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| Creator | Title | Description | Subject | Date | ||
|---|---|---|---|---|---|---|
| 1 |
 | Regehr, John | Testing static analyzers with randomly generated programs | Static analyzers should be correct. We used the random C-program generator Csmith, initially intended to test C compilers, to test parts of the Frama-C static analysis platform. Although Frama-C was already relatively mature at that point, fifty bugs were found and fixed during the process, in the f... | 2012-01-01 | |
| 2 |
 | Freire, Juliana | Provenance in scientific workflow systems | The automated tracking and storage of provenance information promises to be a major advantage of scientific workflow systems. We discuss issues related to data and workflow provenance, and present techniques for focusing user attention on meaningful provenance through "user views," for managing the ... | Workflow systems; Provenance | 2007 |
| 3 |
 | Freire, Juliana; Silva, Claudio T. | Provenance for computational tasks: a survey | The problem of systematically capturing and managing provenance for computational tasks has recently received significant attention because of its relevance to a wide range of domains and applications. The authors give an overview of important concepts related to provenance management, so that poten... | Provenance management; Computational tasks | 2008-05 |
| 4 |
 | Hansen, Charles D. | Knowledge-based out-of-core algorithms for data management in visualization | Data management is the very first issue in handling very large datasets. Many existing out-of-core algorithms used in visualization are closely coupled with application-specific logic. This paper presents two knowledgebased out-of-core prefetching algorithms that do not use hard-coded rendering-re... | 2006 | |
| 5 |
 | Venkatasubramanian, Suresh | Efficient protocols for distributed classification and optimization | A recent paper [1] proposes a general model for distributed learning that bounds the communication required for learning classifiers with e error on linearly separable data adversarially distributed across nodes. In this work, we develop key improvements and extensions to this basic model. Our first... | 2012-01-01 |
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