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Full-Text Articles in Engineering
Propagation And Provenance Of Probabilistic And Interval Uncertainty In Cyberinfrastructure-Related Data Processing And Data Fusion, Paulo Pinheiro Da Silva, Aaron A. Velasco, Martine Ceberio, Christian Servin, Matthew G. Averill, Nicholas Ricky Del Rio, Luc Longpre, Vladik Kreinovich
Propagation And Provenance Of Probabilistic And Interval Uncertainty In Cyberinfrastructure-Related Data Processing And Data Fusion, Paulo Pinheiro Da Silva, Aaron A. Velasco, Martine Ceberio, Christian Servin, Matthew G. Averill, Nicholas Ricky Del Rio, Luc Longpre, Vladik Kreinovich
Departmental Technical Reports (CS)
In the past, communications were much slower than computations. As a result, researchers and practitioners collected different data into huge databases located at a single location such as NASA and US Geological Survey. At present, communications are so much faster that it is possible to keep different databases at different locations, and automatically select, transform, and collect relevant data when necessary. The corresponding cyberinfrastructure is actively used in many applications. It drastically enhances scientists' ability to discover, reuse and combine a large number of resources, e.g., data and services.
Because of this importance, it is desirable to be able to …
Why Intervals? Why Fuzzy Numbers? Towards A New Justification, Vladik Kreinovich
Why Intervals? Why Fuzzy Numbers? Towards A New Justification, Vladik Kreinovich
Departmental Technical Reports (CS)
The purpose of this paper is to present a new characterization of the set of all intervals (and of the corresponding set of fuzzy numbers). This characterization is based on several natural properties useful in mathematical modeling; the main of these properties is the necessity to be able to combine (fuse) several pieces of knowledge.
Interval Approach To Preserving Privacy In Statistical Databases: Related Challenges And Algorithms Of Computational Statistics, Luc Longpre, Gang Xiang, Vladik Kreinovich, Eric Freudenthal
Interval Approach To Preserving Privacy In Statistical Databases: Related Challenges And Algorithms Of Computational Statistics, Luc Longpre, Gang Xiang, Vladik Kreinovich, Eric Freudenthal
Departmental Technical Reports (CS)
In many practical situations, it is important to store large amounts of data and to be able to statistically process the data. A large part of the data is confidential, so while we welcome statistical data processing, we do not want to reveal sensitive individual data. If we allow researchers to ask all kinds of statistical queries, this can lead to violation of people's privacy. A sure-proof way to avoid these privacy violations is to store ranges of values (e.g., between 40 and 50 for age) instead of the actual values. This idea solves the privacy problem, but it leads …