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Institution

Hewlett-Packard

CompanyPalo Alto, California, United States
About: Hewlett-Packard is a company organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Signal & Substrate (printing). The organization has 34663 authors who have published 59808 publications receiving 1467218 citations. The organization is also known as: Hewlett Packard & Hewlett-Packard Company.


Papers
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Proceedings ArticleDOI
15 Aug 2011
TL;DR: DevoFlow is designed and evaluated, a modification of the OpenFlow model which gently breaks the coupling between control and global visibility, in a way that maintains a useful amount of visibility without imposing unnecessary costs.
Abstract: OpenFlow is a great concept, but its original design imposes excessive overheads. It can simplify network and traffic management in enterprise and data center environments, because it enables flow-level control over Ethernet switching and provides global visibility of the flows in the network. However, such fine-grained control and visibility comes with costs: the switch-implementation costs of involving the switch's control-plane too often and the distributed-system costs of involving the OpenFlow controller too frequently, both on flow setups and especially for statistics-gathering.In this paper, we analyze these overheads, and show that OpenFlow's current design cannot meet the needs of high-performance networks. We design and evaluate DevoFlow, a modification of the OpenFlow model which gently breaks the coupling between control and global visibility, in a way that maintains a useful amount of visibility without imposing unnecessary costs. We evaluate DevoFlow through simulations, and find that it can load-balance data center traffic as well as fine-grained solutions, without as much overhead: DevoFlow uses 10--53 times fewer flow table entries at an average switch, and uses 10--42 times fewer control messages.

1,132 citations

Proceedings ArticleDOI
19 May 2004
TL;DR: The new Semantic Web recommendations for RDF, RDFS and OWL have, at their heart, the RDF graph, and Jena2, a second-generation RDF toolkit, is similarly centered on the R DF graph.
Abstract: The new Semantic Web recommendations for RDF, RDFS and OWL have, at their heart, the RDF graph. Jena2, a second-generation RDF toolkit, is similarly centered on the RDF graph. RDFS and OWL reasoning are seen as graph-to-graph transforms, producing graphs of virtual triples. Rich APIs are provided. The Model API includes support for other aspects of the RDF recommendations, such as containers and reification. The Ontology API includes support for RDFS and OWL, including advanced OWL Full support. Jena includes the de facto reference RDF/XML parser, and provides RDF/XML output using the full range of the rich RDF/XML grammar. N3 I/O is supported. RDF graphs can be stored in-memory or in databases. Jena's query language, RDQL, and the Web API are both offered for the next round of standardization.

1,125 citations

Posted Content
TL;DR: The ROC convex hull (ROCCH) method as mentioned in this paper combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers.
Abstract: In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.

1,114 citations

Journal ArticleDOI
TL;DR: NVSim is developed, a circuit-level model for NVM performance, energy, and area estimation, which supports various NVM technologies, including STT-RAM, PCRAM, ReRAM, and legacy NAND Flash and is expected to help boost architecture-level NVM-related studies.
Abstract: Various new nonvolatile memory (NVM) technologies have emerged recently. Among all the investigated new NVM candidate technologies, spin-torque-transfer memory (STT-RAM, or MRAM), phase-change random-access memory (PCRAM), and resistive random-access memory (ReRAM) are regarded as the most promising candidates. As the ultimate goal of this NVM research is to deploy them into multiple levels in the memory hierarchy, it is necessary to explore the wide NVM design space and find the proper implementation at different memory hierarchy levels from highly latency-optimized caches to highly density- optimized secondary storage. While abundant tools are available as SRAM/DRAM design assistants, similar tools for NVM designs are currently missing. Thus, in this paper, we develop NVSim, a circuit-level model for NVM performance, energy, and area estimation, which supports various NVM technologies, including STT-RAM, PCRAM, ReRAM, and legacy NAND Flash. NVSim is successfully validated against industrial NVM prototypes, and it is expected to help boost architecture-level NVM-related studies.

1,100 citations

Book ChapterDOI
02 May 2004
TL;DR: In this paper, the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain, was considered and protocols based on the use of homomorphic encryption and balanced hashing were proposed.
Abstract: We consider the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain. This problem has many applications for online collaboration. We present protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments. For lists of length k, we obtain O(k) communication overhead and O(k ln ln k) computation. The protocol for the semi-honest environment is secure in the standard model, while the protocol for the malicious environment is secure in the random oracle model. We also consider the problem of approximating the size of the intersection, show a linear lower-bound for the communication overhead of solving this problem, and provide a suitable secure protocol. Lastly, we investigate other variants of the matching problem, including extending the protocol to the multi-party setting as well as considering the problem of approximate matching.

1,076 citations


Authors

Showing all 34676 results

NameH-indexPapersCitations
Andrew White1491494113874
Stephen R. Forrest1481041111816
Rafi Ahmed14663393190
Leonidas J. Guibas12469179200
Chenming Hu119129657264
Robert E. Tarjan11440067305
Hong-Jiang Zhang11246149068
Ching-Ping Wong106112842835
Guillermo Sapiro10466770128
James R. Heath10342558548
Arun Majumdar10245952464
Luca Benini101145347862
R. Stanley Williams10060546448
David M. Blei98378111547
Wei-Ying Ma9746440914
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202223
2021240
20201,028
20191,269
2018964