We consider the problem of processing exact results for sliding window joins over data streams with limited memory. Existing approaches deal with memory limitations by shedding loads, and therefore cannot provide exact or even highly accurate results for sliding window joins over data streams showing time varying rate of data arrivals. We provide an Exact Window Join (EWJ) algorithm incorporating disk storage as an archive. Our algorithm spills window data onto the disk on a periodic basis, refines the output result by properly retrieving the disk resident data, and maximizes output rate by employing techniques to manage the memory blocks. The problem of managing the window blocks in memory--similar in nature to the caching issue--captures both the temporal and frequency related properties of the stream arrivals. At the same, we improve I/O efficiency by amortizing a disk scan over a large number of input tuple. We provide experimental results demonstrating the performance and effectiveness of the proposed algorithm.
{"title":"Processing Exact Results for Sliding Window Joins over Time-Sequence, Streaming Data Using a Disk Archive","authors":"Abhirup Chakraborty, Ajit Singh","doi":"10.1109/ACIIDS.2009.64","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.64","url":null,"abstract":"We consider the problem of processing exact results for sliding window joins over data streams with limited memory. Existing approaches deal with memory limitations by shedding loads, and therefore cannot provide exact or even highly accurate results for sliding window joins over data streams showing time varying rate of data arrivals. We provide an Exact Window Join (EWJ) algorithm incorporating disk storage as an archive. Our algorithm spills window data onto the disk on a periodic basis, refines the output result by properly retrieving the disk resident data, and maximizes output rate by employing techniques to manage the memory blocks. The problem of managing the window blocks in memory--similar in nature to the caching issue--captures both the temporal and frequency related properties of the stream arrivals. At the same, we improve I/O efficiency by amortizing a disk scan over a large number of input tuple. We provide experimental results demonstrating the performance and effectiveness of the proposed algorithm.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116382300","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}
Shahrouz Moaven, A. Kamandi, J. Habibi, Hamed Ahmadi
Evaluating architectures and choosing the correct one is a critical issue in software engineering domain, in accordance with extremely extension of architecture-driven designs. In the first years of defining architecture styles, some special quality attributes were introduced as their basic attributes. After a moment, by utilizing them in practice, some results were obtained confirming some of attributes; some others meanwhile were not witnessed. As software architecture construction process is dependent on and addressed by both usage conditions and quality attributes, in this paper a framework has been proposed to provide an environment and a platform that can cover evaluation of architecture styles with a technique that not only exploits both qualitative and quantitative information but also considering users' needs is possible precisely and with high quality. Moreover, we define a classification and notation in order to describe heterogeneous architectures. It provides us with the ability of evaluating heterogeneous architecture styles of a software system.
{"title":"Toward a Framework for Evaluating Heterogeneous Architecture Styles","authors":"Shahrouz Moaven, A. Kamandi, J. Habibi, Hamed Ahmadi","doi":"10.1109/ACIIDS.2009.68","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.68","url":null,"abstract":"Evaluating architectures and choosing the correct one is a critical issue in software engineering domain, in accordance with extremely extension of architecture-driven designs. In the first years of defining architecture styles, some special quality attributes were introduced as their basic attributes. After a moment, by utilizing them in practice, some results were obtained confirming some of attributes; some others meanwhile were not witnessed. As software architecture construction process is dependent on and addressed by both usage conditions and quality attributes, in this paper a framework has been proposed to provide an environment and a platform that can cover evaluation of architecture styles with a technique that not only exploits both qualitative and quantitative information but also considering users' needs is possible precisely and with high quality. Moreover, we define a classification and notation in order to describe heterogeneous architectures. It provides us with the ability of evaluating heterogeneous architecture styles of a software system.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122980663","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}
Machine learning is the field that is dedicated to the design and development of algorithms and techniques that allow computers to “learn”. Two common types of learning that are often mentioned are supervised learning and unsupervised learning. One often understands that in supervised learning, the system is given the desired output, and it is required to produce the correct output for the given input, while in unsupervised learning the system is given only the input and the objective is to find the natural structure inherent in the input data. We, however, suggest that even with unsupervised learning, the information inside the input, the structure of the input, and the sequence that the input is given to the system actually make the learning “supervised” in some way. Therefore, we recommend that in order to make the machine learn, even in a “supervised” manner, we should use an “unsupervised learning” model together with an appropriate way of presenting the input. We propose in this paper a simple plasticity neural network model that has the ability of storing information as well as storing the association between a pair of inputs. We then introduce two simple unsupervised learning rules and a framework to supervise our neural network.
{"title":"Supervising an Unsupervised Neural Network","authors":"T. D. Bui, Duy Khuong Nguyen, Tien Dat Ngo","doi":"10.1109/ACIIDS.2009.92","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.92","url":null,"abstract":"Machine learning is the field that is dedicated to the design and development of algorithms and techniques that allow computers to “learn”. Two common types of learning that are often mentioned are supervised learning and unsupervised learning. One often understands that in supervised learning, the system is given the desired output, and it is required to produce the correct output for the given input, while in unsupervised learning the system is given only the input and the objective is to find the natural structure inherent in the input data. We, however, suggest that even with unsupervised learning, the information inside the input, the structure of the input, and the sequence that the input is given to the system actually make the learning “supervised” in some way. Therefore, we recommend that in order to make the machine learn, even in a “supervised” manner, we should use an “unsupervised learning” model together with an appropriate way of presenting the input. We propose in this paper a simple plasticity neural network model that has the ability of storing information as well as storing the association between a pair of inputs. We then introduce two simple unsupervised learning rules and a framework to supervise our neural network.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"10878 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124691539","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}
One of the main problems of a wireless local area networks (WLANs) management model is the difficulty for remote administrators to determine whether the wireless base station could provide proper connection services to users. SYSLOG (security issues in network event logging) records events occurring in wireless base stations and conveys the events back to administrators. This study employed back-propagation neural networks (BPNN) with principal components analysis (PCA) to analyze the SYSLOG data and the connection status between wireless base stations and users. The PCA technique was used to select essential SYSLOG data influencing connecting status; and the BPNN model was applied to categorize the connection status in terms of SYSLOG data. The simulation results indicated that the BPNN with PCA procedure is a feasible and promising way in the management of wireless local area networks.
{"title":"Management of Wireless Local Area Networks by Artificial Neural Networks with Principal Components Analysis","authors":"Ping-Feng Pai, Ying-Chieh Chang, Yu-Pin Hu","doi":"10.1109/ACIIDS.2009.56","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.56","url":null,"abstract":"One of the main problems of a wireless local area networks (WLANs) management model is the difficulty for remote administrators to determine whether the wireless base station could provide proper connection services to users. SYSLOG (security issues in network event logging) records events occurring in wireless base stations and conveys the events back to administrators. This study employed back-propagation neural networks (BPNN) with principal components analysis (PCA) to analyze the SYSLOG data and the connection status between wireless base stations and users. The PCA technique was used to select essential SYSLOG data influencing connecting status; and the BPNN model was applied to categorize the connection status in terms of SYSLOG data. The simulation results indicated that the BPNN with PCA procedure is a feasible and promising way in the management of wireless local area networks.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129681596","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}
This paper presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one alpha-numerical character, i.e. A..Z, 0..9) serve as the first stage classifier. In the second stage, a support vector machine (SVM) trained on scale-invariant feature transform (SIFT) descriptors obtained from training sub-windows were employed. A recall rate of 0.920792 and precision rate of 0.90185 was obtained.
{"title":"Two-Stage License Plate Detection Using Gentle Adaboost and SIFT-SVM","authors":"W. T. Ho, Hao Wooi Lim, Yong Haur Tay","doi":"10.1109/ACIIDS.2009.25","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.25","url":null,"abstract":"This paper presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one alpha-numerical character, i.e. A..Z, 0..9) serve as the first stage classifier. In the second stage, a support vector machine (SVM) trained on scale-invariant feature transform (SIFT) descriptors obtained from training sub-windows were employed. A recall rate of 0.920792 and precision rate of 0.90185 was obtained.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122344","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}
Ninh-Thuan Truong, Thi Mai Thuong Tran, Van-Khanh To, Viet-Ha Nguyen
Checking the consistency in component models at design phase is essential in component-based software engineering (CBSE). In our previous work, we proposed an approach for verifying automatically the matching between protocol state machines (PSMs) and the Use Case Map (UCM), using the B method. Due to the expressive power of B notations, however, we cannot describe the parallel processing in the implementation machine, particularly we are not able to express all features (such as AND-forks/joins, OR-forks/joins) of UCMs in a B implementation machine. In this work, we propose an approach to solve the expression problem of UCM features using a graph-based algorithm. The UCM path which describes the interaction between components is extracted and then decomposed into sequential events' paths if it has AND-forks/joins and/or OR-forks/joins. Each of sequential events’ paths will be checked with the order of events of PSMs by the proposed algorithm.
在基于组件的软件工程(CBSE)中，在设计阶段检查组件模型的一致性是必不可少的。在我们之前的工作中，我们提出了一种方法来自动验证协议状态机(psm)和用例图(UCM)之间的匹配，使用B方法。然而，由于B符号的表达能力，我们无法描述实现机器中的并行处理，特别是我们无法在B实现机器中表达ucm的所有特征(例如and -fork /join, or -fork /join)。在这项工作中，我们提出了一种使用基于图的算法来解决UCM特征表达问题的方法。描述组件之间交互的UCM路径被提取出来，如果它具有and -fork /连接和/或or -fork /连接，则将其分解为顺序事件的路径。该算法将用psm的事件顺序来检查每个序列事件的路径。
{"title":"Checking the Consistency between UCM and PSM Using a Graph-Based Method","authors":"Ninh-Thuan Truong, Thi Mai Thuong Tran, Van-Khanh To, Viet-Ha Nguyen","doi":"10.1109/ACIIDS.2009.66","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.66","url":null,"abstract":"Checking the consistency in component models at design phase is essential in component-based software engineering (CBSE). In our previous work, we proposed an approach for verifying automatically the matching between protocol state machines (PSMs) and the Use Case Map (UCM), using the B method. Due to the expressive power of B notations, however, we cannot describe the parallel processing in the implementation machine, particularly we are not able to express all features (such as AND-forks/joins, OR-forks/joins) of UCMs in a B implementation machine. In this work, we propose an approach to solve the expression problem of UCM features using a graph-based algorithm. The UCM path which describes the interaction between components is extracted and then decomposed into sequential events' paths if it has AND-forks/joins and/or OR-forks/joins. Each of sequential events’ paths will be checked with the order of events of PSMs by the proposed algorithm.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126509096","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 imperfect production processes always leads to imperfect products and decreases the profit of business. Improving the production processes by increasing the investment cost will decrease the defective percentage of the items. In this study, we develop an EPQ model of deteriorating items with investment on imperfect production processes. An algorithm is developed to derive a replenishment policy such that the expected unit time profit is maximized. Numerical examples are provided to illustrate the theory.
{"title":"Optimal Production Decisions for Deteriorating Items with Investment on Production Processes","authors":"P. Hsu, H. Teng, H. Wee","doi":"10.1109/ACIIDS.2009.35","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.35","url":null,"abstract":"The imperfect production processes always leads to imperfect products and decreases the profit of business. Improving the production processes by increasing the investment cost will decrease the defective percentage of the items. In this study, we develop an EPQ model of deteriorating items with investment on imperfect production processes. An algorithm is developed to derive a replenishment policy such that the expected unit time profit is maximized. Numerical examples are provided to illustrate the theory.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125720724","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}
T. Hong, Wei-Tee Lin, Chun-Hao Chen, Chen-Sen Ouyang
In this paper, we try to automatically induce the membership functions appropriate for the TS fuzzy model. A GA-based learning algorithm is thus proposed to achieve the purpose. The proposed approach considers the shapes of membership functions in fitness evaluation in addition to the accuracy. The shapes of membership functions are evaluated by the overlap and coverage factors, which are used to avoid the bad types of membership functions. The experimental results show that the proposed approach can derive the membership functions in the Takagi-Sugeno system with low errors and good shapes.
{"title":"Learning Membership Functions in Takagi-Sugeno Fuzzy Systems by Genetic Algorithms","authors":"T. Hong, Wei-Tee Lin, Chun-Hao Chen, Chen-Sen Ouyang","doi":"10.1109/ACIIDS.2009.18","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.18","url":null,"abstract":"In this paper, we try to automatically induce the membership functions appropriate for the TS fuzzy model. A GA-based learning algorithm is thus proposed to achieve the purpose. The proposed approach considers the shapes of membership functions in fitness evaluation in addition to the accuracy. The shapes of membership functions are evaluated by the overlap and coverage factors, which are used to avoid the bad types of membership functions. The experimental results show that the proposed approach can derive the membership functions in the Takagi-Sugeno system with low errors and good shapes.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"1 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120843683","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}
This paper evaluates the Neural Network AutoRegressive with eXogenous (NNARX) structure in modeling the steam distillation essential oil extraction. The model order will be selected based on Rissanen’s Minimum Description Length (MDL) information criterion. In the training of NNARX model, both unregularized and regularized models will be assessed. There are three regularization levels of the weight decay that will be implemented in this work. The number of hidden neuron and iteration will be optimized before the training session. The testing of the trained model will be based on R2, adjusted-R2, NMSE, RMSE, residual histogram and correlation tests. All results will be compared and evaluated with respect to the testing data.
{"title":"Black Box Modeling of Steam Distillation Essential Oil Extraction System Using NNARX Structure","authors":"M. Rahiman, M. Taib, Y.M. Salleh","doi":"10.1109/ACIIDS.2009.95","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.95","url":null,"abstract":"This paper evaluates the Neural Network AutoRegressive with eXogenous (NNARX) structure in modeling the steam distillation essential oil extraction. The model order will be selected based on Rissanen’s Minimum Description Length (MDL) information criterion. In the training of NNARX model, both unregularized and regularized models will be assessed. There are three regularization levels of the weight decay that will be implemented in this work. The number of hidden neuron and iteration will be optimized before the training session. The testing of the trained model will be based on R2, adjusted-R2, NMSE, RMSE, residual histogram and correlation tests. All results will be compared and evaluated with respect to the testing data.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124611575","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}
When applying the traditional data mining methods to World Wide Web documents, the typical problem is that a normal web page contains a variety of information of different kinds in addition to its main content. This additional information such as navigation, advertisement or copyright notices negatively influences the results of the data mining methods as for example the content classification. In this paper, we present a method of interesting area detection in a web page. This method is inspired by an assumed human reader approach to this task. First, basic visual blocks are detected in the page and subsequently, the purpose of these blocks is guessed based on their visual appearance. We describe a page segmentation method used for the visual block detection, we propose a way of the block classification based on the visual features and finally, we provide an experimental evaluation of the method on real-world data.
{"title":"Web Page Element Classification Based on Visual Features","authors":"Radek Burget, Ivana Rudolfova","doi":"10.1109/ACIIDS.2009.71","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.71","url":null,"abstract":"When applying the traditional data mining methods to World Wide Web documents, the typical problem is that a normal web page contains a variety of information of different kinds in addition to its main content. This additional information such as navigation, advertisement or copyright notices negatively influences the results of the data mining methods as for example the content classification. In this paper, we present a method of interesting area detection in a web page. This method is inspired by an assumed human reader approach to this task. First, basic visual blocks are detected in the page and subsequently, the purpose of these blocks is guessed based on their visual appearance. We describe a page segmentation method used for the visual block detection, we propose a way of the block classification based on the visual features and finally, we provide an experimental evaluation of the method on real-world data.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462193","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}
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