1265290
Prediction of Web Page Accesses by Proxy Server Log
As the population of web users grows, the variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. Recently, many efforts have been made to analyze user behaviors on the WWW. In this paper, we represent user behaviors by sequences of consecutive web page accesses, derived from the access log of a proxy server. Moreover, the frequent sequences are discovered and organized as an index. Based on the index, we propose a scheme for predicting user requests and a proxy-based framework for prefetching web pages. We perform experiments on real data. The results show that our approach makes the predictions with a high degree of accuracy with little overhead. In the experiments, the best hit ratio of the prediction achieves 75.69%, while the longest time to make a prediction only requires 2.3 ms.
6184263
An Efficient Approach to Extracting Approximate Repeating Patterns in Music Databases
Pattern extraction from music strings is an important problem. The patterns extracted from music strings can be used as features for music retrieval or analysis. Previous works on music pattern extraction only focus on exact repeating patterns. However, music segments with minor differences may sound similar. The concept of the prototypical melody has therefore been proposed to represent these similar music segments. In musicology, the number of music segments that are similar to a prototypical melody implies the importance degree of the prototypical melody to the music work. In this paper, a novel approach is developed to extract all the prototypical melodies in a music work. Our approach considers each music segment as a candidate for the prototypical melody and uses the edit distance to determine the set of music segments that are similar to this candidate. A lower bounding mechanism, which estimates the number of similar music segments for each candidate and prunes the impossible candidates is designed to speed up the process. Experiments are performed on a real data set and the results show a significant improvement of our approach over the existing approaches in the average response time.
5950644
Hiding Sensitive Association Rules with Limited Side Effects
Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries. The experimental results show the effectiveness of our approach, i.e., undesired side effects are avoided in the rule hiding process. The results also report that in most cases, all the sensitive rules are hidden without spurious rules falsely generated. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed
13420664
Music Classification Using Significant Repeating Patterns
With the popularity of multimedia applications, a large amount of music data has been accumulated on the Internet. Automatic classification of music data becomes a critical technique for providing an efficient and effective retrieval of music data. In this paper, we propose a new approach for classifying music data based on their contents. In this approach, we focus on monophonic music features represented as rhythmic and melodic sequences. Moreover, we use repeating patterns of music data to do music classification. For each pattern discovered from a group of music data, we employ a series of measurements to estimate its usefulness for classifying this group of music data. According to the patterns contained in a music piece, we determine which class it should be assigned to. We perform a series of experiments and the results show that our approach performs on average better than the approach based on the probability distribution of contextual information in music.
13332463
Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window
Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. In this paper, we develop a novel approach for mining frequent itemsets from data streams based on a time-sensitive sliding window model. Our approach consists of a storage structure that captures all possible frequent itemsets and a table providing approximate counts of the expired data items, whose size can be adjusted by the available storage space. Experiment results show that in our approach both the execution time and the storage space remain small under various parameter settings. In addition, our approach guarantees no false alarm or no false dismissal to the results yielded.
6547998
Efficient kNN search in polyphonic music databases using a lower bounding mechanism
AbstractQuerying polyphonic music from a large data collection is an
interesting topic. Recently, researchers have attempted to provide efficient methods for content-based retrieval in polyphonic music databases where queries are polyphonic. However, most of them do not work well for similarity search, which is important to many applications. In this paper, we propose three polyphonic representations with the associated similarity measures and a novel method to retrieve k music works that contain segments most similar to the query. In general, most of the index-based methods for similarity search generate all the possible answers to the query and then perform exact matching on the index for each possible answer. Based on the edit distance, our method generates only a few possible answers by performing the deletion and/or replacement operations on the query. Each possible answer is then used to perform exact matching on a list-based index, which allows the insertion operations to be performed. For each possible answer, its edit distance to the query is regarded as a lower bound of the edit distances between the matched results and the query. Based on the kNN results that match a possible answer, the possible answers that cannot provide better results are skipped. By using this mechanism, we design a method for efficient kNN search in polyphonic music databases. The experimental results show that our method outperforms the previous methods in efficiency. We also evaluate the effectiveness of our method by showing the search results to the musician and nonmusician user groups. The experimental results provide useful guidelines on the design of a polyphonic music database.
14112648
Reduction design for generic universal switch blocks
A k-side switch block with W terminals per side is said to be a universal switch block ((k, W)-USB) if every set of the nets satisfying the routing constraint (i.e., the number of nets on each side is at most W) is simultaneously routable through the switch block. The (4, W)-USB was originated by designing better switch modules for 2-D FPGAs, such as Xilinx XC4000-type FPGAs, whereas the generic USBs can be applied in multidimensional or some nonconventional 2-D FPGA architectures. The problem we study in this article is to design (k, W)-USBs with the minimum number of switches for any given pair of (k, W). We provide graph models for routing requirements and switch blocks and develop a series of decomposition theorems for routing requirements with the help of a new graph model. The powerful decomposition theory leads to the automatic generation of routing requirements and a detailed routing algorithm, as well as the reduction design method of building large USBs by smaller ones. As a result, we derive a class of well-structured and highly scalable optimum (k, W)-USBs for k ≤ 6, or even Ws, and near-optimum (k, W)-USBs for k ≥ 7 and odd Ws. We also give routing experiments to justify the routing improvement upon the entire chip using the USBs. The results demonstrate the usefulness of USBs.
13225508
Dynamic-Compiler-Driven Control for Microprocessor Energy and Performance
A general dynamic-compilation environment offers power and performance control opportunities for microprocessors. The authors propose a dynamic-compiler-driven runtime voltage and frequency optimizer. A prototype of their design, implemented and deployed in a real system, achieves energy savings of up to 70 percent
18681026
Breast Cancer Diagnosis Using Neural-Based Linear Fusion Strategies
Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, which are used to combine a group of component multilayer perceptrons with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perceptron Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods.
5096474
Fusing Features in Direct Volume Rendered Images
In this paper, we propose a novel framework which can fuse multiple user selected features in different direct volume rendered images into a comprehensive image according to users’ preference. The framework relies on three techniques, i.e., user voting, genetic algorithm, and image similarity. In this framework, we transform the fusing problem to an optimization problem with a novel energy function which is based on user voting and image similarity. The optimization problem can then be solved by the genetic algorithm. Experimental results on some real volume data demonstrate the effectiveness of our framework.
15058650
Query estimation by adaptive sampling
The ability to provide accurate and efficient result estimations of user queries is very important for the query optimizer in database systems. In this paper, we show that the traditional estimation techniques with data reduction points of view do not produce satisfiable estimation results if the query patterns are dynamically changing. We further show that to reduce query estimation error, instead of accurately capturing the data distribution, it is more effective to capture the user query patterns. In this paper, we propose query estimation techniques that can adapt to user query patterns for more accurate estimates of the size of selection or range queries over databases.