mixes, 1250 queries in all Test driver and store under test running on the same machine 10 query mixes executed for warm up

16. Average Run Time Per Query Gives a different perspective on query performance for the stores No data store performs optimally for all query types at all Data set sizes (50K – 25M triples) Sesame best for Queries 1 - 4 but has bad performance for queries 5 – 9 DR2 server fastest for queries 6 – 9 but bad for all the lower ones Similar results for Jena SDB and Virtuoso

17. Average Run Time Per Query

20. Contributions First benchmark to compare stores that implement SPARQL query language and protocol for data access Dataset generator (RDF, XML and Relational representation) First benchmark to test RDF stores with realistic workloads of use case motivated queries

21. My Work Build a scalable RDF store for storing the Smart Grid data – Sensor readings, building information, weather data, Time schedule for each customer Scale to 50000 sensors (20M triples to be loaded every 15mins) Load Fast and slow changing data

22. My work Support a range of SPARQL queries on the store Web Portal: (latency ~sec) – 100 customers x 100 columns = 10000 triples Schedule trigger: (latency ~min) – ~50,000 customers x 5 schedule events per day x 4 triples = 1,000,000 triples Forecast training: (latency ~hrs) – 3 years x 365 days x 100 readings x 200 buildings x 2 sensor x 25 columns = 1,095,000,000 triples

23. Thank you Questions ?