Rana Alotaibi

Co-founder (Stealth Mode)

Google Scholar DBLP

Bio

With over a decade of experience in research and developing data analytics platforms, Rana Alotaibi is currently co-founding a startup operating in stealth mode and is an Assistant Professor at KACST. Previously, she was a Senior Scientist at Microsoft Gray Systems Lab (GSL) in Redmond, WA, where she focused on query optimization within Microsoft Fabric's unified Lakehouse ecosystem to accelerate cloud analytics. She began her career at IBM Almaden Research Center in San Jose, CA, working on query optimization for hybrid data stores.

Alotaibi earned her Ph.D. and M.Sc. in Computer Science from UC San Diego, specializing in semantic query optimization for data systems. Her research has been published in leading conferences, including SIGMOD, VLDB, ICDE, and CIDR. She holds five U.S. patents and, together with a distinguished team of data systems scientists, received the Microsoft Patent Award in both 2023 and 2024. She also actively contributes to the research community by serving on the program committees of leading data systems conferences, including SIGMOD and VLDB.

Research

Research focuses on query and workload optimization, autonomous data services, and data systems storage.

Publications

  1. I Can't Believe It's Not Yannakakis: Pragmatic Bitmap Filters in Microsoft SQL Server Hangdong Zhao, Yuanyuan Tian, Rana Alotaibi, Bailu Ding, Nicolas Bruno, Jesús Camacho-Rodríguez, Vassilis Papadimos, Ernesto Cervantes Juárez, César A. Galindo-Legaria, Carlo Curino CIDR 2026
  2. Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? Rana Alotaibi, Yuanyuan Tian, Stefan Grafberger, Jesús Camacho-Rodríguez, Nicolas Bruno, Brian Kroth, Sergiy Matusevych, Ashvin Agrawal, Mahesh Behera, Ashit Gosalia, César A. Galindo-Legaria, Milind Joshi, Milan Potocnik, Beysim Sezgin, Xiaoyu Li, Carlo Curino CIDR 2025
  3. MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud Brian Kroth, Sergiy Matusevych, Rana Alotaibi, Yiwen Zhu, Anja Gruenheid, Yuanyuan Tian VLDB 2024 (Demo)
  4. SIBYL: Characterizing and Forecasting Evolving Query Workloads Hanxian Huang, Tarique Siddiqui, Rana Alotaibi, Carlo Curino, Jyoti Leeka, Alekh Jindal, Jishen Zhao, Jesús Camacho-Rodríguez, Yuanyuan Tian SIGMOD 2024
  5. NL2SQL is a solved problem... Not! Avrilia Floratou, Fotis Psallidas, Fuheng Zhao, Shaleen Deep, Gunther Hagleither, Wangda Tan, Joyce Cahoon, Rana Alotaibi, Jordan Henkel, Abhik Singla, Alex Van Grootel, Brandon Chow, Kai Deng, Katherine Lin, Marcos Campos, Venkatesh Emani, Vivek Pandit, Victor Shnayder, Wenjing Wang, Carlo Curino CIDR 2024
  6. Towards Building Autonomous Data Services on Azure The GSL Team SIGMOD 2023 (Industry)
  7. Heterogeneous Data Management Revisited: from Mediators to Modern Polystores Rana Alotaibi, Maxime Buron, Alin Deutsch, François Goasdoué, Ioana Manolescu French Summer School on Massive Distributed Data (MDD) 2022
  8. HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries Rana Alotaibi, Bogdan Cautis, Alin Deutsch, Ioana Manolescu SIGMOD 2021
  9. HERMES: Data Placement and Schema Optimization for Enterprise Knowledge Bases Chuan Lei, Abdul Quamar, Vasilis Efthymiou, Fatma Özcan, Rana Alotaibi The VLDB Journal 2021
  10. Property Graph Schema Optimization for Domain-Specific Knowledge Graphs Rana Alotaibi, Chuan Lei, Abdul Quamar, Vasilis Efthymiou, Fatma Özcan ICDE 2021
  11. ESTOCADA: Towards Scalable Polystore Systems Rana Alotaibi, Bogdan Cautis, Alin Deutsch, Moustafa Latrache, Ioana Manolescu, Yifei Yang VLDB 2020 (Demo)
  12. Towards Scalable Hybrid Stores: Constraint-based Rewriting to the Rescue Rana Alotaibi, Bogdan Cautis, Alin Deutsch, Moustafa Latrache, Ioana Manolescu, Yifei Yang SIGMOD 2019