Matteo Riondato

Contact info

Publications

My publications and citations on: DBLP, Google Scholar, AMiner, ResearchGate, ORCID iD iconORCID, ResearcherID, Scopus.

Here are the BibTeX entries for my papers.

Many of the linked PDFs are extended versions of the original publications.

Journal articles, Conference papers, Technical report, Tutorials

Journal Articles

  1. M. Riondato and E. Upfal ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages, ACM Transactions on Knowledge Discovery from Data, 12(5):61, 2018
  2. L. De Stefani, A. Epasto, M. Riondato, and E. Upfal. TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size, ACM Transactions on Knowledge Discovery from Data, 11(2):43, 2017
  3. M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, Data Mining and Knowledge Discovery, 31(2):314—349, 2017
  4. M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, Data Mining and Knowledge Discovery, 30(2):438—475, 2016
  5. M. Riondato and E. Upfal. Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees, ACM Transactions on Knowledge Discovery from Data, 8(4):20, 2014
  6. A. Pietracaprina, M. Riondato, E. Upfal, and F. Vandin. Mining Top-k Frequent Itemsets through Progressive Sampling, Data Mining and Knowledge Discovery, 21(2):310—326, 2010

Conference Papers

  1. L. Pellegrina, M. Riondato, and F. Vandin, SPuManTE: Significant Pattern Mining with Unconditional Testing, ACM KDD'19, 2019.
  2. S. Servan-Schreiber, M. Riondato, and E. Zgraggen. ProSecCo: Progressive Sequence Mining with Convergence Guarantees, IEEE ICDM'18, 2018
  3. M. Riondato and F. Vandin. MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension, ACM KDD'18, 2018
  4. M. Riondato and E. Upfal ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages, ACM KDD'16, 2016
  5. L. De Stefani, A. Epasto, M. Riondato, and E. Upfal. TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size, ACM KDD'16, 2016
  6. A. Mahmoody, M. Riondato, and E. Upfal. Wiggins: Detecting Valuable Information in Dynamic Networks with Limited Resources, ACM WSDM'16, 2016
  7. M. Riondato and E. Upfal. Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages, ACM KDD'15, 2015, Presentation Video
  8. A. Anagnastopoulos, L. Becchetti, A. Fazzone, I. Mele, and M. Riondato. The Importance of Being Experts: Efficient Max-Finding in Crowdsourcing, ACM SIGMOD'15, 2015
  9. M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, IEEE ICDM'14, 2014
  10. M. Riondato. Sampling-based Data Mining Algorithms: Modern Techniques and Case Studies, ECML PKDD'14, 2014
  11. M. Riondato and F. Vandin. Finding the True Frequent Itemsets, SIAM SDM'14, 2014
  12. M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, ACM WSDM'14, 2014
  13. M. Riondato, J. A. DeBrabant, R. Fonseca, and E. Upfal. PARMA: A Parallel Randomized Algorithm for Association Rules Mining in MapReduce, ACM CIKM'12, 2012
  14. M. Riondato and E. Upfal. Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees, ECML PKDD'12, 2012
  15. A. Pietracaprina, G. Pucci, M. Riondato, F. Silvestri, and E. Upfal. Space-round Tradeoffs for MapReduce Computations, ACM ICS'12, 2012
  16. M. Akdere, U. Çetintemel, M. Riondato, E. Upfal, and S. B. Zdonik. Learning-based Query Performance Modeling and Prediction, IEEE ICDE'12, 2012
  17. M. Riondato, M. Akdere, U. Çetintemel, S. B. Zdonik, and E. Upfal. The VC-dimension of SQL Queries and Selectivity Estimation through Sampling, ECML PKDD'11, 2011
  18. M. Akdere, U. Çetintemel, M. Riondato, E. Upfal, and S. B. Zdonik. The Case for Predictive Database Systems: Opportunities and Challenges, CIDR'11, 2011

Technical Reports

  1. M. Riondato. Sharpe Ratio: Estimation, Confidence Intervals, and Hypothesis Testing. Two Sigma Technical Report Series, 2018-001.

Tutorials

  1. L. Pellegrina, M. Riondato, and F. Vandin, Hypothesis Testing and Statistically-sound Pattern Mining (2-pager), ACM KDD'19 (Slides TBD).
  2. F. Bonchi, G. De Francisci Morales, and M. Riondato. Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms, WWW'16. Slides.
  3. M. Riondato and E. Upfal. VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms, ACM KDD'15, ECML PKDD'15, ACM CIKM'15. Slides, Video at KDD'15 (Part 1), Video at KDD'15 (Part 2)

PhD Dissertation

  1. M. Riondato. Sampling-based Randomized Algorithms for Big Data Analytics, Brown University Department of Computer Science, 2014

Other technical writings

  1. M. Riondato, Jails, in FreeBSD Handbook.

Coauthors

Mert Akdere, Aris Anagnostopoulos, Luca Becchetti, Francesco Bonchi, Uğur Çetintemel, Justin A. DeBrabant, Gianmarco De Francisci Morales, Lorenzo De Stefani, Rodrigo Fonseca, Alessandro Epasto, David García Soriano, Evgenios M. Kornaropoulos, Ahmad Mahmoody, Ida Mele, Leonardo Pellegrina, Andrea Pietracaprina, Geppino Pucci, Sacha Servan-Schreiber, Francesco Silvestri, Eli Upfal, Fabio Vandin, Stan B. Zdonik, Emanuel Zgraggen