Data warehousing is one of the most important fundamental components of business intelligence. One approach to improve a data warehouse is indexing. Y-tree is a type of index which is designed to facilitate one of the most critical data warehousing functionalities, i.e. data insertion. Y trees support bulk insertion efficiently and also can provide the query performance as efficient as traditional value-list indexes. In this paper, we propose a theoretical study on the performance of the Y-tree indexes when implemented on flash drives. Our work will cover two main functionalities of Y-trees on flash drives, i.e. insertion and query performance. Given that flash drives are emerging as the main storage of the server-class computers, and their price per size are getting lower. The analysis of the performance in this paper can help to estimate the performance in practices. Also, the result can help practitioners to optimize Y-tree according to the given environment.
{"title":"Performance Analysis of Y-Tree on Flash Drives","authors":"Narong Boonyawat, J. Natwichai","doi":"10.1109/ICCNT.2010.126","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.126","url":null,"abstract":"Data warehousing is one of the most important fundamental components of business intelligence. One approach to improve a data warehouse is indexing. Y-tree is a type of index which is designed to facilitate one of the most critical data warehousing functionalities, i.e. data insertion. Y trees support bulk insertion efficiently and also can provide the query performance as efficient as traditional value-list indexes. In this paper, we propose a theoretical study on the performance of the Y-tree indexes when implemented on flash drives. Our work will cover two main functionalities of Y-trees on flash drives, i.e. insertion and query performance. Given that flash drives are emerging as the main storage of the server-class computers, and their price per size are getting lower. The analysis of the performance in this paper can help to estimate the performance in practices. Also, the result can help practitioners to optimize Y-tree according to the given environment.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128920904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The assets reorganization of ST companies’ has an important meaning on companies’ performance. This article calculates the financial indicators and finds out the problems in assets reorganization, then proposes corresponding settlement to solve these problems.
{"title":"Performance Study of Reorganization of the ST Company's Assets","authors":"M. Sun, Xiao Jing Li","doi":"10.1109/ICCNT.2010.115","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.115","url":null,"abstract":"The assets reorganization of ST companies’ has an important meaning on companies’ performance. This article calculates the financial indicators and finds out the problems in assets reorganization, then proposes corresponding settlement to solve these problems.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115388177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generally, there appears to be a direct relationship between the data rate and the throughput in single rate network. However, under multi rate networks, the long term throughput of each station becomes largely independent of its own data rate, rather, it gets bounded by the lowest data rate peer. The presence of a low data rate link in particular brings down the aggregate throughput, thereby restricting the benefits of higher data rates used by the peers. In this paper we propose a simple and efficient mechanism by which the stations employing higher data rates receive greater transmission opportunities, thereby increasing the aggregate throughput. The calculations are based on the baseline property, which guarantees airtime fairness in the network if all competing stations meet their target throughput. Every station attempts to meet its respective target throughput by dynamically adjusting its minimum contention window CWmin and, hence, controlling its transmission opportunities.
{"title":"Providing Time Based Fairness in Multi Rate Ad Hoc Network","authors":"S. Varma, V. Tokekar","doi":"10.1109/ICCNT.2010.17","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.17","url":null,"abstract":"Generally, there appears to be a direct relationship between the data rate and the throughput in single rate network. However, under multi rate networks, the long term throughput of each station becomes largely independent of its own data rate, rather, it gets bounded by the lowest data rate peer. The presence of a low data rate link in particular brings down the aggregate throughput, thereby restricting the benefits of higher data rates used by the peers. In this paper we propose a simple and efficient mechanism by which the stations employing higher data rates receive greater transmission opportunities, thereby increasing the aggregate throughput. The calculations are based on the baseline property, which guarantees airtime fairness in the network if all competing stations meet their target throughput. Every station attempts to meet its respective target throughput by dynamically adjusting its minimum contention window CWmin and, hence, controlling its transmission opportunities.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117086488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Patcharaprakiti, K. Kirtikara, D. Chenvidhya, V. Monyakul, B. Muenpinij
This paper proposes a new method to modeling a power inverter of grid-connected photovoltaic system by using a system identification approach. In this method, the system is considered as a black box of which it is not necessary to know structures and parameters inside. Modeling of one type of grid connected single phase inverter is carried out. Four linear models have been compared, i.e. an Autoregressive with Exogenous (ARX) model, an Autoregressive Moving Average with Exogenous (ARMAX) model, a Box-Jenkins (BJ) model an Output Error (OE) model. Four nonlinear models are studied, i.e, a Nonlinear Autoregressive with Exogenous (ARX) model, a Hammerstein model, a Wiener Model and a Hammerstein-Wiener Model. The best linear model is an Output Error model whereas the best nonlinear model is a Hammerstein-Wiener model with wavelet network estimators. Comparing modeling of the inverter by an Output-Error (OE) model and a Hammerstein-Wiener model, a Hammerstein-Wiener model is better because of its lower order and higher percentage of best fit.
{"title":"Modeling of Single Phase Inverter of Photovoltaic System Using System Identification","authors":"N. Patcharaprakiti, K. Kirtikara, D. Chenvidhya, V. Monyakul, B. Muenpinij","doi":"10.1109/ICCNT.2010.120","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.120","url":null,"abstract":"This paper proposes a new method to modeling a power inverter of grid-connected photovoltaic system by using a system identification approach. In this method, the system is considered as a black box of which it is not necessary to know structures and parameters inside. Modeling of one type of grid connected single phase inverter is carried out. Four linear models have been compared, i.e. an Autoregressive with Exogenous (ARX) model, an Autoregressive Moving Average with Exogenous (ARMAX) model, a Box-Jenkins (BJ) model an Output Error (OE) model. Four nonlinear models are studied, i.e, a Nonlinear Autoregressive with Exogenous (ARX) model, a Hammerstein model, a Wiener Model and a Hammerstein-Wiener Model. The best linear model is an Output Error model whereas the best nonlinear model is a Hammerstein-Wiener model with wavelet network estimators. Comparing modeling of the inverter by an Output-Error (OE) model and a Hammerstein-Wiener model, a Hammerstein-Wiener model is better because of its lower order and higher percentage of best fit.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125778919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The usage of the Internet has become ubiquitous, even for desktop applications to assume that the computer system it is running on is connected to the Internet. Desktop applications rely on the Internet connectivity for software license authentication and also for maintenance through downloading of software patches. However, the latter can pose an annoyance to the user when he or she is relying on the Internet for real-time gaming or during heavy downloading of multimedia files. In this paper, we study the effectiveness of using the ARMA model to provide short range forecasting of Internet network TCP traffic for a single broadband line. The outcome of the research is positive and indicates that a step size of 30 seconds and irrespective of the window size gives the most accurate forecast. Through amplification of the results, this method shows strong indication that it can be implemented by software application developers to determine the most appropriate non-disruptive period to download their software patches. For small sized software patches, the software application can activate the download and a period of 120 seconds would be sufficient.
{"title":"Towards Forecasting Low Network Traffic for Software Patch Downloads: An ARMA Model Forecast Using CRONOS","authors":"I. Tan, Poo Kuan Hoong, C. Y. Keong","doi":"10.1109/ICCNT.2010.35","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.35","url":null,"abstract":"The usage of the Internet has become ubiquitous, even for desktop applications to assume that the computer system it is running on is connected to the Internet. Desktop applications rely on the Internet connectivity for software license authentication and also for maintenance through downloading of software patches. However, the latter can pose an annoyance to the user when he or she is relying on the Internet for real-time gaming or during heavy downloading of multimedia files. In this paper, we study the effectiveness of using the ARMA model to provide short range forecasting of Internet network TCP traffic for a single broadband line. The outcome of the research is positive and indicates that a step size of 30 seconds and irrespective of the window size gives the most accurate forecast. Through amplification of the results, this method shows strong indication that it can be implemented by software application developers to determine the most appropriate non-disruptive period to download their software patches. For small sized software patches, the software application can activate the download and a period of 120 seconds would be sufficient.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127509224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing search engines ignore the user’s search context and return results to the user solely based on their relevance value to the query. Hence, the need for personalized information retrieval system which takes into account the actual user’s profile during the retrieval process is “mandatory.” In this paper, we propose a framework for personalized information retrieval model using user profiles. It incorporates the user profile module into the retrieval process to filter results returned by traditional search engine so that they meet the specific needs of the user.
{"title":"A Framework for Personalized Information Retrieval Model","authors":"F. Gemechu, Yu Zhang, Liu Ting","doi":"10.1109/ICCNT.2010.125","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.125","url":null,"abstract":"Existing search engines ignore the user’s search context and return results to the user solely based on their relevance value to the query. Hence, the need for personalized information retrieval system which takes into account the actual user’s profile during the retrieval process is “mandatory.” In this paper, we propose a framework for personalized information retrieval model using user profiles. It incorporates the user profile module into the retrieval process to filter results returned by traditional search engine so that they meet the specific needs of the user.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131044774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, wireless sensor networks become a new way to obtain information from an interesting area. There are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes and network lifetime in wireless sensor networks. In this paper, we propose a new method to improve ACPM and solve the problem of isolated nodes and to prolong their lifetime. After cluster construction, the isolated nodes gradually enhance their transmission range till finding an adjacent cluster to link. Furthermore, after collecting all data within a cluster, a cluster-head node will assign the most powerful member node in the cluster to forward the data to the base station. Simulation results show that our approach effectively conserves energy for isolated nodes.
{"title":"An Energy-Efficient Clustering Protocol for Wireless Sensor Networks","authors":"Yun-Sheng Yen, R. Chang, Sin-Lung Ke","doi":"10.1109/ICCNT.2010.85","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.85","url":null,"abstract":"In recent years, wireless sensor networks become a new way to obtain information from an interesting area. There are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes and network lifetime in wireless sensor networks. In this paper, we propose a new method to improve ACPM and solve the problem of isolated nodes and to prolong their lifetime. After cluster construction, the isolated nodes gradually enhance their transmission range till finding an adjacent cluster to link. Furthermore, after collecting all data within a cluster, a cluster-head node will assign the most powerful member node in the cluster to forward the data to the base station. Simulation results show that our approach effectively conserves energy for isolated nodes.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123299117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decision making is an important task for enterprise managers, and is typically based on various data sources derived from information systems, such as enterprise resource planning, supply chain management and customer relationship management. Numerous business intelligence tools (BI) thus have been developed to support decision making. Some existing BI tools have several limitations, for example lacking data analysis and visualization capabilities. To increase the data analysis capability of BI tools, this study focuses on efficient data mining tools and presents an intelligent BI system framework based on many computational intelligence paradigms, including a predictor tool based on neuro-computing (cerebellar model articulation controller neural network, CMAC NN), a classifier tool based on neuro-computing (CMAC NN) and optimizer tools based on evolutionary computing and artificial life (such as real-coded genetic algorithm and artificial immune system). The predictor tool can be used to make predictions or conduct time series forecasting, the classifier tool can be applied to solve classification tasks, and the optimizer tools can be employed to optimize the parameter settings of the predictor and classifier tools. The proposed BI system can potentially be considered as an efficient data analysis tool for supporting business decisions.
{"title":"Computational Intelligence-Based Intelligent Business Intelligence System: Concept and Framework","authors":"Jui-Yu Wu","doi":"10.1109/ICCNT.2010.23","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.23","url":null,"abstract":"Decision making is an important task for enterprise managers, and is typically based on various data sources derived from information systems, such as enterprise resource planning, supply chain management and customer relationship management. Numerous business intelligence tools (BI) thus have been developed to support decision making. Some existing BI tools have several limitations, for example lacking data analysis and visualization capabilities. To increase the data analysis capability of BI tools, this study focuses on efficient data mining tools and presents an intelligent BI system framework based on many computational intelligence paradigms, including a predictor tool based on neuro-computing (cerebellar model articulation controller neural network, CMAC NN), a classifier tool based on neuro-computing (CMAC NN) and optimizer tools based on evolutionary computing and artificial life (such as real-coded genetic algorithm and artificial immune system). The predictor tool can be used to make predictions or conduct time series forecasting, the classifier tool can be applied to solve classification tasks, and the optimizer tools can be employed to optimize the parameter settings of the predictor and classifier tools. The proposed BI system can potentially be considered as an efficient data analysis tool for supporting business decisions.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115257984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtualization is a term that refers to the abstraction of computer resources. The purpose of virtual computing environment is to improve resource utilization by providing a unified integrated operating platform for users and applications based on aggregation of heterogeneous and autonomous resources. More recently, virtualization at all levels (system, storage, and network) became important again as a way to improve system security, reliability and availability, reduce costs, and provide greater flexibility. This paper explains the basics of system virtualization and addresses pros and cons of virtualization along with taxonomy and challenges.
{"title":"Virtualization: A Survey on Concepts, Taxonomy and Associated Security Issues","authors":"Jyotiprakash Sahoo, Subasish Mohapatra, Radha Lath","doi":"10.1109/ICCNT.2010.49","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.49","url":null,"abstract":"Virtualization is a term that refers to the abstraction of computer resources. The purpose of virtual computing environment is to improve resource utilization by providing a unified integrated operating platform for users and applications based on aggregation of heterogeneous and autonomous resources. More recently, virtualization at all levels (system, storage, and network) became important again as a way to improve system security, reliability and availability, reduce costs, and provide greater flexibility. This paper explains the basics of system virtualization and addresses pros and cons of virtualization along with taxonomy and challenges.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122652202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Pornpanomchai, Juti Wongkorsub, Terapong Pornaudomdaj, Pimluk Vessawasdi
There are many kinds of Buddha amulets, which are difficult for people who want to recognize all of them. The objective of this research is to build the computer system that can recognize some Thai Buddhist amulets. Our system is called -Buddhist Amulet Recognition System or BARS¿. It consists of 4 main components; 1) image acquisition, 2) image preprocessing, 3) image recognition, and 4) display result. In image acquisition component, the Buddhist amulet picture is taken by a digital camera with a white paper for the picture background. In image preprocessing component, the Buddhist amulet image is enhanced for improving a recognition precision rate in the next stage. In recognition component, a template matching technique is applied to recognize the Buddhist amulet image. In display result component, a graphic user interface (GUI) is created for displaying the recognition results. We have collected 52 kinds of Buddhist amulets with the total of 318 images for the training data set. We determine correlation value equal to 500 and this program precision equal to 80 percent and take 0.76 milliseconds per image.
{"title":"Buddhist Amulet Recognition System (BARS)","authors":"C. Pornpanomchai, Juti Wongkorsub, Terapong Pornaudomdaj, Pimluk Vessawasdi","doi":"10.1109/ICCNT.2010.128","DOIUrl":"https://doi.org/10.1109/ICCNT.2010.128","url":null,"abstract":"There are many kinds of Buddha amulets, which are difficult for people who want to recognize all of them. The objective of this research is to build the computer system that can recognize some Thai Buddhist amulets. Our system is called -Buddhist Amulet Recognition System or BARS¿. It consists of 4 main components; 1) image acquisition, 2) image preprocessing, 3) image recognition, and 4) display result. In image acquisition component, the Buddhist amulet picture is taken by a digital camera with a white paper for the picture background. In image preprocessing component, the Buddhist amulet image is enhanced for improving a recognition precision rate in the next stage. In recognition component, a template matching technique is applied to recognize the Buddhist amulet image. In display result component, a graphic user interface (GUI) is created for displaying the recognition results. We have collected 52 kinds of Buddhist amulets with the total of 318 images for the training data set. We determine correlation value equal to 500 and this program precision equal to 80 percent and take 0.76 milliseconds per image.","PeriodicalId":135847,"journal":{"name":"2010 Second International Conference on Computer and Network Technology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133631470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：