Fotis Psallidas

Senior RSDE

출판물

자료 보기:

- Optimizing Data Pipelines for Machine Learning in Feature Stores
  
  Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez
  
  Proc. VLDB Endow. | August 2023, 제 16 권: pp. 4230-4239
  
  DOI PDF
- Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts
  
  Fotis Psallidas, Megan Eileen Leszczynski, Mohammad Hossein Namaki, Avrilia Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotić, Markus Weimer, Yinghui Wu, Yiwen Zhu
  
  ACM SIGMOD | July 2023
  
  Publication
- Schema Matching using Pre-Trained Language Models
  
  Yunjia Zhang, Avrilia Floratou, Joyce Cahoon, Subru Krishnan, Andreas C. Müller, Dalitso Banda, Fotis Psallidas, Jignesh M. Patel
  
  ICDE | January 2023
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 제 51 권(2): pp. 30-37
  
  Publication DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- Vamsa: Automated Provenance Tracking in Data Science Scripts
  
  Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yiwen Zhu, Markus Weimer, Yinghui Wu
  
  KDD | August 2020
  
  Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Physical Plan Instrumentation in Databases: Mechanisms and Applications
  
  Fotis Psallidas
  
  December 2018
  
  DOI Publication

- Optimizing Data Pipelines for Machine Learning in Feature Stores
  
  Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez
  
  Proc. VLDB Endow. | August 2023, 제 16 권: pp. 4230-4239
  
  DOI PDF
- Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts
  
  Fotis Psallidas, Megan Eileen Leszczynski, Mohammad Hossein Namaki, Avrilia Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotić, Markus Weimer, Yinghui Wu, Yiwen Zhu
  
  ACM SIGMOD | July 2023
  
  Publication
- Schema Matching using Pre-Trained Language Models
  
  Yunjia Zhang, Avrilia Floratou, Joyce Cahoon, Subru Krishnan, Andreas C. Müller, Dalitso Banda, Fotis Psallidas, Jignesh M. Patel
  
  ICDE | January 2023
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 제 51 권(2): pp. 30-37
  
  Publication DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- Vamsa: Automated Provenance Tracking in Data Science Scripts
  
  Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yiwen Zhu, Markus Weimer, Yinghui Wu
  
  KDD | August 2020
  
  Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 제 51 권(2): pp. 30-37
  
  Publication DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 제 51 권(2): pp. 30-37
  
  Publication DOI
- Physical Plan Instrumentation in Databases: Mechanisms and Applications
  
  Fotis Psallidas
  
  December 2018
  
  DOI Publication

- Optimizing Data Pipelines for Machine Learning in Feature Stores
  
  Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez
  
  Proc. VLDB Endow. | August 2023, 제 16 권: pp. 4230-4239
  
  DOI PDF
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 제 51 권(2): pp. 30-37
  
  Publication DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts
  
  Fotis Psallidas, Megan Eileen Leszczynski, Mohammad Hossein Namaki, Avrilia Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotić, Markus Weimer, Yinghui Wu, Yiwen Zhu
  
  ACM SIGMOD | July 2023
  
  Publication
- Schema Matching using Pre-Trained Language Models
  
  Yunjia Zhang, Avrilia Floratou, Joyce Cahoon, Subru Krishnan, Andreas C. Müller, Dalitso Banda, Fotis Psallidas, Jignesh M. Patel
  
  ICDE | January 2023
  
  Publication
- Vamsa: Automated Provenance Tracking in Data Science Scripts
  
  Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yiwen Zhu, Markus Weimer, Yinghui Wu
  
  KDD | August 2020
  
  Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Physical Plan Instrumentation in Databases: Mechanisms and Applications
  
  Fotis Psallidas
  
  December 2018
  
  DOI Publication

Fotis Psallidas

출판물

Optimizing Data Pipelines for Machine Learning in Feature Stores

Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts

Schema Matching using Pre-Trained Language Models

Data Science Through the Looking Glass

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines

Vamsa: Automated Provenance Tracking in Data Science Scripts

Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

Extending Relational Query Processing with ML Inference

Physical Plan Instrumentation in Databases: Mechanisms and Applications

Optimizing Data Pipelines for Machine Learning in Feature Stores

Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts

Schema Matching using Pre-Trained Language Models

Data Science Through the Looking Glass

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines

Vamsa: Automated Provenance Tracking in Data Science Scripts

Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

Extending Relational Query Processing with ML Inference

Data Science Through the Looking Glass

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines

Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

Extending Relational Query Processing with ML Inference

Data Science Through the Looking Glass

Physical Plan Instrumentation in Databases: Mechanisms and Applications

Optimizing Data Pipelines for Machine Learning in Feature Stores

Data Science Through the Looking Glass

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines

Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts

Schema Matching using Pre-Trained Language Models

Vamsa: Automated Provenance Tracking in Data Science Scripts

Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

Extending Relational Query Processing with ML Inference

Physical Plan Instrumentation in Databases: Mechanisms and Applications