Real Time Semantic Warehousing: Sindice technology for the enterprise

1. Real Time Semantic Warehousing: Sindice.com technology for the enterprise Giovanni Tummarello, Ph.DData Intensive Infrastructure UNIT - DERI.ie CEO SindiceTech

2. How we started : Sindice.com 80 Billions triple, 500,000,000 RDF Graphs, 5 TB of data. The Sindice Suite powers Sindice.com. Online with 99,9%+ uptime.

3. Semantic Sandboxes on: Sindice.com Data Sandboxes in Sindice.com – Powered by CloudSpaces

4. And then we met people asking can you do it for us

5. To stay competitive, Pharmaceutical companies need to leverage all the data available from inside sources as well as from the increasingly many public HCLS data sources available. Due to the diversity of this data with respect to nature, formats, quality, there are complex integration issues. Traditional data warehousing technology require big upfront thinking and is handled within a company in the “go via the IT department” approach. This does not meet the need of data scientists who are the only ones that can do the complex cross-use case thinking required. Via Real Time Semantic Data Warehousing (RETIS) data scientist expect to get: The ability to speed up “In silico” scientific workflows (interrelation of diverse large datasets) by orders of magnitude by relying on a data warehousing approach. The ability to create large scale “data maps” or “aggregated views” which would allow researchers to see “trends” and gather insights at high level which would not be possible by data accessed via single lookups. The ability to receive recommendations and suggestions for new data connections based on an ever evolving ecosystem of available experimental datasets. Provide their R&D departments with superior tools for investigating their internal knowledge; search engines and data browsing tools which provide unified views of multiple, evolving, live datasets without leakage of specific “queries” to the outside world which would reveal internal research trends The ability to leverage the ever increasing body of public, crowd curated open data Example story (Pharmaceutical company0

6. Linked Data clouds for the Enterprise • Strategic knowledge spaces, where new databases can be added and “leveraged” with an unprecedented ease • Integration “Pay as you go” : explore now, fine tune later. • Its BigData (Cluster+Clouds) meets RDF and Semantic Technologies

7. Sindice.com

8. Because you need Semantic SandBoxes

9. A Dataspace Template Semantic Web Data • A typical implementation template. • Dataspaces own: • Resources • Services • Datasets for others to reuse

10. Dataspace Composition Scalable cascading semantic ‘Dataspaces” • Resources allocated in public/private clouds • Allow to get Sindice Data and mix it/ process it for private purposes

11. Cloud powered! <dataspace id= “iphonedataspace”> <dependencies>   http://ecommerce01.dataspace.sindice.net/</dataspace>   http://price01.dataspace.sindice.net/</dependencies> <resources> <mysql name=“sql”> <hbasesize=“10g”> <siren name=“index”> <triplestore name=“sparql” kind=“virtuoso” /> </resources><retention> (see later) <update-rate>1D</update-rate> <timeout>1D</timeout></retention></dataspace>

12. Scale is only 1 dimension Multiple dimensions of WeD data integration • RDF tool stack  flexibility • Cluster scalable processing  scalability • “Cloud” Pipelines  dynamicity

13. Full Json Like Search.On Solr.All operators supported.

14. What is SIREn ? • Plugin to Solr • Built for searching and operating on semistructured data and relational datastructures

15. SIREn: Semantic IR Engine Extension to Enterprise Search Engine Solr Semantic, full-text, incremental updates, distributed search Semantic Databases SIREn Constant time

16. Limitations of Apache Solr • Not efficient with highly heterogeneous structured data sources • Limitation on the number of attributes: • Dictionary size explosion

17. Dictionary Size Explosion

18. Dictionary Size Explosion • Dictionary construction • Concatenation of attribute name and term • N * M complexity (worst case) • 2 attributes * 2 terms = 4 dictionary entries • 100K attributes * 1B terms = 100B entries

19. Limitations of Apache Solr • Not efficient with highly heterogeneous structured data sources • Limitation on the number of attributes: • Dictionary size explosion • Query clause explosion when searching across all attributes

20. Limitations of Apache Solr • Not efficient with highly heterogeneous structured data sources • Limitation on the number of attributes: • Dictionary size explosion • Query clause explosion when searching across all attributes • Limited support for structured query • Multi-valued attributes

21. Multi-valued attributes • No support in Solr for "all words must match in the same value of a multi-valued field". • A field value is a bag of words • No distinction between multiple values

22. Multi-valued attributes • No support in Solr for "all words must match in the same value of a multi-valued field". • A field value is a bag of words • No distinction between multiple values • Query example • label : man’s friend • Solr returns Record 1 & 2 as results

23. Limitations of Apache Solr • Not efficient with highly heterogeneous structured data sources • Limitation on the number of attributes: • Dictionary size explosion • Query clause explosion when searching across all attributes • Limited support for structured query • Multi-valued attributes • No full-text search on attribute names

24. Full-text search on attribute names • No support in Solr for “keyword search in attribute names". • Query example • (name OR label) = “Renaud Delbru” • Solr is unable to find the records without the exact attribute name

25. Limitations of Apache Solr • Not efficient with highly heterogeneous structured data sources • Limitation on the number of attributes: • Dictionary size explosion • Query clause explosion when searching across all attributes • Limited support for structured query • Multi-valued attributes • No full-text search on attribute names • No 1:N relationship materialisation

26. Relationship materialization • Its Json like indexing and searching • Materialize the relationships between your entities and others.

27. Some numbers: Siren on Sindice Data Collection • 500M web data documents (RDF, RDFa, Microformat, etc.) • 200K datasets • 50B triples Settings • Cluster of 4 nodes • 2 nodes for indexing • 2 nodes for querying • Replication Services • Keyword and structured queries • Dataset search • >> 99% uptime Indexing Performance • Full index construction takes approx24 hours • 436K triples / second

28. Large scale RDF ‘Summaries”

29. Introducing large scale RDF ‘Summaries” We do it for: • Data exploration • How to find datasets about movies ? • Assisted SPARQL Query Editor • What is the data structure ? • Dataset Quality • How to differentiate relevant form irrelevant dataset ?

30. Large Scale RDF summaries Class Level 12M relationships 10B relationships

31. Sindice Analytics Widget Demo • http://test01.sindice.net:9001/sindice-stats-webapp/ • http://test01.sindice.net/szydan/dataset-view/dataset/default/www.bbc.co.uk

32. Relational Faceted Browsing. At speed of light Patent Pending

33. SparQL is awesome. And now your guys can actually use it.

34. Thank you Sindice.com team April 2012 With the contribution of