2297694
Distributed cooperative Apache web server
Given explosive data traAEc in the world-wide web (WWW), it is crucial to achieve the scalable performance of web servers. The overall performance and resource utilization can be improved by spreading document requests among a group of web servers. This leads to the design and implementation of Distributed Cooperative Apache (DC-Apache) web server. In this paper, we describe the unique features of the DC-Apache system (1) to migrate and replicate documents among cooperating servers, (2) using dynamic hyperlink generation to distribute requests for documents to balance the load, and (3) to maintain replicated copies in a consistent state. We also address the issue of storage management for more e ective document replication under limited capacity. In the experiments, the DC-Apache system demonstrated its ability to achieve high performance and scalability by e ectively distributing load among a group of cooperating Apache servers and by eliminating hot spots and performance bottleneck with replicated documents. The DC-Apache system is an e ective and practical solution to provide high performance and scalability to cope with ever increasing demands from clients all over the web. keywords: WWW, Scalable Web server, Apache, DCApache, Distributed Web server, Replication, Load balancing
5061697
XISS/R: XML Indexing and Storage System using RDBMS
We demonstrate the XISS/R system, an implementation of the XML Indexing and Storage System (XISS) on top of a relational database. The system is based on the XISS extended preorder numbering scheme, which captures the nesting structure of XML data and provides the opportunity for storage and query processing independent of the particular structure of the data. The system includes a web-based user interface, which enables stored documents to be queried via XPath. The user interface utilizes the XPath Query Engine, which automatically translates XPath queries into efficient SQL statements.
1659842
Partition Based Path Join Algorithms for XML Data
Path expression is an important component in querying XML data. The extended preorder numbering scheme enables us to quickly determine the ancestor-descendant relationship between elements in the hierarchy of XML data. Using the numbering scheme, a path expression can be evaluated by join operations to avoid potentially high cost of tree traversals. In this paper, we first formulate XML path queries as range-point join queries. Then we discuss the partition based algorithms that can utilize the range containment property to efficiently process the range-point join queries. Under the partition based framework, we propose three algorithms, namely Descendant partition join, Segment-tree partition join and Ancestor Link partition join, which can be chosen by a query optimizer for different input data characteristics. The experimental results show that the partition based algorithms can make better use of the buffer memory than sort-merge algorithms, and the proposed Ancestor Link join algorithm yields the best performance by using small in-memory data structures and by taking advantage of unevenly sized inputs.
2653165
Skyline index for time series data
We have developed a new indexing strategy that helps overcome the curse of dimensionality for time series data. Our proposed approach, called skyline index, adopts new skyline bounding regions (SBR) to approximate and represent a group of time series data according to their collective shape. Skyline bounding regions allow us to define a distance function that tightly lower bounds the distance between a query and a group of time series data. In an extensive performance study, we investigate the impact of different distance functions by various dimensionality reduction and indexing techniques on the performance of similarity search, including index pages accessed, data objects fetched, and overall query processing time. In addition, we show that, for k-nearest neighbor queries, the proposed skyline index approach can be coupled with the state of the art dimensionality reduction techniques such as adaptive piecewise constant approximation (APCA) and improve its performance by up to a factor of 3.
6512103
Indexing and Querying XML Data for Regular Path Expressions
A tone arm assembly for record players having a first motor for rotating a tone arm in the vertical direction, a second motor for rotating the tone arm in the horizontal direction with respect to a record disc, respectively, a detector for detecting low frequency vertical and horizontal vibrations of the tone arm during the reproduction of a record disc and feeding back an output signal of the detector to the motors so as to cancel the vibrations, whereby high fidelity reproduction of sounds can be performed. Further, the stylus pressure adjustment and elevation angle adjustment of the tone arm are also performed.
3084819
Distributed energy-conserving routing protocols
This paper discusses several distributed power-aware routing protocols in wireless ad-hoc networks (especially sensor networks). We seek to optimize the lifetime of the network. We have developed three distributed power-aware algorithms and analyzed their efficiency in terms of the number of message broadcasts and the overall network lifetime modelled as the time to the first message that can not be sent. These are: (1) a distributed min power algorithm (modelled on a distributed version of Dijkstra's algorithm), (2) a distributed max-min algorithm, and (3) the distributed version of the centralized online max-min zP/sub min/ algorithm presented by Qun Li et al. (2001). The first two algorithms are used to define the third, although they are very interesting and useful on their own for applications where the optimization criterion is the minimum power, respectively the maximum residual power. The distributed max-min zP/sub min/ algorithm optimizes the overall lifetime of the network by avoiding nodes of low power, while not using too much total power.
37613840
Improving medical/biological data classification performance by wavelet preprocessing
Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough.
1906525
A Robust and Secure RFID-Based Pedigree System (Short Paper)
There has been considerable interest recently on developing a system to track items like pharmaceutical drugs or food products. Such a system can help prevent counterfeits, aid product recall, and improve general logistics. In this paper, we present such system based on radio frequency identity (RFID) technology. Our solution provides the means of storing the entire movement of the item from original manufacturer to final consumer on the RFID tag itself, and also makes it more difficult to introduce large numbers of counterfeits. The solution also allows the end user to easily verify the authenticity of the item.
3013042
Feature extraction via generalized uncorrelated linear discriminant analysis
Feature extraction is important in many applications, such as text and image retrieval, because of high dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature extraction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, we will first propose the ULDA/QR algorithm to simplify the previous implementation of ULDA. Then we propose the ULDA/GSVD algorithm, based on a novel optimization criterion, to address the singularity problem. It is applicable for undersampled problem, where the data dimension is much larger than the data size, such as text and image retrieval. The novel criterion used in ULDA/GSVD is the perturbed version of the one from ULDA/QR, while surprisingly, the solution to ULDA/GSVD is shown to be independent of the amount of perturbation applied. We did extensive experiments on text and face image data to show the effectiveness of ULDA/GSVD and compare with other popular feature extraction algorithms.
6685320
GPCA: an efficient dimension reduction scheme for image compression and retrieval
Recent years have witnessed a dramatic increase in the quantity of image data collected, due to advances in fields such as medical imaging, reconnaissance, surveillance, astronomy, multimedia etc. With this increase has come the need to be able to store, transmit, and query large volumes of image data efficiently. A common operation on image databases is the retrieval of all images that are similar to a query image. For this, the images in the database are often represented as vectors in a high-dimensional space and a query is answered by retrieving all image vectors that are proximal to the query image in this space, under a suitable similarity metric. To overcome problems associated with high dimensionality, such as high storage and retrieval times, a dimension reduction step is usually applied to the vectors to concentrate relevant information in a small number of dimensions. Principal Component Analysis (PCA) is a well-known dimension reduction scheme. However, since it works with vectorized representations of images, PCA does not take into account the spatial locality of pixels in images. In this paper, a new dimension reduction scheme, called Generalized Principal Component Analysis (GPCA), is presented. This scheme works directly with images in their native state, as two-dimensional matrices, by projecting the images to a vector space that is the tensor product of two lower-dimensional vector spaces. Experiments on databases of face images show that, for the same amount of storage, GPCA is superior to PCA in terms of quality of the compressed images, query precision, and computational cost.