most citedFunctional Dependencies Unleashed for Scalable Data Exchange

7 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DB2020★ 2 cited

Effective and Efficient Variable-Length Data Series Analytics

Michele Linardi

In the last twenty years, data series similarity search has emerged as a fundamental operation at the core of several analysis tasks and applications related to data series collect…

cs.DB2020★ 2 cited

Scalable Data Series Subsequence Matching with ULISSE

Michele Linardi, Themis Palpanas

Data series similarity search is an important operation and at the core of several analysis tasks and applications related to data series collections. Despite the fact that data se…

cs.DB2020★ 2 cited

Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series

Michele Linardi, Yan Zhu, Themis Palpanas +1

In the last fifteen years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, in…

cs.DB2020

VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series

Michele Linardi, Yan Zhu, Themis Palpanas +1

Data series motif discovery represents one of the most useful primitives for data series mining, with applications to many domains, such as robotics, entomology, seismology, medici…

cs.DB2016★ 7 cited

Functional Dependencies Unleashed for Scalable Data Exchange

Angela Bonifati, Ioana Ileana, Michele Linardi

We address the problem of efficiently evaluating target functional dependencies (fds) in the Data Exchange (DE) process. Target fds naturally occur in many DE scenarios, including…