Automatic Fault Behavior Detection and Modeling by a State-Based Specification Method. - Luca Pazzi; Matteo Interlandi; Marco Pradelli (HASE, 2010)

MediaPresenter, a Web Platform for Multimedia Content Management. - Sonia Bergamaschi; Fabio Ferrari; Matteo Interlandi; Maurizio Vincini (SEBD, 2011)

Reasoning about Knowledge in Distributed Systems Using Datalog. - Matteo Interlandi (Datalog, 2012)

Knowlog: A Declarative Language for Reasoning about Knowledge in Distributed Systems. - Matteo Interlandi (ER, 2012)

A Meta-language for MDX Queries in eLog Business Solution. - Sonia Bergamaschi; Matteo Interlandi; Mario Longo; Laura Po; Maurizio Vincini (ICDE, 2012)

Enhancing Datalog with Epistemic Operators to Reason About Knowledge in Distributed Systems. - Matteo Interlandi (SEBD, 2012)

Datalog in Time and Space, Synchronously. - Matteo Interlandi; Letizia Tanca; Sonia Bergamaschi (AMW, 2013)

Using a HMM based approach for mapping keyword queries into database terms. - Sonia Bergamaschi; Francesco Guerra 001; Matteo Interlandi; Silvia Rota; Raquel Trillo; Yannis Velegrakis (SEBD, 2013)

QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques. - Sonia Bergamaschi; Francesco Guerra 001; Matteo Interlandi; Raquel Trillo Lado; Yannis Velegrakis (Proc. VLDB Endow., 2013)

Towards Declarative Imperative Data-parallel Systems. - Matteo Interlandi; Giovanni Simonini; Sonia Bergamaschi (SEBD, 2014)

On the CALM Principle for Bulk Synchronous Parallel Computation. - Matteo Interlandi; Letizia Tanca (CoRR, 2014)

On the CALM Principle for BSP Computation. - Matteo Interlandi; Letizia Tanca (AMW, 2015)

Proof positive and negative in data cleaning. - Matteo Interlandi; Nan Tang 001 (ICDE, 2015)

Titian: Data Provenance Support in Spark. - Matteo Interlandi; Kshitij Shah; Sai Deep Tetali; Muhammad Ali Gulzar; Seunghyun Yoo; Miryung Kim; Todd D. Millstein; Tyson Condie (Proc. VLDB Endow., 2015)

The Magic of Pushing Extrema into Recursion: Simple, Powerful Datalog Programs. - Carlo Zaniolo; Mohan Yang; Ariyam Das; Matteo Interlandi (AMW, 2016)

Programming and Runtime Support to Blaze FPGA Accelerator Deployment at Datacenter Scale. - Muhuan Huang; Di Wu 010; Cody Hao Yu; Zhenman Fang; Matteo Interlandi; Tyson Condie; Jason Cong (SoCC, 2016)

Optimizing Interactive Development of Data-Intensive Applications. - Matteo Interlandi; Sai Deep Tetali; Muhammad Ali Gulzar; Joseph Noor; Tyson Condie; Miryung Kim; Todd D. Millstein (SoCC, 2016)

Interactive Debugging for Big Data Analytics. - Muhammad Ali Gulzar; Xueyuan Han; Matteo Interlandi; Shaghayegh Mardani; Sai Deep Tetali; Todd D. Millstein; Miryung Kim (HotCloud, 2016)

BigDebug: debugging primitives for interactive big data processing in spark. - Muhammad Ali Gulzar; Matteo Interlandi; Seunghyun Yoo; Sai Deep Tetali; Tyson Condie; Todd D. Millstein; Miryung Kim (ICSE, 2016)

Big Data Analytics with Datalog Queries on Spark. - Alexander Shkapsky; Mohan Yang; Matteo Interlandi; Hsuan Chiu; Tyson Condie; Carlo Zaniolo (SIGMOD Conference, 2016)

BigDebug: interactive debugger for big data analytics in Apache Spark. - Muhammad Ali Gulzar; Matteo Interlandi; Tyson Condie; Miryung Kim (SIGSOFT FSE, 2016)

Combining user and database perspective for solving keyword queries over relational databases. - Sonia Bergamaschi; Francesco Guerra 001; Matteo Interlandi; Raquel Trillo Lado; Yannis Velegrakis (Inf. Syst., 2016)

Automated debugging in data-intensive scalable computing. - Muhammad Ali Gulzar; Matteo Interlandi; Xueyuan Han; Mingda Li; Tyson Condie; Miryung Kim (SoCC, 2017)

Towards Accelerating Generic Machine Learning Prediction Pipelines. - Alberto Scolari; Yunseong Lee; Markus Weimer; Matteo Interlandi (ICCD, 2017)

Cleaning MapReduce Workflows. - Matteo Interlandi; Julien Lacroix; Omar Boucelma; Francesco Guerra 001 (HPCS, 2017)

Debugging Big Data Analytics in Spark with BigDebug. - Muhammad Ali Gulzar; Matteo Interlandi; Tyson Condie; Miryung Kim (SIGMOD Conference, 2017)

Apache REEF: Retainable Evaluator Execution Framework. - Byung-Gon Chun; Tyson Condie; Yingda Chen; Brian Cho; Andrew Chung; Carlo Curino; Chris Douglas; Matteo Interlandi; Beomyeol Jeon; Joo Seong Jeong; Gyewon Lee; Yunseong Lee; et al. (ACM Trans. Comput. Syst., 2017)

Fixpoint semantics and optimization of recursive Datalog programs with aggregates. - Carlo Zaniolo; Mohan Yang; Ariyam Das; Alexander Shkapsky; Tyson Condie; Matteo Interlandi (Theory Pract. Log. Program., 2017)

Declarative BigData Algorithms via Aggregates and Relational Database Dependencies. - Carlo Zaniolo; Mohan Yang; Matteo Interlandi; Ariyam Das; Alexander Shkapsky; Tyson Condie (AMW, 2018)

RIOS: Runtime Integrated Optimizer for Spark. - Youfu Li 003; Mingda Li; Ling Ding 002; Matteo Interlandi (SoCC, 2018)

Declarative Algorithms in Datalog with Extrema: Their Formal Semantics Simplified. - Carlo Zaniolo; Mohan Yang; Matteo Interlandi; Ariyam Das; Alexander Shkapsky; Tyson Condie (ICLP (Technical Communications), 2018)

PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. - Yunseong Lee; Alberto Scolari; Byung-Gon Chun; Marco Domenico Santambrogio; Markus Weimer; Matteo Interlandi (OSDI, 2018)

Supporting Data Provenance in Data-Intensive Scalable Computing Systems. - Matteo Interlandi; Tyson Condie (IEEE Data Eng. Bull., 2018)

From the Edge to the Cloud: Model Serving in ML.NET. - Yunseong Lee; Alberto Scolari; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (IEEE Data Eng. Bull., 2018)

Scaling-up reasoning and advanced analytics on BigData. - Tyson Condie; Ariyam Das; Matteo Interlandi; Alexander Shkapsky; Mohan Yang; Carlo Zaniolo (Theory Pract. Log. Program., 2018)

A datalog-based computational model for coordination-free, data-parallel systems. - Matteo Interlandi; Letizia Tanca (Theory Pract. Log. Program., 2018)

Adding data provenance support to Apache Spark. - Matteo Interlandi; Ari Ekmekji; Kshitij Shah; Muhammad Ali Gulzar; Sai Deep Tetali; Miryung Kim; Todd D. Millstein; Tyson Condie (VLDB J., 2018)

Understanding Data in the Blink of an Eye. - Matteo Paganelli; Paolo Sottovia; Antonio Maccioni; Matteo Interlandi; Francesco Guerra 001 (CIKM, 2019)

Acorn: Aggressive Result Caching in Distributed Data Processing Frameworks. - Lana Ramjit; Matteo Interlandi; Eugene Wu 002; Ravi Netravali (SoCC, 2019)

Coded Elastic Computing. - Yaoqing Yang; Matteo Interlandi; Pulkit Grover; Soummya Kar; Saeed Amizadeh; Markus Weimer (ISIT, 2019)

Machine Learning at Microsoft with ML.NET. - Zeeshan Ahmed; Saeed Amizadeh; Mikhail Bilenko; Rogan Carr; Wei-Sheng Chin; Yael Dekel; Xavier Dupré; Vadim Eksarevskiy; Senja Filipi; Tom Finley; Abhishek Goswami; Monte Hoover; et al. (KDD, 2019)

Machine Learning at Microsoft with ML .NET. - Zeeshan Ahmed; Saeed Amizadeh; Mikhail Bilenko; Rogan Carr; Wei-Sheng Chin; Yael Dekel; Xavier Dupré; Vadim Eksarevskiy; Eric Erhardt; Costin Eseanu; Senja Filipi; Tom Finley; et al. (CoRR, 2019)

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. - Gyeong-In Yu; Saeed Amizadeh; Artidoro Pagnoni; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (CoRR, 2019)

Data Science through the looking glass and what we found there. - Fotis Psallidas; Yiwen Zhu; Bojan Karlas; Matteo Interlandi; Avrilia Floratou; Konstantinos Karanasos; Wentao Wu 001; Ce Zhang 001; Subru Krishnan; Carlo Curino; Markus Weimer (CoRR, 2019)

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 001; et al. (CIDR, 2020)

Extending Relational Query Processing with ML Inference. - Konstantinos Karanasos; Matteo Interlandi; Fotis Psallidas; Rathijit Sen; Kwanghyun Park 001; Ivan Popivanov; Doris Xin; Supun Nakandala; Subru Krishnan; Markus Weimer; Yuan Yu; Raghu Ramakrishnan 001; et al. (CIDR, 2020)

SEIZE User Desired Moments: Runtime Inspection for Parallel Dataflow Systems. - Youfu Li 003; Matteo Interlandi; Fotis Psallidas; Wei Wang 010; Carlo Zaniolo (ICDCS, 2020)

Building Continuous Integration Services for Machine Learning. - Bojan Karlas; Matteo Interlandi; Cédric Renggli; Wentao Wu 001; Ce Zhang 001; Deepak Mukunthu Iyappan Babu; Jordan Edwards; Chris Lauren; Andy Xu; Markus Weimer (KDD, 2020)

A Tensor Compiler for Unified Machine Learning Prediction Serving. - Supun Nakandala; Karla Saur; Gyeong-In Yu; Konstantinos Karanasos; Carlo Curino; Markus Weimer; Matteo Interlandi (OSDI, 2020)

Explaining data with descriptions. - Matteo Paganelli; Paolo Sottovia; Antonio Maccioni; Matteo Interlandi; Francesco Guerra 001 (Inf. Syst., 2020)

Using Descriptions for Explaining Entity Matches (Discussion Paper). - Matteo Paganelli; Paolo Sottovia; Antonio Maccioni; Matteo Interlandi; Francesco Guerra 001 (SEBD, 2021)

Transforming ML Predictive Pipelines into SQL with MASQ. - Francesco Del Buono; Matteo Paganelli; Paolo Sottovia; Matteo Interlandi; Francesco Guerra 001 (SIGMOD Conference, 2021)

Steering Query Optimizers: A Practical Take on Big Data Workloads. - Parimarjan Negi; Matteo Interlandi; Ryan Marcus; Mohammad Alizadeh; Tim Kraska; Marc T. Friedman; Alekh Jindal (SIGMOD Conference, 2021)

Machine Learning for Cloud Data Systems: the Promise, the Progress, and the Path Forward. - Alekh Jindal; Matteo Interlandi (Proc. VLDB Endow., 2021)

Tensors: An abstraction for general data processing. - Dimitrios Koutsoukos; Supun Nakandala; Konstantinos Karanasos; Karla Saur; Gustavo Alonso; Matteo Interlandi (Proc. VLDB Endow., 2021)

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. - Gyeong-In Yu; Saeed Amizadeh; Sehoon Kim 001; Artidoro Pagnoni; Ce Zhang 001; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (Proc. VLDB Endow., 2021)

Phoebe: A Learning-based Checkpoint Optimizer. - Yiwen Zhu; Matteo Interlandi; Abhishek Roy 008; Krishnadhan Das; Hiren Patel; Malay Bag; Hitesh Sharma; Alekh Jindal (Proc. VLDB Endow., 2021)

SEIZE: Runtime Inspection for Parallel Dataflow Systems. - Youfu Li 003; Matteo Interlandi; Fotis Psallidas; Wei Wang 010; Carlo Zaniolo (IEEE Trans. Parallel Distributed Syst., 2021)

End-to-end Optimization of Machine Learning Prediction Queries. - Kwanghyun Park 001; Karla Saur; Dalitso Banda; Rathijit Sen; Matteo Interlandi; Konstantinos Karanasos (SIGMOD Conference, 2022)

Deploying a Steered Query Optimizer in Production at Microsoft. - Wangda Zhang; Matteo Interlandi; Paul Mineiro; Shi Qiao 001; Nasim Ghazanfari; Karlen Lie; Marc T. Friedman; Rafah Hosn; Hiren Patel; Alekh Jindal (SIGMOD Conference, 2022)

Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines. - Bojan Karlas; David Dao; Matteo Interlandi; Bo Li 026; Sebastian Schelter; Wentao Wu 001; Ce Zhang 001 (CoRR, 2022)

Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem. - Yuki Asada; Victor Fu; Apurva Gandhi; Advitya Gemawat; Lihao Zhang; Dong He 002; Vivek Gupta; Ehi Nosakhare; Dalitso Banda; Rathijit Sen; Matteo Interlandi (CoRR, 2022)

Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem. - Yuki Asada; Victor Fu; Apurva Gandhi; Advitya Gemawat; Lihao Zhang; Vivek Gupta; Ehi Nosakhare; Dalitso Banda; Rathijit Sen; Matteo Interlandi (Proc. VLDB Endow., 2022)

Query Processing on Tensor Computation Runtimes. - Dong He 002; Supun Chathuranga Nakandala; Dalitso Banda; Rathijit Sen; Karla Saur; Kwanghyun Park 001; Carlo Curino; Jesús Camacho-Rodríguez; Konstantinos Karanasos; Matteo Interlandi (Proc. VLDB Endow., 2022)

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines. - Fotis Psallidas; Yiwen Zhu; Bojan Karlas; Jordan Henkel; Matteo Interlandi; Subru Krishnan; Brian Kroth; K. Venkatesh Emani; Wentao Wu 001; Ce Zhang 001; Markus Weimer; Avrilia Floratou; et al. (SIGMOD Rec., 2022)

The Tensor Data Platform: Towards an AI-centric Database System. - Apurva Gandhi; Yuki Asada; Victor Fu; Advitya Gemawat; Lihao Zhang; Rathijit Sen; Carlo Curino; Jesús Camacho-Rodríguez; Matteo Interlandi (CIDR, 2023)

Query Processing on Gaming Consoles. - Wei Cui; Qianxi Zhang; Spyros Blanas; Jesús Camacho-Rodríguez; Brandon Haynes; Yinan Li; Ravi Ramamurthy; Peng Cheng 005; Rathijit Sen; Matteo Interlandi (DaMoN, 2023)

Unshackling Database Benchmarking from Synthetic Workloads. - Parimarjan Negi; Laurent Bindschaedler; Mohammad Alizadeh; Tim Kraska; Jyoti Leeka; Anja Gruenheid; Matteo Interlandi (ICDE, 2023)

Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques. - Matthias Boehm 001; Matteo Interlandi; Chris Jermaine (SIGMOD Conference Companion, 2023)

Towards Building Autonomous Data Services on Azure. - Yiwen Zhu; Yuanyuan Tian 001; Joyce Cahoon; Subru Krishnan; Ankita Agarwal; Rana Alotaibi; Jesús Camacho-Rodríguez; Bibin Chundatt; Andrew Chung; Niharika Dutta; Andrew Fogarty; Anja Gruenheid; et al. (SIGMOD Conference Companion, 2023)

Revisiting Query Performance in GPU Database Systems. - Jiashen Cao; Rathijit Sen; Matteo Interlandi; Joy Arulraj; Hyesoon Kim (CoRR, 2023)

Optimizing Data Pipelines for Machine Learning in Feature Stores. - Rui Liu; Kwanghyun Park 001; Fotis Psallidas; Xiaoyong Zhu; Jinghui Mo; Rathijit Sen; Matteo Interlandi; Konstantinos Karanasos; Yuanyuan Tian 001; Jesús Camacho-Rodríguez (Proc. VLDB Endow., 2023)

A Deep Dive into Common Open Formats for Analytical DBMSs. - Chunwei Liu; Anna Pavlenko; Matteo Interlandi; Brandon Haynes (Proc. VLDB Endow., 2023)

Pushing ML Predictions Into DBMSs. - Matteo Paganelli; Paolo Sottovia; Kwanghyun Park 001; Matteo Interlandi; Francesco Guerra 001 (IEEE Trans. Knowl. Data Eng., 2023)

GPU Database Systems Characterization and Optimization. - Jiashen Cao; Rathijit Sen; Matteo Interlandi; Joy Arulraj; Hyesoon Kim (Proc. VLDB Endow., 2023)

Pushing ML Predictions into DBMSs (Extended Abstract). - Matteo Paganelli; Paolo Sottovia; Kwanghyun Park 001; Matteo Interlandi; Francesco Guerra 001 (ICDE, 2024)

Data Debugging with Shapley Importance over Machine Learning Pipelines. - Bojan Karlas; David Dao; Matteo Interlandi; Sebastian Schelter; Wentao Wu 001; Ce Zhang 001 (ICLR, 2024)

Eighth Workshop on Data Management for End-to-End Machine Learning (DEEM). - Madelon Hulsebos; Matteo Interlandi; Shreya Shankar (SIGMOD Conference Companion, 2024)

FairnessEval: a Framework for Evaluating Fairness of Machine Learning Models. - Andrea Baraldi 002; Matteo Brucato; Miroslav Dudík; Francesco Guerra 001; Matteo Interlandi (EDBT, 2025)

Ninth Workshop on Data Management for End-to-End Machine Learning (DEEM). - Stefan Grafberger; Madelon Hulsebos; Matteo Interlandi; Shreya Shankar (SIGMOD Conference Companion, 2025)

Data formats in analytical DBMSs: performance trade-offs and future directions. - Chunwei Liu; Anna Pavlenko; Matteo Interlandi; Brandon Haynes (VLDB J., 2025)

Terabyte-Scale Analytics in the Blink of an Eye. - Bowen Wu; Wei Cui; Carlo Curino; Matteo Interlandi; Rathijit Sen (CoRR, 2025)

GPU Acceleration of SQL Analytics on Compressed Data. - Zezhou Huang; Krystian Sakowski; Hans Lehnert; Wei Cui; Carlo Curino; Matteo Interlandi; Marius Dumitru; Rathijit Sen (CoRR, 2025)