Michele Linardi

482 total citations
15 papers, 313 citations indexed

About

Michele Linardi is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Michele Linardi has authored 15 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Signal Processing, 9 papers in Artificial Intelligence and 4 papers in Computer Networks and Communications. Recurrent topics in Michele Linardi's work include Time Series Analysis and Forecasting (15 papers), Music and Audio Processing (5 papers) and Data Management and Algorithms (5 papers). Michele Linardi is often cited by papers focused on Time Series Analysis and Forecasting (15 papers), Music and Audio Processing (5 papers) and Data Management and Algorithms (5 papers). Michele Linardi collaborates with scholars based in France, United States and Netherlands. Michele Linardi's co-authors include Themis Palpanas, Yan Zhu, Eamonn Keogh, Paul Boniol, Mohammed Meftah, Vassilis Christophides, Adel T. Abbas, V. Christophides and Claudia Paris and has published in prestigious journals such as Proceedings of the VLDB Endowment, Data Mining and Knowledge Discovery and The VLDB Journal.

In The Last Decade

Michele Linardi

15 papers receiving 307 citations

Peers

Michele Linardi

SP

AI

CNC

CVPR

EE

Jonathan R. Wells Australia

Hossein Rouhani Zeidanloo Malaysia

Muhamad Erza Aminanto Indonesia

Zahra Zamanzadeh Darban Australia

D. Vasumathi India

M. Sugumaran India

Tong Anh Tuan Vietnam

Elena Ikonomovska Slovenia

A. B. Siddique United States

Jonathan R. Wells Australia

Michele Linardi

74 ×0.3

SP

216 ×1.1

AI

107 ×1.7

CNC

28 ×0.8

CVPR

5 ×0.2

EE

Citations per year, relative to Michele Linardi Michele Linardi (= 1×) peers Jonathan R. Wells

Countries citing papers authored by Michele Linardi

Since Specialization

Citations

This map shows the geographic impact of Michele Linardi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michele Linardi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michele Linardi more than expected).

Fields of papers citing papers by Michele Linardi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michele Linardi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michele Linardi. The network helps show where Michele Linardi may publish in the future.

Co-authorship network of co-authors of Michele Linardi

This figure shows the co-authorship network connecting the top 25 collaborators of Michele Linardi. A scholar is included among the top collaborators of Michele Linardi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michele Linardi. Michele Linardi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

1.

Abbas, Adel T., et al.. (2023). Evaluating Explanation Methods of Multivariate Time Series Classification through Causal Lenses. SPIRE - Sciences Po Institutional REpository. 1–10. 2 indexed citations

2.

Abbas, Adel T., et al.. (2023). Towards Explainable AI4EO: An Explainable Deep Learning Approach for Crop Type Mapping using Satellite Images Time Series. University of Twente Research Information. 1088–1091. 1 indexed citations

3.

Boniol, Paul, et al.. (2021). Unsupervised and scalable subsequence anomaly detection in large data series. The VLDB Journal. 30(6). 909–931. 43 indexed citations

4.

Boniol, Paul, et al.. (2021). Correction to: Unsupervised and scalable subsequence anomaly detection in large data series. The VLDB Journal. 32(2). 469–469. 1 indexed citations

5.

Linardi, Michele, Yan Zhu, Themis Palpanas, & Eamonn Keogh. (2020). Matrix profile goes MAD: variable-length motif and discord discovery in data series. Data Mining and Knowledge Discovery. 34(4). 1022–1071. 44 indexed citations

6.

Linardi, Michele & Themis Palpanas. (2020). Scalable data series subsequence matching with ULISSE. The VLDB Journal. 29(6). 1449–1474. 20 indexed citations

7.

Linardi, Michele. (2020). Effective and Efficient Variable-Length Data Series Analytics. arXiv (Cornell University). 2 indexed citations

8.

Boniol, Paul, et al.. (2020). SAD: An Unsupervised System for Subsequence Anomaly Detection. 1778–1781. 14 indexed citations

9.

Boniol, Paul, et al.. (2020). Automated Anomaly Detection in Large Sequences. 1834–1837. 43 indexed citations

10.

Linardi, Michele & Themis Palpanas. (2018). Scalable, variable-length similarity search in data series: the ULISSE approach. Very Large Data Bases. 11(13). 2236–2248. 31 indexed citations

11.

Linardi, Michele, Yan Zhu, Themis Palpanas, & Eamonn Keogh. (2018). Matrix Profile X. 1053–1066. 49 indexed citations

12.

Linardi, Michele & Themis Palpanas. (2018). Scalable, variable-length similarity search in data series. Proceedings of the VLDB Endowment. 11(13). 2236–2248. 1 indexed citations

13.

Linardi, Michele, Yan Zhu, Themis Palpanas, & Eamonn Keogh. (2018). VALMOD. 1757–1760. 15 indexed citations

14.

Linardi, Michele & Themis Palpanas. (2018). ULISSE: ULtra Compact Index for Variable-Length Similarity Search in Data Series. 3. 1356–1359. 22 indexed citations

15.

Linardi, Michele & Themis Palpanas. (2018). Scalable, variable-length similarity search in data series. Proceedings of the VLDB Endowment. 11(13). 2236–2248. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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