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42,205
Simulation Methodology for Decision Support Workloads
"... The impact of any new architectural proposal must be evaluated under realistic working conditions. This class of analysis requires trustworthy simulation tools and representative workloads that allow us to know the real effectiveness of the improvement. In this work, we propose a methodology that en ..."
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that enables the use of an important family of transactional workloads, such as decision support system workloads, in a full system simulator. In contrast to numerical applications, with this type of workload it is not possible to scale down the problem size in order to reduce the computational requirements
Performance analysis of decision support workloads for the desktop environment
, 2003
"... Database applications are one of the fastest-growing classes of applications. Their execution characteristics differ from those of scientific or general-purpose applications. Due to their proprietary nature, software licenses typically prevent the disclosure of performance information. This has resu ..."
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future, it is likely that desktop environments may be the choice for small to medium sized database applications. We analyze the performance of Decision Support Systems for the desktop environment. We implement the TPC-H DSS benchmark using the SHORE storage manager on the Windows 2000 platform
Projecting the Performance of Decision Support Workloads on Systems with Smart Storage (SmartSTOR
- Computer Science Division, University of California, Berkeley
, 1999
"... Recent developments in both hardware and software have made it worthwhile to consider embedding intelligence in storage to handle general purpose processing that can be offloaded from the hosts. In particular, low-cost processing power is now widely available and software can be made robust, secure ..."
Abstract
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Cited by 21 (8 self)
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STOR architecture for decision support workloads since these workloads are increasingly important commercially and are known to be pushing the limits of current system designs. Our analysis suggests that there is a definite advantage in using fewer but more powerful processors, a result that bolsters the case
Projecting the Performance of Decision Support Workloads on Systems with Smart Storage (SmartSTOR) *
"... Recent developments in both hardware and software have made it worthwhile to consider embedding intelligence in storage to handle general purpose processing that can be ofloaded from the hosts. In particulal; low-cost processing power is now widely available and software can be made robust, secure a ..."
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STOR architecture for decision support workloads. Our analysis suggests that there is a definite performance advantage in using fewer but more powerful processors, a result that bolsters the case for sharing apow-erful processor among multiple disks. As for software archi-tecture, we find that the ofloading
Decision-support workload characteristics on clustered database server from the OS perspective
- In Proceedings of the International Conference on Distributed Conputing Systems(ICDCS
, 2003
"... chliu ¨ frankeh¨ ..."
Characterizing the Scalability of Decision-Support Workloads on Clusters and SMP Systems
"... Abstract. Using a public domain version of a commercial clustered database server and TPC-H like 3 decision support queries, this paper studies the performance and scalability issues of a Pentium/Linux clus-ter and an 8-way Linux SMP. The execution prole demonstrates the dominance of the I/O subsyst ..."
Abstract
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Cited by 1 (1 self)
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Abstract. Using a public domain version of a commercial clustered database server and TPC-H like 3 decision support queries, this paper studies the performance and scalability issues of a Pentium/Linux clus-ter and an 8-way Linux SMP. The execution prole demonstrates the dominance of the I
Support-Vector Networks
- Machine Learning
, 1995
"... The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special pr ..."
Abstract
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Cited by 3703 (35 self)
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The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
Abstract
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Cited by 727 (1 self)
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We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision
Transductive Inference for Text Classification using Support Vector Machines
, 1999
"... This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimiz ..."
Abstract
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Cited by 892 (4 self)
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This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try
Benchmarking Least Squares Support Vector Machine Classifiers
- NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
Abstract
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Cited by 476 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set
Results 1 - 10
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42,205
