2937871
Improving XML Querying with Maximal Frequent Query Patterns
Querying on XML data is a computational-expensive process due to the complex nature of both the XML data and the query. In this paper, we propose an approach to expedite XML query processing by caching the results of a specific class of queries, namely the maximal frequent queries. We mine the maximal frequent query patterns from user-issued queries and cache the results of such queries. We propose a recursive algorithm for query processing using the cached query results. Query rewriting is employed to deal with four kinds of similar queries namely exact matching, exact containment, semantic matching and semantic containment. We perform experiments on the XMARK datasets and show that the proposed methods are both effective and efficient in improving the performance of XML queries.
14012459
MSFSS: A Storage System for Mass Small Files
We designed and implemented MSFSS, a scalable and flexible distributed file system for storage and retrieval of mass small files. MSFSS is a platform built upon the existing commodity file systems. It automatically stores files onto the best-fit commodity file systems according their access patterns. To avoid central bottleneck, it optimizes metadata size, separates metadata operations from file data transfer, and implements batch metadata operations. The system provides data migration, hot file caching, and replication, which are essential for large scale, reliable storage systems. It has successfully been deployed as storage system for our Web application which has about 50 TB of small files. Experimental results show that MSFSS provides high scalability and throughput in file operation services.
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
116588251
On-line evolutionary exponential family mixture
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.
1107258
Improving image clustering: An unsupervised feature weight learning framework
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, because we cannot determine which features are more important. In this paper, we provide a feature weight learning framework for clustering which can obtain the feature weights and cluster labels simultaneously. An alternative optimization algorithm is adopted to solve this problem. Empirical studies on the toy data and real image data demonstrate our algorithm's effectiveness in improving the clustering performance.
11514442
A test pattern ordering algorithm for diagnosis with truncated fail data
In this paper, we propose a test pattern ordering algorithm for fault diagnosis. Test pattern ordering is effective in situations where the fail log is truncated and contains a limited number of fail data. In such cases, higher diagnostic resolution can be achieved with the test set appropriately ordered. Test pattern ordering is independent of the diagnosis algorithm used. The higher resolution achieved by test pattern ordering is obtained at no additional cost once the test patterns have been appropriately ordered. Experimental results on two industrial designs are presented to demonstrate the effectiveness of the proposed method
16819932
A Client Peer Adjustment Policy for Peer-to-Peer Media Streaming
Previous work indicates that more cooperation is needed between peers for peer-to-peer (P2P) media streaming application than files sharing. That implies it is important to select cooperators in P2P networks. However, most of peer selection algorithms are performed at requesting ones and focus on how to choose servers from which clients can benefit more while ignoring whether the scalability and service capacity of systems will be influenced by these results. In this paper, we pay more attention to heterogeneous network capacity of peers and propose a policy fulfilled by server peers to select and adjust clients according to their service capacity and contribution. Peers with higher service capacity may replace existing children. And experiments proved that our policy could improve system's overall utilization and contribution, as well as the depth of multicast tree when compared with delay-based algorithm
17507770
An Improved Level Set for Liver Segmentation and Perfusion Analysis in MRIs
Determining liver segmentation accurately from MRIs is the primary and crucial step for any automated liver perfusion analysis, which provides important information about the blood supply to the liver. Although implicit contour extraction methods, such as level set methods (LSMs) and active contours, are often used to segment livers, the results are not always satisfactory due to the presence of artifacts and low-gradient response on the liver boundary. In this paper, we propose a multiple-initialization, multiple-step LSM to overcome the leakage and over-segmentation problems. The multiple-initialization curves are first evolved separately using the fast marching methods and LSMs, which are then combined with a convex hull algorithm to obtain a rough liver contour. Finally, the contour is evolved again using global level set smoothing to determine a precise liver boundary. Experimental results on 12 abdominal MRI series showed that the proposed approach obtained better liver segmentation results, so that a refined liver perfusion curve without respiration affection can be obtained by using a modified chamfer matching algorithm and the perfusion curve is evaluated by radiologists.
40279092
An Audio Feature Extraction Method Taking Class Information into Account
An inverter ballast for fluorescent lamps utilizing a resonant circuit coupled to the lamp and operated from a source of DC power through transistors. The transistors are controlled by a sensing means in the resonant loop which insures that the transistor switch at zero current. Furthermore, the coupling means for the load controls a source of heater voltage to the anodes of the fluorescent light which operate at very low current, an antisaturation circuit made up of separate windings are on the current transformer and connected to the switching transistor bases and through diodes to the power supply to prevent "shoot through" current in the transistors. An autotransformer is used as a high leakage reactance transformer; and a circuit is provided to prevent overload of the resonant circuit in case of lamp failure. Shoot through currents are prevented, and overload of the resonant circuit is prevented, and an autotransformer coupling of the resonant circuit to the load is used.
17959493
A Fast Document Classification Algorithm Based on Improved KNN
A novel KNN classification algorithm combining model and evidence theory is proposed in this paper. The new method not only overcomes the main shortage of lazy learning in traditional KNN, but also takes the distances between samples to be recognized and samples in k-neighbors into account. At the same time the method resolves the unrecognizable cases of unknown samples. Applying the classification algorithm into the document recognition, experimental results show its satisfied recognition rate and fast categorization speed