10975724
Adding Semantics to Email Clustering
This paper presents a novel algorithm to cluster emails according to their contents and the sentence styles of their subject lines. In our algorithm, natural language processing techniques and frequent itemset mining techniques are utilized to automatically generate meaningful generalized sentence patterns (GSPs) from subjects of emails. Then we put forward a novel unsupervised approach which treats GSPs as pseudo class labels and conduct email clustering in a supervised manner, although no human labeling is involved. Our proposed algorithm is not only expected to improve the clustering performance, it can also provide meaningful descriptions of the resulted clusters by the GSPs. Experimental results on open dataset (Enron email dataset) and a personal email dataset collected by ourselves demonstrate that the proposed algorithm outperforms the K-means algorithm in terms of the popular measurement Fl. Furthermore, the cluster naming readability is improved by 68.5% on the personal email dataset.
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.
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.
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.
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.
42167729
Computation of chain queries in distributed database systems
27106762
Analysis and Comparison of Cache Coherence Protocols for a Packet-Switched Multiprocessor
Analytical models are developed for seven existing cache protocols, namely, Write-Once, Write-Through, Synapse, Berkeley, Illinois, Firefly, and Dragon. The protocols are implemented on a multiprocessor with a packet-switched shared bus. The models are based on queuing networks that consist of both open and closed classes of customers. The models incorporate the requests for invalidation signals, write-through, and write-back operations, and the solution is based on the mean value analysis (MVA) algorithm. The performance of these protocols under various system parameters is compared on the basis of the models. It is found that Firefly and Dragon perform better than the others. >
1859019
A parallel algorithm for clustering protein-protein interaction networks
The increasing availability of interaction graphs requires new resource-efficient tools capable of extracting valuable biological knowledge from these networks. In this paper we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that is capable of running on clusters of computers. Our parallel implementation achieves almost linear speed-up up to 32 processors and allows us to run this computationally intensive algorithm on large protein-protein interaction networks. Preliminary experiments show that the algorithm has very high accuracy in identifying functional related protein modules.
31570297
Performance of Multiple-Bus Interconnections for Multiprocessors
Abstract Bus structures, in general, are easily understood and therefore preferred by manufactures for implementation. Multiple-bus systems can be viewed as an incremental expansion of single-bus architectures that can provide high bandwidth and reliability. This research note provides a brief overview of various analytical techniques suitable for performance evaluation of the multiple-bus multiprocessors. Some results and comparisons based on these techniques are also provided.
5698182
Measurement, analysis and performance improvement of the Apache Web server
Performance of Web servers is critical to the success of many corporations and organizations. However, very few results have been published that quantitatively study the server behavior and identify the performance bottlenecks. In this paper we measured and analyzed the behavior of the popular Apache Web server on a uniprocessor system and a 4-CPU SMP (Symmetric Multi-Processor) system running the IBM AIX operating system. Using the AIX built-in tracing facility, we obtained detailed information on kernel events and system activities while running Apache driven by the SPECweb96 and the WebStone benchmarks. After quantitatively identifying the performance bottlenecks, we proposed and implemented 6 techniques that improve the throughput of Apache by 61%. These techniques are general purpose and can be applied to other Web servers as well.