11120101
A comparison of DFT and DWT based similarity search in time-series databases
Similarity searc h in time-series databases has received signi cant attention lately .P opular tec hniques for e cien t retrieval of time sequences in time-series databases has been to use Discrete Fourier Transform (DFT). Recently, the Discrete Wavelet T ransform (DWT) has gained popular interest in database domain and several proposals have been made to replace DFT by DWT for similarity search over time-series databases. In this paper, we explore the feasibility of replacing DFT by DWT with a comprehensive analysis of the DFT and DWT as matching functions in time-series databases. Our results show that although the DWT based technique has several adv an tages,e.g., the D WThas complexity of O(N) whereas DFT is O(N logN), D WT does not reduce relativ e matching error and does not increase query precision in similarity searc h as suggested by previous works [1]. We conclude that, by exploring the conjugate property of DFT in real domain, the DFT-based and DWT-based techniques yield comparable results on similarity searc h in time-series databases.
18150673
The anatomy of a multimodal information filter
The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.
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.
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.
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.
43222800
A New Recombination Lower Bound and the Minimum Perfect Phylogenetic Forest Problem
Understanding recombination is a central problem in population genetics. In this paper, we address an established problem in Computational Biology: compute lower bounds on the minimum number of historical recombinations for generating a set of sequences [11,13,9,1,2,15]. In particular, we propose a new recombination lower bound: the forest bound. We show that the forest bound can be formulated as the minimumperfect phylogenetic forest problem, a natural extension to the classic binary perfect phylogeny problem, which may be of interests on its own. We then show that the forest bound is provably higher than the optimal haplotype bound [13], a very good lower bound in practice [15].We prove that, like several other lower bounds [2], computing the forest bound isNPhard. Finally, we describe an integer linear programming (ILP) formulation that computes the forest bound precisely for certain range of data. Simulation results show that the forest bound may be useful in computing lower bounds for low quality data.
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.
16403469
PSALM: A Data Model for Pervasive Visualization
In this poster, we investigate how to achieve visualization in a pervasive visualization environment, especially on new platforms such as PDAs, cell phones, and other mobile devices that have very limited resources and usually are not considered appropriate platforms for visualization. We will use a clinic application as an example and our target application. In this system, patient data, such as CT and MRI, are stored in a central server in the hospital and physicians can access these data using a cell phone for a quick preview, a pocket PC or laptop for some initial diagnosis, and a powerful workstation for any further diagnosis.