DBrev: Dreaming of A Database Revolution

The DBrev project by Gjergji Kasneci, Jurgen Van Gael, and Thore Graepel from Microsoft Research Cambridge aims to address key issues in data management, query processing, information extraction, and integration. It explores managing anonymized data, uncertainty in applications, and context awareness. The project leverages large-scale graphical models and factor graphs for data provenance and integration, tackling ambiguity and consistency challenges. Explore how DBrev combines logical constraints and sources of evidence to revolutionize database systems.

• Database revolution
• Data management
• Information extraction
• Graphical models
• Data integration

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1. DBrev: Dreaming of a Database Revolution Gjergji Kasneci, Jurgen Van Gael, Thore Graepel Microsoft Research Cambridge, UK

2. Uncertainty in Applications Managing anonymized data (Approximate) Query Processing Managing sensor data Information extraction Information integration Intelligent data management with following requirements: Store, represent, retrieve data and confidence Assess accuracy Self diagnostic and calibration + DB & IR Statistical ML

3. Main Issues Context Awareness Retrieval & Discovery Provenance Ambiguity Consistency Outrageous: solve these problems simultaneously in integrated system DBrev

4. DBrev Exploits Large-Scale Graphical Model Combine logical constraints and sources of evidence about knowledge fragments into belief network, e.g.: Sample Belief Network for Aggregating User Feedback and Expertise on Knowledge Fragments, Kasneci et al.: WSDM 11

5. DBrev on Information Extraction and Integration Data Provenance Tracing derivation chain back to the sources Closely related to consistency and curation open problem in the presence of multiple sources (Dalvi, R , Suciu: CACM 09) Provenance through factor graphs in DBrev:

6. DBrev on Information Extraction and Integration Data Provenance Tracing derivation chain back to the sources Closely related to consistency and curation open problem in the presence of multiple sources (Dalvi, R , Suciu: CACM 09) Provenance through factor graphs in DBrev: <MichaelJackson, diedOn, 25-07-2009> <MichaelJackson, livesIn, Ireland> michaeljackson.com f1 f1 f2 michaeljackson- sightings.com wikipedia.org/wiki/Michael_Jackson

7. DBrev on Information Extraction and Integration Ambiguity & Context Awareness Are two recognized entities the same? Reasoning over contextual and background info, e.g. The fruit flies like a banana. Problem lies at the heart of AI. Ambiguity & Context in DBrev:

8. DBrev on Information Extraction and Integration Ambiguity & Context Awareness Are two recognized entities the same? Reasoning over contextual and background info, e.g. The fruit flies like a banana. Problem lies at the heart of AI. Ambiguity & Context in DBrev: Entity1 f sameAs f Ontological description/ Semantic features Statistical fingerprint derived from the Web Entity Entity2

9. DBrev on Information Extraction and Integration Consistency In DBs handled by universal constraints in FOL What about more expressive logical constraints? E.g., transitive dependencies between tuples can also support the lineage Consistency in DBrev: <A, R, B> ^ <B, R, C> ^ <R, type, Transitive> <A, R, C> Extracted Triple: ( x , r , y ) refersTo( x , A) ^ refersTo( y , C) ^ canBeDeduced(A, R, C) refersTo ( r , R)

10. DBrev on Information Extraction and Integration Consistency In DBs handled by universal constraints in FOL What about more expressive logical constraints? E.g., transitive dependencies between tuples can also support the lineage Consistency in DBrev: ^ ^ <A, R, B> ^ <B, R, C> ^ <R, type, Transitive> <A, R, C> Extracted Triple: ( x , r , y ) v refersTo( x , A) ^ refersTo( y , C) ^ canBeDeduced(A, R, C) refersTo ( r , R)

11. DBrev on Information Extraction and Integration Retrieval & Discovery Search and rank knowledge In probabilistic setting, ranking is the only meaningful search semantics (R , Dalvi, Suciu: VLDB 07, Weikum et al.: CACM 09). Retrieval & Discovery in DBrev: partnerOf locatedIn Microsoft $x US certifiedBy SPARQL / Conjunctive Datalog / NAGA

12. DBrev on Information Extraction and Integration Retrieval & Discovery Search and rank knowledge In probabilistic setting, ranking is the only meaningful search semantics (R , Dalvi, Suciu: VLDB 07, Weikum et al.: CACM 09). Approximate Matching Entity / relationship similarity Reasoning over relationship properties Reasoning with temporal / spatial constraints Retrieval & Discovery in DBrev: partnerOf locatedIn Microsoft $x US User Preference Information needs freshness, accuracy, popularity Interests context, background, current interest certifiedBy SPARQL / Conjunctive Datalog / NAGA

13. Summary DBrev builds on large-scale factor graph to simultaneously approach: Retrieval & Discovery provenance context ambiguity consistency An inspiration to combine + DB & IR Statistical ML for the challenges ahead.