1 citations · 1 across the 4 of their papers we have counts for
5 papers
MuSAlS: A Fast Multiple Sequence Alignment Approach Using Hierarchical Clustering
Emily G. Light, Morgan Prior, Noah M. Daniels +1
Motivation: The multiple sequence alignment (MSA) problem has been extensively studied, with numerous approaches developed over recent years. With the rapid growth of sequence data…
Generalized compression and compressive search of large datasets
Morgan E. Prior, Thomas Howard, Emily Light +2
The Big Data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist…
Let them have CAKES: A Cutting-Edge Algorithm for Scalable, Efficient, and Exact Search on Big Data
Morgan E. Prior, Thomas J. Howard, Oliver McLaughlin +3
The ongoing Big Data explosion has created a demand for efficient and scalable algorithms for similarity search. Most recent work has focused on \textit{approximate} -NN search,…
Clustered Hierarchical Anomaly and Outlier Detection Algorithms
Najib Ishaq, Thomas J. Howard, Noah M. Daniels
Anomaly and outlier detection is a long-standing problem in machine learning. In some cases, anomaly detection is easy, such as when data are drawn from well-characterized distribu…
Clustered Hierarchical Entropy-Scaling Search of Astronomical and Biological Data
Najib Ishaq, George Student, Noah M. Daniels
Both astronomy and biology are experiencing explosive growth of data, resulting in a "big data" problem that stands in the way of a "big data" opportunity for discovery. One common…