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Research#

My research focused on modeling and querying data with uncertainty: time series and streams whose values are noisy or incomplete, graphs whose edges may or may not exist, and the data cleaning that comes before any of it. I earned my Ph.D. in Computer Science at the University of Trento (2009–2014), advised by Prof. Themis Palpanas. Dissertation: "Modeling and Querying Data Series and Data Streams with Uncertainty".

Along the way I spent six months as a visiting researcher with the data management and analytics groups at the IBM T.J. Watson Research Center (USA) and three months at the Qatar Computing Research Institute (Qatar). The work appeared in SIGMOD, VLDB, EDBT, SSDBM, KAIS and DKE.

Google Scholar profile Consulting experience

Themes#

  • Uncertain time series

    How to compare and search data series when each value carries uncertainty: similarity measures, correlation-aware distances, and top-k nearest neighbor search. Five papers, 2011–2017.

  • Uncertain graphs

    Classifying nodes in networks whose edges exist only with a probability, with Charu Aggarwal at IBM Research. Two papers, 2014 and 2018.

  • Data streams

    Sliding windows over uncertain streams in IBM's System S, and finding frequent items in ad-hoc recent time windows. Two papers, 2013 and 2015.

  • Data cleaning

    NADEEF, a commodity data cleaning system built at the Qatar Computing Research Institute with Ihab Ilyas. SIGMOD 2013.

Publications#

Year Title Authors Venue
2018 Improving Classification Quality in Uncertain Graphs Michele Dallachiesa, Charu Aggarwal, Themis Palpanas ACM Journal of Data and Information Quality (JDIQ)
2017 Similarity Using Correlation-Aware Measures Katsiaryna Mirylenka, Michele Dallachiesa, Themis Palpanas International Conference on Scientific and Statistical Database Management (SSDBM), Chicago, USA
2017 Correlation-Aware Distance Measures for Data Series Katsiaryna Mirylenka, Michele Dallachiesa, Themis Palpanas International Conference on Extending Database Technology (EDBT), Italy
2015 Top-k Nearest Neighbor Search In Uncertain Data Series Michele Dallachiesa, Themis Palpanas, Ihab F. Ilyas Proceedings of the VLDB Endowment (PVLDB) Journal 8(1)
2015 Sliding windows over uncertain data streams Michele Dallachiesa, Gabriela Jacques-Silva, Bugra Gedik, Kun-Lung Wu, Themis Palpanas Knowledge and Information Systems (KAIS) Journal 45
2014 Node classification in uncertain graphs Michele Dallachiesa, Charu Aggarwal, Themis Palpanas International Conference on Scientific and Statistical Database Management (SSDBM), Aalborg, Denmark
2013 NADEEF: a commodity data cleaning system Michele Dallachiesa, Amr Ebaid, Ahmed Eldawy, Ahmed Elmagarmid, Ihab Ilyas, Mourad Ouzzani, and Nan Tang ACM SIGMOD International Conference on Management of Data (SIGMOD), New York City, NY, USA
2013 Identifying Streaming Frequent Items In Ad-hoc Recent Time Windows Michele Dallachiesa and Themis Palpanas Data & Knowledge Engineering (DKE) Journal
2012 Uncertain Time-Series Similarity: Return to the Basics Michele Dallachiesa, Besmira Nushi, Katsiaryna Mirylenka, Themis Palpanas Proceedings of the VLDB Endowment (PVLDB) Journal, Turkey
2011 Similarity Matching for Uncertain Time Series: Analytical and Experimental Comparison Michele Dallachiesa, Besmira Nushi, Katsiaryna Mirylenka, Themis Palpanas ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data (QUeST), Chicago, USA

Advisor, Prof. Themis Palpanas, Paris Descartes University

"Michele is able to grasp new ideas and concepts, and in general, learn fast. The leading abilities of Michele are evident in his work: he is capable of independent thinking, and of delivering novel and effective solutions to hard problems. He combines a very solid theoretical background with excellent practical skills."

From research to practice#

The same questions run through my consulting work: how much to trust a forecast, how to put honest bounds on a prediction, and how to evaluate a system before relying on it. Conformal prediction in railway cost forecasting, explainable risk flags for megaprojects and urban forests, and the evaluation frameworks for voice agents all start from there.