S
Susan V. Vrbsky
Researcher at University of Alabama
Publications - 84
Citations - 1187
Susan V. Vrbsky is an academic researcher from University of Alabama. The author has contributed to research in topics: Cloud computing & Consistency (database systems). The author has an hindex of 19, co-authored 84 publications receiving 1160 citations. Previous affiliations of Susan V. Vrbsky include University of Illinois at Urbana–Champaign.
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Proceedings ArticleDOI
Comparing NoSQL MongoDB to an SQL DB
TL;DR: This paper compares one of the NoSQL solutions, MongoDB, to the standard SQL relational database, SQL Server, and results show that MongoDB performs equally as well or better than the relationaldatabase, except when aggregate functions are utilized.
Journal ArticleDOI
APPROXIMATE-a query processor that produces monotonically improving approximate answers
Susan V. Vrbsky,Jialu Liu +1 more
TL;DR: APPROXIMATE, a query processor that makes approximate answers available if part of the database is unavailable, or if there is not enough time to produce an exact answer, is described.
Journal ArticleDOI
An on-line replication strategy to increase availability in Data Grids
TL;DR: This paper describes two new metrics to evaluate the reliability of the system, and proposes an on-line optimizer algorithm that can Minimize the Data Missing Rate (MinDmr) in order to maximize the data availability.
Proceedings ArticleDOI
A Virtual Password Scheme to Protect Passwords
TL;DR: A virtual password concept involving a small amount of human computing to secure users' passwords in on-line environments is proposed and adopted based on the fact that a server has more information than any adversary does.
Journal ArticleDOI
Virtual password using random linear functions for on-line services, ATM machines, and pervasive computing
TL;DR: This paper proposes a virtual password concept involving a small amount of human computing to secure users' passwords in on-line environments, ATMs, and pervasive computing, and adopts user-determined randomized linear generation functions to secureusers' passwords based on the fact that a server has more information than any adversary does.