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Year 2006: Volume III Issue I



 Cover Page & Contents (PDF)



Paper Title: The SH-Tree: A Novel and Flexible Super Hybrid Index Structure for Similarity Search on Multidimensional Data.   (PDF)
DANG Tran Khanh

Area: Database Technology

  Abstract: Approaches to indexing and searching feature vectors are an indispensable factor to support similarity search effectively and efficiently. Such feature vectors extracted from real world objects are usually presented in the form of multidimensional data. As a result, many multidimensional data index techniques have been widely introduced to the research community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Although there are a variety of “mixed” index techniques, which try to inherit positive aspects from more than one index technique, the number of techniques that are derived from these two main classes is just a few. In this paper, we introduce such a “mixed” index, the SH-tree: a novel and flexible super hybrid index structure for multidimensional data. Theoretical analyses indicate that the SH-tree is a good combination of the two index technique families with respect to both the presentation and search algorithms. It overcomes shortcomings and makes use of their positive aspects to facilitate efficient similarity searches in multidimensional data spaces. Empirical experiment results with both uniformly distributed and real data sets will confirm our theoretical analyses.



Paper Title: An Integrated System for Cancer-Related Genes Mining from Biomedical Literatures. (PDF)

  Author:  Shih-Nung Chen  and  Kuo-Cheng Wen

Area: Data Mining

  Abstract: According to statistics, the rate of having cancer is relatively high for people in developing  
  and developed countries. So cancer can be called as the enemy of human health. With the destruction of ecological environment and with the change of life style and diet, the rate of having cancer during the life of a general individual is one and a quarter and grows continuously by year. Thus, it is important to study the cause of cancer. However, while biomedical researchers search and retrieve biomedical literature, there is a problem of information overloading. Therefore, to survey thousands and hundreds of biomedical literatures by manual browsing is time wasting and difficult to make sure whether required information can be found. Besides, artificial inattention may lead to incorrect analysis information. The purpose of this study is to develop an integrated system for collecting and mining cancer-related gene. This system uses PubMed search engine of NCBI to search and retrieve biomedical literature and sequence of cancer-related gene. Users can combine cancer name and LOH (loss of heterozygosity), or cancer name and CGH (comparative genomic hybridization) to search, retrieve, collect, classify, and extract the biomedical literatures. This system can extract important information to accelerate the study and save plenty of time for biomedical researchers. Besides, this system can also be used on other diseases.



   Paper Title: Avoidance of Priority Inversion in Real Time Systems Based on Resource 

Author: Tarek Helmy and Syed S. Jafri

Area: CPU Scheduling

Abstract: Priority inversion is a problem that occurs in concurrent processes when low-priority threads hold shared resources required by some high-priority threads, causing the high priority-threads to block indefinitely. This problem is enlarged when the concurrent processes are in a real time system where high- priority threads must be served on time. A novice approach for avoiding the priority inversion problem is presented for processes in real time systems. This approach is based on backing up and restoring the shared resources. A low priority thread always starts on a shadow version of the shared resource, the original resource remains unchanged. When a high-priority thread needs a resource engaged by a low-priority thread, the low priority thread is preempted, the original resource is restored and the high-priority thread is allowed to use the original resource. The approach has been implemented in Java and the experimental results are fetched which verify that the approach is very suitable for real time systems where high-priority threads must be served on time.



Paper Title: A New DNA Implementation of Finite State Machines. (PDF)

Author: A. Nowzari-Dalini, E. Elahi , H. Ahrabian,  and M. Ronaghi

Area: DNA Computing, FSM

Abstract: Two new models for implementing finite state machines  with DNA  computing are presented. The operations used in both models are simple and easy to implement. Operations include immobilization of DNA strands onto paramagnetic beads, DNA hybridization, DNA ligation and restriction enzyme cleavage. Use of paramagnetic beads greatly reduces performance time and demonstrates DNA chip compatibility of the models. In one of the models, the length of DNA strands created during the intermediate operations are independent of the length of the input string. Optical extraction in both models detects the final state.



Paper Title: Cash Forecasting: An Application of Artificial Neural Networks in Finance. (PDF)

Author: PremChand Kumar and Ekta Walia

Area: Neural Network

Abstract: Artificial Neural Networks are universal and highly flexible function approximators first used in the fields of cognitive science and engineering. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. The ability to predict cash requirement within reasonable accuracy of actual demand provides target for supply optimization well in time.  Every financial institution (large or small) faces the same daily challenge.  While it would be devastating to run out of cash, it is important to keep cash at the right levels to meet customer demand.  In such case, it becomes very necessary to have a forecasting system in order to get a clear picture of demand well in advance. This paper presents two neural network models for cash forecasting for a bank branch. One is daily model – taking the parameter values for a day as input to forecast cash requirement for the next day and the other is weekly model, which takes the withdrawal affecting input patterns of a week to predict cash requirement for the next week. The system performs better than other cash forecasting systems. This system can be scaled for all branches of a bank in an area by incorporating historical data from these branches.



Paper Title: Enhancing QoS In Web Caching Using Differentiated Services. (PDF)

Authors: P.Venketesh, S.N. Sivanandam, and S.Manigandan

Area: WWW

Abstract: Web caching and Content Distribution Networks (CDNs) occupy strategic positions in the growth of Internet by effectively delivering data to the end users with reduced access latency. Due to diverse nature of applications, Quality of Service (QoS) is considered an important aspect in web caching. To achieve QoS in Caching scenario web requests are classified into different classes with each class holding different priority levels. In this paper, we show how differentiated strategies combined with dynamic memory allocation based on relative hit rate can provide improved performance for high priority classes with little or no performance degradation for low priority classes. We have developed and evaluated a model that classifies web requests into two classes (Premium, Best-Effort) based on object size that uses LRU and LFU-DA as replacement algorithms. The proposed model efficiently converge the hit rate of classes towards its specified desired hit rate with minimum overhead.



Paper Title: Intelligent Naming System: An Alternative for Enterprise Naming Management. (PDF)

Authors: Ladda Preechaveerakul and Pattarasinee Bhattarakosol

Area: Information Technology, Domain Names

Abstract: People use “name” in general to reference things easily. In addition, one name may refer to various types of things or objects (one name – many objects). For example, a convenience store named “Family Mart” uses this single name for every branch. Similarly, names are also important in all computer communication. Name services have been developed to map logical names to physical resources. The structure of the current name services is hierarchical for scalability. However, this limits one name mapping to many objects. This paper proposes the new naming system called the Intelligent Naming System that employs the concept of sets and trees. The technique called One Name – Many Objects – One Result (ONMOOR) is proposed to ensure that the result of the mapping is the intended object. Furthermore, the proposed solution has been proved by theoretical analysis.

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