Paper Info
Title
The unique strengths and storage access characteristics of discard-based search
Abstract
Discard-based search is a new approach to searching the content of complex, unlabeled, nonindexed data such as digital photographs, medical images, and real-time surveillance data. The essence of this approach is query-specific content-based computation, pipelined with human cognition. In this approach, query-specific parallel computation shrinks a search task down to human scale, thus allowing the expertise, judgment, and intuition of an expert to be brought to bear on the specificity and selectivity of the search. In this paper, we report on the lessons learned in the Diamond project from applying discard-based search to a variety of applications in the health sciences. From the viewpoint of a user, discard-based search offers unique strengths. From the viewpoint of server hardware and software, it offers unique opportunities for optimization that contradict long-established tenets of storage design. Together, these distinctive end-to-end attributes herald a new genre of Internet applications.
Year
DOI
Venue
2010
10.1007/s13174-010-0001-z
J. Internet Services and Applications
Keywords
Field
DocType
data-intensive computing · non-text search technology · medical image processing · interactive search · computer vision · pattern recognition · distributed systems · imagej · matlab · parallel processing · human-in-the-loop · diamond · opendiamond · storage systems · i/o workloads · raid,distributed system,human cognition,parallel computer,data intensive computing,parallel processing,real time,computer vision,pattern recognition,storage system
Human scale,Data-intensive computing,Information retrieval,Computer science,Software,Computer Applications,RAID,Human-in-the-loop,Multimedia,Distributed computing,The Internet,Computation
Journal
Volume
Issue
ISSN
1
1
1869-0238
Citations 
PageRank 
References 
5
0.54
14
Authors
6
Name
Order
Citations
PageRank
M. Satyanarayanan187741707.65
Rahul Sukthankar26137365.45
Lily B. Mummert330763.38
Adam Goode41156.12
Jan Harkes520624.04
Steven W. Schlosser629923.66