11135944
Fuzzy Clustering of Web Logs for User Classification
5900836
The TV-tree: An index structure for high-dimensional data
We propose a file structure to index high-dimensionality data, which are typically points in some feature space. The idea is to use only a few of the features, using additional features only when the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such “varying length” feature vectors. Finally, we report simulation results, comparing the proposed structure with theR*-tree, which is one of the most successful methods for low-dimensionality spaces.The results illustrate the superiority of our method, which saves up to 80% in disk accesses.
1387511
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in k-d space, using k feature-extraction functions, provided by a domain expert [25]. Thus, we can subsequently use highly fine-tuned spatial access methods (SAMs), to answer several types of queries, including the 'Query By Example' type (which translates to a range query); the 'all pairs' query (which translates to a spatial join [8]); the nearest-neighbor or best-match query, etc.However, designing feature extraction functions can be hard. It is relatively easier for a domain expert to assess the similarity/distance of two objects. Given only the distance information though, it is not obvious how to map objects into points.This is exactly the topic of this paper. We describe a fast algorithm to map objects into points in some k-dimensional space (k is user-defined), such that the dis-similarities are preserved. There are two benefits from this mapping: (a) efficient retrieval, in conjunction with a SAM, as discussed before and (b) visualization and data-mining: the objects can now be plotted as points in 2-d or 3-d space, revealing potential clusters, correlations among attributes and other regularities that data-mining is looking for.We introduce an older method from pattern recognition, namely, Multi-Dimensional Scaling (MDS) [51]; although unsuitable for indexing, we use it as yardstick for our method. Then, we propose a much faster algorithm to solve the problem in hand, while in addition it allows for indexing. Experiments on real and synthetic data indeed show that the proposed algorithm is significantly faster than MDS, (being linear, as opposed to quadratic, on the database size N), while it manages to preserve distances and the overall structure of the data-set.
2416912
Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem
We propose combining advanced statistical approaches with data mining techniques to build classifiers to enhance decision-making models for the job assignment problem. Adaptive Generalized Estimation Equation (AGEE) approaches with Gibbs sampling under Bayesian framework and adaptive Bayes classifiers based on the estimations of AGEE models which uses modified Naive Bayes algorithm are proposed. The proposed classifiers have several important features. Firstly, it accounts for the correlation among the outputs and the indeterministic subjective noise into the estimation of parameters. Secondly, it reduces the number of attributes used to predict the class. Moreover, it drops the assumption of independence made by the Naive Bayes classifier. We apply our techniques to the problem of assigning jobs to Navy officers, with the goal of enhancing happiness for both the Navy and the officers. The classification results were compared with nearest neighbor, Multi-Layer Perceptron and Support Vector Machine approaches.
18266651
A similarity-based soft clustering algorithm for documents
Document clustering is an important tool for applications such as Web search engines. Clustering documents enables the user to have a good overall view of the information contained in the documents that he has. However, existing algorithms suffer from various aspects, hard clustering algorithms (where each document belongs to exactly one cluster) cannot detect the multiple themes of a document, while soft clustering algorithms (where each document can belong to multiple clusters) are usually inefficient. We propose SISC (similarity-based soft clustering), an efficient soft clustering algorithm based on a given similarity measure. SISC requires only a similarity measure for clustering and uses randomization to help make the clustering efficient. Comparison with existing hard clustering algorithms like K-means and its variants shows that SISC is both effective and efficient.
12237012
Discovering the Biomedical Deep Web
The rapid growth of biomedical information in the Deep Web has produced unprecedented challenges for traditional search engines. This paper describes a new Deep web resource discovery system for biomedical information. We designed two hypertext mining applications: a Focused Crawler that selectively seeks out relevant pages using a classifier that evaluates the relevance of the document with respect to biomedical information, and a Query Interface Extractor that extracts information from the page to detect the presence of a Deep Web database. Our anecdotes suggest that combining focused crawling with query interface extraction is very effective for building high-quality collections of Deep Web resources on biomedical topics.
27737931
Designing multimedia applications on real-time systems with SMP architecture
Large real-time multimedia systems such as flight simulators have adopted multiprocessor architecture to handle the large amount of events and computations. Existing realtime scheduling algorithms designed for single-processor systems such as Rate Monotonic (RM) and Earliest Deadline First Algorithm (EDF) have very poor performance on multiprocessor systems. On the other hand, most online scheduling algorithms for multiprocessor systems have poor schedulability conditions for real-time applications. In this paper, we study different ways to design real-time applications on SMP machines.
41885018
A theory of lexicographic multi-criteria optimization
The field of multi-criteria optimization is reviewed as it pertains to lexicographic optimization over real-valued vector spaces. How lexicographic optimization differs from multi-criteria optimization that is restricted to proper Pareto optima is explained. Through a survey of previous work, it is revealed that there are currently no generally applicable methods for solving lexicographic optimization problems, and it is explained that this is due to the lack of an adequate mathematical theory for such problems. A more adequate mathematical theory is then presented for lexicographic optimization in this paper.
22001959
A Pinwheel Scheduler for Three Distinct Numbers with a Tight Schedulability Bound
Abstract. Given a multiset of positive integers 
 $A=\{a_1,a_2,\ldots,a_n\}$ , the pinwheel problem is to find an infinite sequence over 
 $\{1,2,\ldots,n\}$ such that there is at least one symbol i within any subsequence of length ai. The density of A is defined as 
 $\rho(A)=\sum^n_{i=1} (1/a_i)$ . In this paper we limit ourselves to instances composed of three distinct integers. The best scheduler [5] published previously can schedule all instances with a density of less than 0.77. A new and fast scheduling scheme based on spectrum partitioning is presented in this paper which improves the 0.77 result to a new 
 $\frac{5}{6}\approx 0.83$ density threshold. This scheduler has achieved the tight schedulability bound of this problem.
1566772
A General Resource Management Framework for Real-Time Operating Systems
In RED-Linux kernel, a General Scheduling Framework (GSF) has been implemented. Under GSF, different scheduling algorithms can be easily implemented. However GSF only addresses the scheduling of CPU. Other system resources are not considered. In this paper we propose a general resource management framework for various OS resources. When user applications request and consume system resources, they may use uniform APIs although each type of OS resource has its own properties and means to be controlled. In the resource management framework, we also allow the inter-relationship between different types of resources to be defined and managed.
15213655
The design and implementation of intelligent transportation Web services
The paper presents the design and implementation of an intelligent transportation Web service (ITWS) and studies various QoS requirements for the Web service infrastructure. In particular, we pay attention to the performance issues on ITWS data transmission and compression. Since SOAP message is a textual document, we can improve the data transmission time by compressing data on transmission. In our experiment, compressing data before or after SOAP serialization both can reduce the size of transmitted message down to as little as 2% and cut the response time to a quarter of the original message.