21 citations · 53 across the 6 of their papers we have counts for
6 papers
Towards Building Autonomous Data Services on Azure
Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon +35
Modern cloud has turned data services into easily accessible commodities. With just a few clicks, users are now able to access a catalog of data processing systems for a wide range…
Sibyl: Forecasting Time-Evolving Query Workloads
Hanxian Huang, Tarique Siddiqui, Rana Alotaibi +6
Database systems often rely on historical query traces to perform workload-based performance tuning. However, real production workloads are time-evolving, making historical queries…
GEqO: ML-Accelerated Semantic Equivalence Detection
Brandon Haynes, Rana Alotaibi, Anna Pavlenko +3
Large scale analytics engines have become a core dependency for modern data-driven enterprises to derive business insights and drive actions. These engines support a large number o…
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino +19
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios inc…
STREAK: An Efficient Engine for Processing Top-k SPARQL Queries with Spatial Filters
Jyoti Leeka, Srikanta Bedathur, Debajyoti Bera +1
The importance of geo-spatial data in critical applications such as emergency response, transportation, agriculture etc., has prompted the adoption of recent GeoSPARQL standard in…
A Formal Graph Model for RDF and Its Implementation
Vinh Nguyen, Jyoti Leeka, Olivier Bodenreider +1
Formalizing an RDF abstract graph model to be compatible with the RDF formal semantics has remained one of the foundational problems in the Semantic Web. In this paper, we propose…