8 citations · 15 across the 7 of their papers we have counts for
7 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…
The World of Graph Databases from An Industry Perspective
Yuanyuan Tian
Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have eme…
Exact PPS Sampling with Bounded Sample Size
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
Probability proportional to size (PPS) sampling schemes with a target sample size aim to produce a sample comprising a specified number of items while ensuring that each item i…
WiSer: A Highly Available HTAP DBMS for IoT Applications
Ronald Barber, Christian Garcia-Arellano, Ronen Grosman +10
In a classic transactional distributed database management system (DBMS), write transactions invariably synchronize with a coordinator before final commitment. While enforcing seri…
Temporally-Biased Sampling Schemes for Online Model Management
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily,…
Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations (Extended Version)
Yingjun Wu, Jia Yu, Yuanyuan Tian +2
Database administrators construct secondary indexes on data tables to accelerate query processing in relational database management systems (RDBMSs). These indexes are built on top…
Temporally-Biased Sampling for Online Model Management
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily,…