Data Like This: Ranked Search of Genomic Data Vision Paper

Authors

Veronika Margaret Megler, Portland State University
David Maier, Portland State University
Daniel Bottomly, Oregon Health and Science University
Libbey White, Oregon Health and Science University
Shannon McWeeney, Oregon Health and Science University
Beth Wilmot, Oregon Health and Science University

Published In

Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web

Document Type

Citation

Publication Date

2015

Subjects

Information retrieval, Database management, Data mining

Abstract

High-throughput genetic sequencing produces the ultimate "big data": a human genome sequence contains more than 3B base pairs, and more and more characteristics, or annotations, are being recorded at the base-pair level. Locating areas of interest within the genome is a challenge for researchers, limiting their investigations. We describe our vision of adapting "big data" ranked search to the problem of searching the genome. Our goal is to make searching for data as easy for scientists as searching the Internet.

Locate the Document

http://dx.doi.org/10.1145/2795218.2795221

DOI

10.1145/2795218.2795221

Persistent Identifier

http://archives.pdx.edu/ds/psu/20875

Citation Details

V. M. Megler, David Maier, Daniel Bottomly, Libbey White, Shannon McWeeney, and Beth Wilmot. 2015. Data Like This: Ranked Search of Genomic Data Vision Paper. In Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web (ExploreDB '15), Georgia Koutrika, Laks V. S. Lakshmanan, Mirek Riedewald, and Kostas Stefanidis (Eds.). ACM, New York, NY, USA, 3-5.