Abdullah Mueen is a Professor in Computer Science at the University of New Mexico. He joined UNM as an Assistant Professor in 2013. Earlier he was a Scientist in the Cloud and Information Sciences Lab at Microsoft Corporation.

For computing the similarity, we use Mueen’s ultra-fast Algorithm for Similarity Search (MASS), because MASS is algorithmically efficient and free of hyperparameters.

Abdullah Mueen is an Assistant Professor in Computer Science at University of New Mexico since 2013. Earlier he was a Scientist in the Cloud and Information Science Lab at Microsoft Corporation. His major interest is in temporal data mining with a focus on two unique types of signals: social networks and electrical sensors.

Abdullah Mueen. Assistant Professor. University of New Mexico. Department of Computer Science

Abdullah Mueen: DiffuScope: inferring post-specific diffusion network. In the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and

Abdullah Mueen is an assistant professor at the department of computer science in the University of New Mexico. Earlier, he served as a scientist in Microsoft Corporation (2012-2013). Dr. Mueen is interested in analyzing large data to discover patterns and anomalies.

Abdullah Mueen, Suman Nath, Jie Liu: Fast approximate correlation for massive time-series data. SIGMOD Conference 2010: 171-182

Abdullah Mueen University of New Mexico 1 University Blvd Albuquerque, NM 87131 [email protected] ABSTRACT Modern engineering incorporates smart technologies in all aspects of our lives. Smart technologies are generating ter-abytes of log messages every day to report their status. It is crucial to analyze these log messages and present usable

A Mueen, N Chavoshi, N Abu-El-Rub, H Hamooni, A Minnich, Fast Warping Distance for Sparse Time Series, Technical Report at UNM: http://www.cs.unm.edu/~mueen/Projects/AWarp/awarp.pdf

Mueen . What are Time Series? 1 of 2 A time series is a collection of observations made sequentially in time. More than most types of data, time series lend themselves to visual