Matteo Interlandi

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 81

Top Topics

Machine Learning

Query Optimizer

Top Venues

Proc. VLDB Endow.

SIGMOD Conference

Publications

Bojan Karlas, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu, Ce Zhang
Data Debugging with Shapley Importance over Machine Learning Pipelines. ICLR (2024)
Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin Chundatt, Andrew Chung, Niharika Dutta, Andrew Fogarty, Anja Gruenheid, Brandon Haynes, Matteo Interlandi, Minu Iyer, Nick Jurgens, Sumeet Khushalani, Brian Kroth, Manoj Kumar, Jyoti Leeka, Sergiy Matusevych, Minni Mittal, Andreas Müller, Kartheek Muthyala, Harsha Nagulapalli, Yoonjae Park, Hiren Patel, Anna Pavlenko, Olga Poppe, Santhosh Ravindran, Karla Saur, Rathijit Sen, Steve Suh, Arijit Tarafdar, Kunal Waghray, Demin Wang, Carlo Curino, Raghu Ramakrishnan
Towards Building Autonomous Data Services on Azure. CoRR (2024)
Matteo Paganelli, Paolo Sottovia, Kwanahvun Park, Matteo Interlandi, Francesco Guerra
Pushing ML Predictions into DBMSs (Extended Abstract). ICDE (2024)
Madelon Hulsebos, Matteo Interlandi, Shreya Shankar
Eighth Workshop on Data Management for End-to-End Machine Learning (DEEM). SIGMOD Conference Companion (2024)
Matthias Boehm, Matteo Interlandi, Chris Jermaine
Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques. SIGMOD Conference Companion (2023)
Jiashen Cao, Rathijit Sen, Matteo Interlandi, Joy Arulraj, Hyesoon Kim
GPU Database Systems Characterization and Optimization. Proc. VLDB Endow. 17 (3) (2023)
Chunwei Liu, Anna Pavlenko, Matteo Interlandi, Brandon Haynes
A Deep Dive into Common Open Formats for Analytical DBMSs. Proc. VLDB Endow. 16 (11) (2023)
Matteo Paganelli, Paolo Sottovia, Kwanghyun Park, Matteo Interlandi, Francesco Guerra
Pushing ML Predictions Into DBMSs. IEEE Trans. Knowl. Data Eng. 35 (10) (2023)
Parimarjan Negi, Laurent Bindschaedler, Mohammad Alizadeh, Tim Kraska, Jyoti Leeka, Anja Gruenheid, Matteo Interlandi
Unshackling Database Benchmarking from Synthetic Workloads. ICDE (2023)
Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez
Optimizing Data Pipelines for Machine Learning in Feature Stores. Proc. VLDB Endow. 16 (13) (2023)
Wei Cui, Qianxi Zhang, Spyros Blanas, Jesús Camacho-Rodríguez, Brandon Haynes, Yinan Li, Ravi Ramamurthy, Peng Cheng, Rathijit Sen, Matteo Interlandi
Query Processing on Gaming Consoles. DaMoN (2023)
Jiashen Cao, Rathijit Sen, Matteo Interlandi, Joy Arulraj, Hyesoon Kim
Revisiting Query Performance in GPU Database Systems. CoRR (2023)
Apurva Gandhi, Yuki Asada, Victor Fu, Advitya Gemawat, Lihao Zhang, Rathijit Sen, Carlo Curino, Jesús Camacho-Rodríguez, Matteo Interlandi
The Tensor Data Platform: Towards an AI-centric Database System. CIDR (2023)
Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin Chundatt, Andrew Chung, Niharika Dutta, Andrew Fogarty, Anja Gruenheid, Brandon Haynes, Matteo Interlandi, Minu Iyer, Nick Jurgens, Sumeet Khushalani, Brian Kroth, Manoj Kumar, Jyoti Leeka, Sergiy Matusevych, Minni Mittal, Andreas Mueller, Kartheek Muthyala, Harsha Nagulapalli, Yoonjae Park, Hiren Patel, Anna Pavlenko, Olga Poppe, Santhosh Ravindran, Karla Saur, Rathijit Sen, Steve Suh, Arijit Tarafdar, Kunal Waghray, Demin Wang, Carlo Curino, Raghu Ramakrishnan
Towards Building Autonomous Data Services on Azure. SIGMOD Conference Companion (2023)
Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Lihao Zhang, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi
Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem. Proc. VLDB Endow. 15 (12) (2022)
Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc T. Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal
Deploying a Steered Query Optimizer in Production at Microsoft. CoRR (2022)
Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos
End-to-end Optimization of Machine Learning Prediction Queries. CoRR (2022)
Bojan Karlas, David Dao, Matteo Interlandi, Bo Li, Sebastian Schelter, Wentao Wu, Ce Zhang
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines. CoRR (2022)
Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc T. Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal
Deploying a Steered Query Optimizer in Production at Microsoft. SIGMOD Conference (2022)
Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos
End-to-end Optimization of Machine Learning Prediction Queries. SIGMOD Conference (2022)
Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Lihao Zhang, Dong He, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi
Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem. CoRR (2022)
Apurva Gandhi, Yuki Asada, Victor Fu, Advitya Gemawat, Lihao Zhang, Rathijit Sen, Carlo Curino, Jesús Camacho-Rodríguez, Matteo Interlandi
The Tensor Data Platform: Towards an AI-centric Database System. CoRR (2022)
Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi
Query Processing on Tensor Computation Runtimes. CoRR (2022)
Dong He, Supun Chathuranga Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi
Query Processing on Tensor Computation Runtimes. Proc. VLDB Endow. 15 (11) (2022)
Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, K. Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines. SIGMOD Rec. 51 (2) (2022)
Alekh Jindal, Matteo Interlandi
Machine Learning for Cloud Data Systems: the Promise, the Progress, and the Path Forward. Proc. VLDB Endow. 14 (12) (2021)
Francesco Del Buono, Matteo Paganelli, Paolo Sottovia, Matteo Interlandi, Francesco Guerra
Transforming ML Predictive Pipelines into SQL with MASQ. SIGMOD Conference (2021)
Yiwen Zhu, Matteo Interlandi, Abhishek Roy, Krishnadhan Das, Hiren Patel, Malay Bag, Hitesh Sharma, Alekh Jindal
Phoebe: A Learning-based Checkpoint Optimizer. Proc. VLDB Endow. 14 (11) (2021)
Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc T. Friedman, Alekh Jindal
Steering Query Optimizers: A Practical Take on Big Data Workloads. SIGMOD Conference (2021)
Youfu Li, Matteo Interlandi, Fotis Psallidas, Wei Wang, Carlo Zaniolo
SEIZE: Runtime Inspection for Parallel Dataflow Systems. IEEE Trans. Parallel Distributed Syst. 32 (4) (2021)
Matteo Paganelli, Paolo Sottovia, Antonio Maccioni, Matteo Interlandi, Francesco Guerra
Using Descriptions for Explaining Entity Matches (Discussion Paper). SEBD (2021)
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. Proc. VLDB Endow. 15 (1) (2021)
Yiwen Zhu, Matteo Interlandi, Abhishek Roy, Krishnadhan Das, Hiren Patel, Malay Bag, Hitesh Sharma, Alekh Jindal
Phoebe: A Learning-based Checkpoint Optimizer. CoRR (2021)
Dimitrios Koutsoukos, Supun Nakandala, Konstantinos Karanasos, Karla Saur, Gustavo Alonso, Matteo Interlandi
Tensors: An abstraction for general data processing. Proc. VLDB Endow. 14 (10) (2021)
Matteo Paganelli, Paolo Sottovia, Antonio Maccioni, Matteo Interlandi, Francesco Guerra
Explaining data with descriptions. Inf. Syst. 92 (2020)
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
A Tensor Compiler for Unified Machine Learning Prediction Serving. CoRR (2020)
Youfu Li, Matteo Interlandi, Fotis Psallidas, Wei Wang, Carlo Zaniolo
SEIZE User Desired Moments: Runtime Inspection for Parallel Dataflow Systems. ICDCS (2020)
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
Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML. CIDR (2020)
Konstantinos Karanasos, Matteo Interlandi, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Doris Xin, Supun Nakandala, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
Extending Relational Query Processing with ML Inference. CIDR (2020)
Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
Building Continuous Integration Services for Machine Learning. KDD (2020)
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
A Tensor Compiler for Unified Machine Learning Prediction Serving. OSDI (2020)
Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer
Coded Elastic Computing. ISIT (2019)
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, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu
Machine Learning at Microsoft with ML.NET. KDD (2019)
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
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML. CoRR (2019)
Matteo Interlandi, Ravi Netravali, Lana Ramjit, Eugene Wu
Acorn: Aggressive Result Caching in Distributed Data Processing Frameworks. SoCC (2019)
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, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Shon Katzenberger, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Sarthak Shah, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu
Machine Learning at Microsoft with ML .NET. CoRR (2019)
Lana Ramjit, Matteo Interlandi, Eugene Wu, Ravi Netravali
Acorn: Aggressive Result Caching in Distributed Data Processing Frameworks. SoCC (2019)
Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandala, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
Extending Relational Query Processing with ML Inference. CoRR (2019)
Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer
Data Science through the looking glass and what we found there. CoRR (2019)
Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. CoRR (2019)
Matteo Paganelli, Paolo Sottovia, Antonio Maccioni, Matteo Interlandi, Francesco Guerra
Understanding Data in the Blink of an Eye. CIKM (2019)
Tyson Condie, Ariyam Das, Matteo Interlandi, Alexander Shkapsky, Mohan Yang, Carlo Zaniolo
Scaling-Up Reasoning and Advanced Analytics on BigData. CoRR (2018)
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
From the Edge to the Cloud: Model Serving in ML.NET. IEEE Data Eng. Bull. 41 (4) (2018)
Matteo Interlandi, Tyson Condie
Supporting Data Provenance in Data-Intensive Scalable Computing Systems. IEEE Data Eng. Bull. 41 (1) (2018)
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. OSDI (2018)
Carlo Zaniolo, Mohan Yang, Matteo Interlandi, Ariyam Das, Alexander Shkapsky, Tyson Condie
Declarative Algorithms in Datalog with Extrema: Their Formal Semantics Simplified. ICLP (Technical Communications) (2018)
Tyson Condie, Ariyam Das, Matteo Interlandi, Alexander Shkapsky, Mohan Yang, Carlo Zaniolo
Scaling-up reasoning and advanced analytics on BigData. Theory Pract. Log. Program. 18 (5-6) (2018)
Matteo Interlandi, Letizia Tanca
A datalog-based computational model for coordination-free, data-parallel systems. Theory Pract. Log. Program. 18 (5-6) (2018)
Carlo Zaniolo, Mohan Yang, Matteo Interlandi, Ariyam Das, Alexander Shkapsky, Tyson Condie
Declarative BigData Algorithms via Aggregates and Relational Database Dependencies. AMW (2018)
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. CoRR (2018)
Matteo Interlandi, Ari Ekmekji, Kshitij Shah, Muhammad Ali Gulzar, Sai Deep Tetali, Miryung Kim, Todd D. Millstein, Tyson Condie
Adding data provenance support to Apache Spark. VLDB J. 27 (5) (2018)
Youfu Li, Mingda Li, Ling Ding, Matteo Interlandi
RIOS: Runtime Integrated Optimizer for Spark. SoCC (2018)
Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer
Coded Elastic Computing. CoRR (2018)
Alberto Scolari, Yunseong Lee, Markus Weimer, Matteo Interlandi
Towards Accelerating Generic Machine Learning Prediction Pipelines. ICCD (2017)
Muhammad Ali Gulzar, Matteo Interlandi, Xueyuan Han, Mingda Li, Tyson Condie, Miryung Kim
Automated debugging in data-intensive scalable computing. SoCC (2017)
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, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Mariia Mykhailova, Shravan M. Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Markus Weimer, Youngseok Yang
Apache REEF: Retainable Evaluator Execution Framework. ACM Trans. Comput. Syst. 35 (2) (2017)
Matteo Interlandi, Julien Lacroix, Omar Boucelma, Francesco Guerra
Cleaning MapReduce Workflows. HPCS (2017)
Carlo Zaniolo, Mohan Yang, Matteo Interlandi, Ariyam Das, Alexander Shkapsky, Tyson Condie
Fixpoint Semantics and Optimization of Recursive Datalog Programs with Aggregates. CoRR (2017)
Muhammad Ali Gulzar, Matteo Interlandi, Tyson Condie, Miryung Kim
. SIGMOD Conference (2017)
Carlo Zaniolo, Mohan Yang, Ariyam Das, Alexander Shkapsky, Tyson Condie, Matteo Interlandi
Fixpoint semantics and optimization of recursive Datalog programs with aggregates. Theory Pract. Log. Program. 17 (5-6) (2017)
Muhammad Ali Gulzar, Matteo Interlandi, Tyson Condie, Miryung Kim
BigDebug: interactive debugger for big data analytics in Apache Spark. SIGSOFT FSE (2016)
Sonia Bergamaschi, Francesco Guerra, Matteo Interlandi, Raquel Trillo Lado, Yannis Velegrakis
Combining user and database perspective for solving keyword queries over relational databases. Inf. Syst. 55 (2016)
Carlo Zaniolo, Mohan Yang, Ariyam Das, Matteo Interlandi
The Magic of Pushing Extrema into Recursion: Simple, Powerful Datalog Programs. AMW (2016)
Muhammad Ali Gulzar, Xueyuan Han, Matteo Interlandi, Shaghayegh Mardani, Sai Deep Tetali, Todd D. Millstein, Miryung Kim
Interactive Debugging for Big Data Analytics. HotCloud (2016)
Matteo Interlandi, Sai Deep Tetali, Muhammad Ali Gulzar, Joseph Noor, Tyson Condie, Miryung Kim, Todd D. Millstein
Optimizing Interactive Development of Data-Intensive Applications. SoCC (2016)
Muhammad Ali Gulzar, Matteo Interlandi, Seunghyun Yoo, Sai Deep Tetali, Tyson Condie, Todd D. Millstein, Miryung Kim
BigDebug: debugging primitives for interactive big data processing in spark. ICSE (2016)
Alexander Shkapsky, Mohan Yang, Matteo Interlandi, Hsuan Chiu, Tyson Condie, Carlo Zaniolo
Big Data Analytics with Datalog Queries on Spark. SIGMOD Conference (2016)
Muhuan Huang, Di Wu, Cody Hao Yu, Zhenman Fang, Matteo Interlandi, Tyson Condie, Jason Cong
Programming and Runtime Support to Blaze FPGA Accelerator Deployment at Datacenter Scale. SoCC (2016)
Matteo Interlandi, Letizia Tanca
On the CALM Principle for BSP Computation. AMW (2015)
Matteo Interlandi, Kshitij Shah, Sai Deep Tetali, Muhammad Ali Gulzar, Seunghyun Yoo, Miryung Kim, Todd D. Millstein, Tyson Condie
Titian: Data Provenance Support in Spark. Proc. VLDB Endow. 9 (3) (2015)
Matteo Interlandi, Nan Tang
Proof positive and negative in data cleaning. ICDE (2015)
Matteo Interlandi, Letizia Tanca
On the CALM Principle for Bulk Synchronous Parallel Computation. CoRR (2014)
Matteo Interlandi, Giovanni Simonini, Sonia Bergamaschi
Towards Declarative Imperative Data-parallel Systems. SEBD (2014)
Sonia Bergamaschi, Francesco Guerra, Matteo Interlandi, Silvia Rota, Raquel Trillo, Yannis Velegrakis
Using a HMM based approach for mapping keyword queries into database terms. SEBD (2013)
Matteo Interlandi, Letizia Tanca, Sonia Bergamaschi
Datalog in Time and Space, Synchronously. AMW (2013)
Sonia Bergamaschi, Francesco Guerra, Matteo Interlandi, Raquel Trillo Lado, Yannis Velegrakis
QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques. Proc. VLDB Endow. 6 (12) (2013)
Matteo Interlandi
Reasoning about Knowledge in Distributed Systems Using Datalog. Datalog (2012)
Sonia Bergamaschi, Matteo Interlandi, Mario Longo, Laura Po, Maurizio Vincini
A Meta-language for MDX Queries in eLog Business Solution. ICDE (2012)
Matteo Interlandi
Enhancing Datalog with Epistemic Operators to Reason About Knowledge in Distributed Systems. SEBD (2012)
Matteo Interlandi
Knowlog: A Declarative Language for Reasoning about Knowledge in Distributed Systems. ER (2012)
Sonia Bergamaschi, Fabio Ferrari, Matteo Interlandi, Maurizio Vincini
MediaPresenter, a Web Platform for Multimedia Content Management. SEBD (2011)
Luca Pazzi, Matteo Interlandi, Marco Pradelli
Automatic Fault Behavior Detection and Modeling by a State-Based Specification Method. HASE (2010)