PROST: A programmable structured peer-to-peer overlay network

We present the idea of a programmable structured P2P architecture. Our proposed system allows the key-based routing infrastructure, which is common to all structured P2P overlays, to be shared by multiple applications. Furthermore, our architecture allows the dynamic and on-demand deployment of new applications and services on top of the shared routing layer.

Parsing Arabic relative clauses: A paninian dependency grammar approach

We present an approach to parsing rehive clauses in Arabic in the tradition of the Paninian Grammar Frumework/2] which leads to deriving U common logicul form for equivalent sentences. Particular attention is paid to the analysis of resumptive pronouns in the retrieval of syntuctico-semantic relationships. The analysis arises from the development of a lexicalised dependency grammar for Arabic that has application for machine translation.

A new indexing method for high dimensional dataset

Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.

Multiresolution query optimization in an online environment

Multiresolution (or multi-scale) techniques make it possible for Web-based GIS applications to access large dataset. The performance of such systems relies on data transmission over network and multiresolution query processing. In the literature the latter has received little research attention so far, and the existing methods are not capable of processing large dataset. In this paper, we aim to improve multiresolution query processing in an online environment. A cost model for such query is proposed first, followed by three strategies for its optimization. Significant theoretical improvement can be observed when comparing against available methods. Application of these strategies is also discussed, and similar performance enhancement can be expected if implemented in online GIS applications.

Finding temporal features of event-oriented patterns

A major task of traditional temporal event sequence mining is to predict the occurrences of a special type of event (called target event) in a long temporal sequence. Our previous work has defined a new type of pattern, called event-oriented pattern, which can potentially predict the target event within a certain period of time. However, in the event-oriented pattern discovery, because the size of interval for prediction is pre-defined, the mining results could be inaccurate and carry misleading information. In this paper, we introduce a new concept, called temporal feature, to rectify this shortcoming. Generally, for any event-oriented pattern discovered under the pre-given size of interval, the temporal feature is the minimal size of interval that makes the pattern interesting. Thus, by further investigating the temporal features of discovered event-oriented patterns, we can refine the knowledge for the target event prediction.

Partial completion of activity in business process specification

We examine the current workflow modelling capability from a new angle and demonstrate a weakness of current workflow specification languages in relation to execution of activities. This shortcoming is mainly due to serious limitations of the corresponding computational/execution model behind the business process modelling language constructs. The main purpose of this paper is the introduction of new specification/modelling constructs allowing for more precise representation of complex activity states during its execution. This new concept enables visibility of a new activity state–partial completion of activity, which in turn allows for a more flexible and precise enforcement/monitoring of automated business processes.

A border-based approach for hiding sensitive frequent itemsets

Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach. © 2005 IEEE

Towards effective indexing for very large video sequence database

With rapid advances in video processing technologies and ever fast increments in network bandwidth, the popularity of video content publishing and sharing has made similarity search an indispensable operation to retrieve videos of user interests. The video similarity is usually measured by the percentage of similar frames shared by two video sequences, and each frame is typically represented as a high-dimensional feature vector. Unfortunately, high complexity of video content has posed the following major challenges for fast retrieval: (a) effective and compact video representations, (b) efficient similarity measurements, and (c) efficient indexing on the compact representations. In this paper, we propose a number of methods to achieve fast similarity search for very large video database. First, each video sequence is summarized into a small number of clusters, each of which contains similar frames and is represented by a novel compact model called Video Triplet (ViTri). ViTri models a cluster as a tightly bounded hypersphere described by its position, radius, and density. The ViTri similarity is measured by the volume of intersection between two hyperspheres multiplying the minimal density, i.e., the estimated number of similar frames shared by two clusters. The total number of similar frames is then estimated to derive the overall similarity between two video sequences. Hence the time complexity of video similarity measure can be reduced greatly. To further reduce the number of similarity computations on ViTris, we introduce a new one dimensional transformation technique which rotates and shifts the original axis system using PCA in such a way that the original inter-distance between two high-dimensional vectors can be maximally retained after mapping. An efficient B+-tree is then built on the transformed one dimensional values of ViTris' positions. Such a transformation enables B+-tree to achieve its optimal performance by quickly filtering a large portion of non-similar ViTris. Our extensive experiments on real large video datasets prove the effectiveness of our proposals that outperform existing methods significantly.