2977781
Reinforcing Web-object Categorization Through Interrelationships
Existing categorization algorithms deal with homogeneous Web objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspect of the inter-object relationship is not sufficient to fully reveal the true categories of Web objects. In this paper, we propose a novel categorization algorithm, called the Iterative Reinforcement Categorization Algorithm (IRC), to exploit the full interrelationship between different types of Web objects on the Web, including Web pages and queries. IRC classifies the interrelated Web objects by iteratively reinforcing the individual classification results of different types of objects via their interrelationship. Experiments on a clickthrough-log dataset from the MSN search engine show that, in terms of the F1 measure, IRC achieves a 26.4% improvement over a pure content-based classification method. It also achieves a 21% improvement over a query-metadata-based method, as well as a 16.4% improvement on F1 measure over the well-known virtual document-based method. Our experiments show that IRC converges fast enough to be applicable to real world applications.
15099164
A scalable supervised algorithm for dimensionality reduction on streaming data
Algorithms on streaming data have attracted increasing attention in the past decade. Among them, dimensionality reduction algorithms are greatly interesting due to the desirability of real tasks. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most widely used dimensionality reduction approaches. However, PCA is not optimal for general classification problems because it is unsupervised and ignores valuable label information for classification. On the other hand, the performance of LDA is degraded when encountering limited available low-dimensional spaces and singularity problem. Recently, Maximum Margin Criterion (MMC) was proposed to overcome the shortcomings of PCA and LDA. Nevertheless, the original MMC algorithm could not satisfy the streaming data model to handle large-scale high-dimensional data set. Thus an effective, efficient and scalable approach is needed. In this paper, we propose a supervised incremental dimensionality reduction algorithm and its extension to infer adaptive low-dimensional spaces by optimizing the maximum margin criterion. Experimental results on a synthetic dataset and real datasets demonstrate the superior performance of our proposed algorithm on streaming data.
13595177
Multiple-Goal Recognition from Low-Level Signals
Researchers and practitioners from both the artificial intelligence and pervasive computing communities have been paying increasing attention to the task of inferring users' high-level goals from low-level sensor readings. A common assumption made by most approaches is that a user either has a single goal in mind, or achieves several goals sequentially. However, in real-world environments, a user often has multiple goals that are concurrently carried out, and a single action can serve as a common step towards multiple goals. In this paper, we formulate the multiple-goal recognition problem and exemplify it in an indoor environment where an RF-based wireless network is available. We propose a goal-recognition algorithm based on a dynamic model set and show how goal models evolve over time based on pre-defined states. Experiments with real data demonstrate that our method can accurately and efficiently recognize multiple interleaving goals in a user's trace.
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
9645876
Handling feature interactions in process-planning
Abstract The development of a successful approach to the integration of computer-aided design and computer-aided process-planning will require solutions to several fundamental problems in planning and geometric reasoning. In this article, we consider two such problems, both of which arise from interactions among machinable features. The first problem is that interactions can lead to several alternative interpretations of what the machinable features are. We address this problem by developing an algebra of features. The second problem is that interactions among features can cause problems in optimizing the process plans. For this problem, we have developed a branch-and-bound optimization algorithm with good heuristics to guide the search. This article presents the theory and mathematical framework for both problems and also the details of the implementation and experimental results.
11767978
Real-Time Scheduling for Multi-agent Call Center Automation
In a call center, service agents with different capabilities are available for solving incoming customer problems at any time. To supply quick response and better problem solution to customers, it is necessary to schedule customer problems to appropriate service agents efficiently. We developed SANet, a service agent network for call center, which integrates multiple service agents including both software agents and human agents, and employs a broker to schedule customer problems to service agents for better solutions according to their changing capabilities and availability. This paper describes the real-time scheduling method in SANet as well as its architecture. There are two phases in our scheduling method. One is problem-type learning. The broker is trained to learn the problem types and hence can decide the type of incoming problems automatically. The other is the scheduling algorithm based on problem types, capabilities and availability of service agents. We highlight an application in which we apply SANet to a call center problem for a cable-TV company. Finally, we support our claims via experimental results and discuss related works.
6173911
A caching strategy to improve iSCSI performance
iSCSI is one of the most recent standards that allows SCSI protocols to be carried out over IP networks. However, to encapsulate the SCSI protocol over IP requires a significant amount of overhead traffic for SCSI commands transfers and handshaking over the Internet. In this paper, we propose a caching scheme, called iCache, to improve the iSCSI performance. iCache uses a log disk along with a piece of non-volatile RAM to cache the iSCSI traffic. Through an efficient caching algorithm, iCache can significantly improve performance over current iSCSI systems. Numerical results using popular benchmark program and real world trace have shown dramatic performance gain.
8600912
Performance Evaluation of Distributed Web Server Architectures under E-Commerce Workloads
One of the central and key components to make the ECommerce a success is high performance and highly reliable web server architectures. Extensive research has been sparked aiming at improving web server performance. Fundamental to the goal of improving web server performance is a solid understanding of behavior and performance of web servers. However, very little research is found on evaluating web server performance based on realistic workload representing E-Commerce applications which are usually exemplified by a large amount of CGI (Common Gateway Interface), ASP (Active Server Page), or Servlet (Java Server-side interface) calls. This paper presents a performance study under the workload with a mixture of static web page requests, CGI requests, Servlet requests, and database queries. System throughputs and user response times are measured for five different server architectures consisting of PCs that run both a web server program and a database. We observed that performance behaviors of the web server architectures considered under this mixed workload are quite different from that under static page workload and sometimes counter-intuitive. Our performance results suggest that there is a large room for potential performance improvement for web servers.
20437986
Design and analysis of multiple-bus arbiters with different priority schemes
The authors present four multiple-user and multiple-server arbiter designs of multiple-bus multiprocessors. All designs are based on decentralized control, which has the advantage of good reliability and potentially high throughput. The priority protocols considered are fixed priority, rotating priority, round-robin priority, and equal priority. For the first two priority protocols, lookahead techniques are used to speed up the arbitration process. The round-robin protocol is based on the decentralized daisy-chain idea and is operated in an asynchronous fashion. Analytical models are developed for the four different priority protocols to evaluate the performance of the different designs. The analyses are based on a probabilistic model for the synchronous case and simple queuing analysis for the asynchronous system. Numerical results obtained from the proposed model show that the equal priority protocol performs better than the others in the system with a few buses. The performance difference is not significant when the number of buses is relatively large.<<ETX>>
7030953
Energy-aware partitioning for multiprocessor real-time systems
In this paper, we address the problem of partitioning periodic real-time tasks in a multiprocessor platform by considering both feasibility and energy-awareness perspectives: our objective is to compute the feasible partitioning that results in minimum energy consumption on multiple identical processors by using variable voltage earliest-deadline-first scheduling. We show that the problem is NP-hard in the strong sense on m /spl ges/ 2 processors even when feasibility is guaranteed a priori. Then, we develop our framework where load balancing plays a major role in producing energy-efficient partitionings. We evaluate the feasibility and energy-efficiency performances of partitioning heuristics experimentally.
9377268
Introducing SCSI-to-IP cache for storage area networks
Data storage plays an essential role in today's fast-growing data-intensive network services. iSCSI is one of the most recent standards that allow SCSI protocols to be carried out over IP networks. However, the disparities between SCSI and IP prevent fast and efficient deployment of SAN (storage area network) over IP. This paper introduces STICS (SCSI-To-IP cache storage), a novel storage architecture that couples reliable and high-speed data caching with low-overhead conversion between SCSI and IP protocols. Through the efficient caching algorithm and localization of certain unnecessary protocol overheads, STICS significantly improves performance over current iSCSI system. Furthermore, STICS can be used as a basic plug-and-play building block for data storage over IP. We have implemented software STICS prototype on Linux operating system. Numerical results using popular PostMark benchmark program and EMC's trace have shown dramatic performance gain over the current iSCSI implementation.