READY: Representation Independent Data Analytics

The output of database exploration and analytics algorithms highly depend on the structure and representation of their input data. To use current database analytics algorithms, users have to find the desired representation for these algorithms and transform (wrangle) their data to these representations. These tasks are hard and time-consuming and major obstacles for unlocking the value of data. RIDE aims at developing algorithms that return the desired results no matter how their input data is represented. Check out our vision paper for more information.

Selected Publication

• Logically Scalable and Efficient Relational Learning
  Jose Picado, Arash Termehchy, Alan Fern, and Parisa Ataie
  The VLDB Journal, to appear.
• Managing Structurally Heterogeneous Databases in Software Product Lines
  Parisa Ataei, Arash Termehchy, and Eric Walkingshaw
  The Proceedings of Poly, September 2018.
• Learning Efficiently Over Heterogenous Databases    [Poster]
  Jose Picado, Sudhanshu Pathak, and Arash Termehchy
  The Proceedings of the VLDB Endowment (Demonstration Track) , August 2018.
• Cost-Effective Conceptual Design Using Taxonomies
  Yodsawalai Chodpathumwan, Ali Vakilian, Arash Termehchy and Amir Nayyeri
  The VLDB Journal, April 2018.
• AutoMode: Relational Learning With Less Black Magic
  Jose Picado, Sudhanshu Pathak, Arash Termehchy, and Alan Fern
  The Proceedings of ICDE, April 2018.
• Variational Databases
  Parisa Ataie, Arash Termehchy, and Eric Walkingshaw
  The Proceedings of DBPL, September 2017.
• Schema Independent Relational Learning    [One-slide teaser]    [Slides]
  Jose Picado, Arash Termehchy, Alan Fern, and Parisa Ataie
  The Proceedings of SIGMOD, May 2017.
• Cost-Effective Conceptual Design Over Taxonomies    [Slides]
  Yodsawalai Chodpathumwan, Ali Vakilian, Arash Termehchy and Amir Nayyeri
  The Proceedings of WebDB, May 2017.
• Representational Scalability
  Jose Picado
  The Conference on Innovative Data Systems Research (CIDR), abstract, January 2017.
• Towards Representation Independent Similarity and Proximity Search
  Yodsawalai Chodpathumwan, Amirhossein Aleyasin, Arash Termehchy, and Yizhou Sun
  The Proceedings of CIKM, October 2016.
  
• Schema Independent and Scalable Relational Learning By Castor
  Jose Picado, Parisa Ataie, Arash Termehchy, and Alan Fern
  The Proceedings of the VLDB Endowment (PVLDB), September 2016.
• Universal-DB: Towards Representation Independent Graph Analytics
  Yodsawalai Chodpathumwan, Amirhossein Aleyasin, Arash Termehchy and Yizhou Sun
  The Proceedings of the VLDB Endowment (PVLDB), September 2015.
• Cost Effective Conceptual Design for Information Extraction
  Arash Termehchy, Ali Vakilian, Yodswalai Chodpathumwan and Marianne Winslett
  The ACM Transactions on Database Systems (TODS), June 2015.
• Which Concepts Are Worth Extracting?    [Slides]
  Arash Termehchy, Ali Vakilian, Yodswalai Chodpathumwan, and Marianne Winslett
  The Proceedings of SIGMOD, June 2014.

People

• Arash Termehchy
• Jose Picado
• Yodsawalai Chodpathumwan
• Sudhanshu Shobhakant Pathak