Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093324
M. Krátký, Radim Bača, D. Bednář, J. Walder, J. Dvorský, P. Chovanec
N-grams are applied in some applications searching in text documents, especially in cases when one must work with phrases, e.g. in plagiarism detection. N-gram is a sequence of n terms (or generally tokens) from a document. We get a set of n-grams by moving a floating window from the begin to the end of the document. During the extraction we must remove duplicate n-grams and we must store additional values to each n-gram type, e.g. n-gram type frequency for each document and so on, it depends on a query model used. Previous works utilize a sorting algorithm to compute the n-gram frequency. These approaches must handle a high number of the same n-grams resulting in high time and space overhead. Moreover, these techniques are often main-memory only, it means they must be executed for small or middle size collections. In this paper, we show an index-based method to the n-gram extraction for large collections. This method utilizes common data structures like B+-tree and Hash table. We show the scalability of our method by presenting experiments with the gigabytes collection.
{"title":"Index-based n-gram extraction from large document collections","authors":"M. Krátký, Radim Bača, D. Bednář, J. Walder, J. Dvorský, P. Chovanec","doi":"10.1109/ICDIM.2011.6093324","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093324","url":null,"abstract":"N-grams are applied in some applications searching in text documents, especially in cases when one must work with phrases, e.g. in plagiarism detection. N-gram is a sequence of n terms (or generally tokens) from a document. We get a set of n-grams by moving a floating window from the begin to the end of the document. During the extraction we must remove duplicate n-grams and we must store additional values to each n-gram type, e.g. n-gram type frequency for each document and so on, it depends on a query model used. Previous works utilize a sorting algorithm to compute the n-gram frequency. These approaches must handle a high number of the same n-grams resulting in high time and space overhead. Moreover, these techniques are often main-memory only, it means they must be executed for small or middle size collections. In this paper, we show an index-based method to the n-gram extraction for large collections. This method utilizes common data structures like B+-tree and Hash table. We show the scalability of our method by presenting experiments with the gigabytes collection.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132827723","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093345
Khalid Iqbal, S. Asghar, S. Fong
Association Rule Mining (ARM) was introduced for the market basket analysis where items that are frequently appeared together per transaction are identified as rules. In such mining process, sensitivity issue of rules has never been addressed for more than a decade. Thus, research on guarding sensitivity in ARM should be attended to in priority by researchers, so that the risk of sensitive information disclosure can be avoided especially when the data sources are being shared. In this paper, we presented a Mode-based PPDM model via Bayesian Network (BN) which can reliably hide away sensitive rules in ARM. Such reliability was never studied nor reported in the literature of XML domain of PPDM. One useful advantage of PPDM model is its ability to unfasten a variety of directions that could be effectively used to overcome disclosure risk in XML Association Rules (XARs). Moreover, PPDM model is known to benefit businesses even in absolute competitive environment.
关联规则挖掘(ARM)是为市场购物篮分析而引入的，在这种分析中，每笔交易中经常出现的项目被标识为规则。在这种采矿过程中，规则的敏感性问题十多年来从未得到解决。因此，研究人员应优先关注ARM中敏感保护的研究，以避免敏感信息泄露的风险，特别是在数据源共享的情况下。本文提出了一种基于贝叶斯网络(BN)的基于模型的PPDM模型，该模型可以可靠地隐藏ARM中的敏感规则。这种可靠性在PPDM的XML领域的文献中从未被研究或报道过。PPDM模型的一个有用的优点是它能够解开各种方向，这些方向可以有效地用于克服XML关联规则(XML Association Rules, xar)中的披露风险。此外，众所周知，即使在绝对竞争的环境中，PPDM模式也有利于企业。
{"title":"A PPDM model using Bayesian Network for hiding sensitive XML Association Rules","authors":"Khalid Iqbal, S. Asghar, S. Fong","doi":"10.1109/ICDIM.2011.6093345","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093345","url":null,"abstract":"Association Rule Mining (ARM) was introduced for the market basket analysis where items that are frequently appeared together per transaction are identified as rules. In such mining process, sensitivity issue of rules has never been addressed for more than a decade. Thus, research on guarding sensitivity in ARM should be attended to in priority by researchers, so that the risk of sensitive information disclosure can be avoided especially when the data sources are being shared. In this paper, we presented a Mode-based PPDM model via Bayesian Network (BN) which can reliably hide away sensitive rules in ARM. Such reliability was never studied nor reported in the literature of XML domain of PPDM. One useful advantage of PPDM model is its ability to unfasten a variety of directions that could be effectively used to overcome disclosure risk in XML Association Rules (XARs). Moreover, PPDM model is known to benefit businesses even in absolute competitive environment.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130749000","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093316
Han Wang, B. Lang
Applying topic model to text mining has achieved a great success. However, state-of-art topic modeling methods still have potential to improve in academic retrieval field. In this paper, we propose an online unified topic model, which is ngram-enhanced. Our model discovers topics with unigrams as well as topical bigrams and is updated by an online inference algorithm with the new incoming data streams. On this basis, we combine our model into the query likelihood model and develop an integrated academic searching system. Experiment results on ACM collection show that our proposed methods outperform the existing approaches on document modeling and searching accuracy. Especially, we prove the efficiency of our system on academic retrieval problem.
{"title":"Online ngram-enhanced topic model for academic retrieval","authors":"Han Wang, B. Lang","doi":"10.1109/ICDIM.2011.6093316","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093316","url":null,"abstract":"Applying topic model to text mining has achieved a great success. However, state-of-art topic modeling methods still have potential to improve in academic retrieval field. In this paper, we propose an online unified topic model, which is ngram-enhanced. Our model discovers topics with unigrams as well as topical bigrams and is updated by an online inference algorithm with the new incoming data streams. On this basis, we combine our model into the query likelihood model and develop an integrated academic searching system. Experiment results on ACM collection show that our proposed methods outperform the existing approaches on document modeling and searching accuracy. Especially, we prove the efficiency of our system on academic retrieval problem.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114985882","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093349
Wejdan Alkaldi, F. Sadri
This paper addresses the efficient processing of queries involving aggregation and group-by in the semantic-model approach to information integration. Query processing algorithms materialization, subqueries, and wrapper have been extended for such aggregate queries. Algorithms have been presented for two cases: In the first case information at different sources are disjoint, while in the second case information sources may contain overlapping information.
{"title":"Query optimization in information integration for queries involving aggregation and group by","authors":"Wejdan Alkaldi, F. Sadri","doi":"10.1109/ICDIM.2011.6093349","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093349","url":null,"abstract":"This paper addresses the efficient processing of queries involving aggregation and group-by in the semantic-model approach to information integration. Query processing algorithms materialization, subqueries, and wrapper have been extended for such aggregate queries. Algorithms have been presented for two cases: In the first case information at different sources are disjoint, while in the second case information sources may contain overlapping information.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123786012","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093359
Tieta Antaresti, M. I. Fanany, A. M. Arymurthy
The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, which increase the size of datasets. In this research, multinomial classification was applied to classify some recorded features obtained from a single ECG (electrocardiograph) sensor. Therefore, a Dirichlet process, a dirichlet distribution of cumulative distribution function of each data partition, was needed to model the distribution of the new generated data by also considering the statistical properties of the previous data. Data balancing process had given the result of 77.21% classification accuracy (CA), and 90.9% area under ROC curve (AUC).
{"title":"Maintaining imbalance highly dependent medical data using dirichlet process data generation","authors":"Tieta Antaresti, M. I. Fanany, A. M. Arymurthy","doi":"10.1109/ICDIM.2011.6093359","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093359","url":null,"abstract":"The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, which increase the size of datasets. In this research, multinomial classification was applied to classify some recorded features obtained from a single ECG (electrocardiograph) sensor. Therefore, a Dirichlet process, a dirichlet distribution of cumulative distribution function of each data partition, was needed to model the distribution of the new generated data by also considering the statistical properties of the previous data. Data balancing process had given the result of 77.21% classification accuracy (CA), and 90.9% area under ROC curve (AUC).","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121397495","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093353
Anwar Alhenshiri, Hoda Badesh
Research has identified high level tasks in activities users perform on the web. Amongst those tasks is information gathering. This type of task is complex, exploratory, and it usually requires more user effort than other tasks. During the task of information gathering, users may follow one or more types of search behaviour. This paper investigates the effect of the user search behaviour on the task of information gathering on the web. Activities users perform on the web during information gathering and their correlation with the type of behaviour followed by the user were examined. The research draws recommendations for further studies that may concern developing tools for supporting web information gathering tasks according to the user search behaviour
{"title":"The effect of user search behaviour on web information gathering tasks","authors":"Anwar Alhenshiri, Hoda Badesh","doi":"10.1109/ICDIM.2011.6093353","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093353","url":null,"abstract":"Research has identified high level tasks in activities users perform on the web. Amongst those tasks is information gathering. This type of task is complex, exploratory, and it usually requires more user effort than other tasks. During the task of information gathering, users may follow one or more types of search behaviour. This paper investigates the effect of the user search behaviour on the task of information gathering on the web. Activities users perform on the web during information gathering and their correlation with the type of behaviour followed by the user were examined. The research draws recommendations for further studies that may concern developing tools for supporting web information gathering tasks according to the user search behaviour","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"82 299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125965631","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093343
T. Anwar, S. Asghar, S. Fong
Data Mining is concerned with extraction of interesting patterns or knowledge from huge amounts of Data. Generally data mining tasks are either predictive or descriptive. Classification falls under predictive induction while clustering and association rule mining fall under descriptive induction. Subgroup discovery is a task at the intersection of supervised learning and descriptive induction. In subgroup discovery we want to uncover individual patterns in data with a given property of interest. We want to find subgroups that cover a large population and are statistically different. The main application areas of subgroup discovery are exploration and descriptive induction, where the user wants to find the overview of dependencies between a target and many explaining variables. Many techniques have been proposed for discovering subgroups and some of these techniques are based on classification. But none of the techniques uses Bayesian networks for the generation of subgroups. Our contributions include a technique for the discovery of subgroups where the subgroups are generated using Bayesian networks.
{"title":"Bayesian based subgroup discovery","authors":"T. Anwar, S. Asghar, S. Fong","doi":"10.1109/ICDIM.2011.6093343","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093343","url":null,"abstract":"Data Mining is concerned with extraction of interesting patterns or knowledge from huge amounts of Data. Generally data mining tasks are either predictive or descriptive. Classification falls under predictive induction while clustering and association rule mining fall under descriptive induction. Subgroup discovery is a task at the intersection of supervised learning and descriptive induction. In subgroup discovery we want to uncover individual patterns in data with a given property of interest. We want to find subgroups that cover a large population and are statistically different. The main application areas of subgroup discovery are exploration and descriptive induction, where the user wants to find the overview of dependencies between a target and many explaining variables. Many techniques have been proposed for discovering subgroups and some of these techniques are based on classification. But none of the techniques uses Bayesian networks for the generation of subgroups. Our contributions include a technique for the discovery of subgroups where the subgroups are generated using Bayesian networks.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127150904","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093321
Dongeun Lee, Sunghoon Choi
This paper proposes automatic target detection and tracking system using Active Shape Model (ASM). Existing model based approaches for tracking are either manually initiated or need some form of user interaction to locate the object in images. Also the low light environmental conditions for surveillance systems make the tracking further harder. Hence the proposed system makes use of multiple sensors in the form of IR and visible cameras to enable tracking in degraded and low light environments. The proposed algorithm consists of the following stages: (i) input image evaluation for obtaining the conditions under which the camera is placed, (ii) an integrated motion detector and target tracker, (iii) active shape tracker(AST) for performing tracking, (iv) update of tracking results for real time tracking of targets. In the first stage the input image is evaluated for the lighting conditions. If the lighting conditions are poor then IR sensor is integrated with the CCD sensor for tracking applications. In the second stage the motion detector and region tracker are used to provide feedback to AST for automatic initialization of tracking. Tracking is carried out in the third stage using ASM. The final stage extracts the parameters and tracking information and applies it to the next frame if the tracking is carried out in real time. The major contribution this work lies in the integration for a completed system, which covers from image processing to tracking algorithms. The approach of combining multiple algorithms succeeds in overcoming fundamental limitations of tracking and at the same time realizes real time implementation. Experimental results show that the proposed algorithm can track people under various environment in real-time. The proposed system has potential uses in the area of surveillance, shape analysis, and model-based coding.
{"title":"Multisensor fusion-based object detection and tracking using Active Shape Model","authors":"Dongeun Lee, Sunghoon Choi","doi":"10.1109/ICDIM.2011.6093321","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093321","url":null,"abstract":"This paper proposes automatic target detection and tracking system using Active Shape Model (ASM). Existing model based approaches for tracking are either manually initiated or need some form of user interaction to locate the object in images. Also the low light environmental conditions for surveillance systems make the tracking further harder. Hence the proposed system makes use of multiple sensors in the form of IR and visible cameras to enable tracking in degraded and low light environments. The proposed algorithm consists of the following stages: (i) input image evaluation for obtaining the conditions under which the camera is placed, (ii) an integrated motion detector and target tracker, (iii) active shape tracker(AST) for performing tracking, (iv) update of tracking results for real time tracking of targets. In the first stage the input image is evaluated for the lighting conditions. If the lighting conditions are poor then IR sensor is integrated with the CCD sensor for tracking applications. In the second stage the motion detector and region tracker are used to provide feedback to AST for automatic initialization of tracking. Tracking is carried out in the third stage using ASM. The final stage extracts the parameters and tracking information and applies it to the next frame if the tracking is carried out in real time. The major contribution this work lies in the integration for a completed system, which covers from image processing to tracking algorithms. The approach of combining multiple algorithms succeeds in overcoming fundamental limitations of tracking and at the same time realizes real time implementation. Experimental results show that the proposed algorithm can track people under various environment in real-time. The proposed system has potential uses in the area of surveillance, shape analysis, and model-based coding.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"422 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128509762","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093331
Dina Sadat Jalali, Alireza Shahrbanoonezhad
In this paper we express a new intrusion detection system based FSM (Finite State Machine) in ad hoc networks. Security is one of the most important issues in current networks. The most common cases of attacks in mobile Ad hoc networks can be drop of routing packages and changes in the incoming packet which aims at disrupting the network routing and overall network reduce performance. The presented approach based on FSM focuses at recognizing the malicious nodes within the network in a fast and accurate way, then it deals with rapid introduction of the malicious nodes to other nodes in the network to prevent sending multiple packets and drop and packet change. Finally, we will show the significant improvement of some factors in comparison with other last works and we simulated our methods by NS2 software.
{"title":"A new method based on Finite State Machine for detecting misbehaving nodes in ad hoc networks","authors":"Dina Sadat Jalali, Alireza Shahrbanoonezhad","doi":"10.1109/ICDIM.2011.6093331","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093331","url":null,"abstract":"In this paper we express a new intrusion detection system based FSM (Finite State Machine) in ad hoc networks. Security is one of the most important issues in current networks. The most common cases of attacks in mobile Ad hoc networks can be drop of routing packages and changes in the incoming packet which aims at disrupting the network routing and overall network reduce performance. The presented approach based on FSM focuses at recognizing the malicious nodes within the network in a fast and accurate way, then it deals with rapid introduction of the malicious nodes to other nodes in the network to prevent sending multiple packets and drop and packet change. Finally, we will show the significant improvement of some factors in comparison with other last works and we simulated our methods by NS2 software.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127496354","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}
Pub Date : 2011-12-01DOI: 10.1109/ICDIM.2011.6093352
Y. Gotoh, Kentaro Suzuki, T. Yoshihisa, H. Taniguchi, M. Kanazawa
Due to the recent spread of streaming delivery of movies, streaming using Peer-to-Peer (P2P) streaming technology has attracted much attention. In P2P streaming systems, to distribute the network load, peers from which the user receives data is selected at random. For this, clients have to wait until their desired data is delivered. Therefore, there are many researches to reduce the waiting time. However, because of the complexity of the implementation, they usually evaluate these methods using computer simulations. In actual environment, indeed, interruption time is not always reduced by increasing clients to deliver data. To evaluate the effectiveness of P2P streaming systems, it is important to implement an actual P2P streaming system. In this paper, we evaluate a P2P streaming system. By using our implemented P2P streaming system, we investigate situations that its system is effective. As a result of our evaluation, we confirmed that interruption time is reduced effectively.
{"title":"Evaluation of P2P streaming systems for webcast","authors":"Y. Gotoh, Kentaro Suzuki, T. Yoshihisa, H. Taniguchi, M. Kanazawa","doi":"10.1109/ICDIM.2011.6093352","DOIUrl":"https://doi.org/10.1109/ICDIM.2011.6093352","url":null,"abstract":"Due to the recent spread of streaming delivery of movies, streaming using Peer-to-Peer (P2P) streaming technology has attracted much attention. In P2P streaming systems, to distribute the network load, peers from which the user receives data is selected at random. For this, clients have to wait until their desired data is delivered. Therefore, there are many researches to reduce the waiting time. However, because of the complexity of the implementation, they usually evaluate these methods using computer simulations. In actual environment, indeed, interruption time is not always reduced by increasing clients to deliver data. To evaluate the effectiveness of P2P streaming systems, it is important to implement an actual P2P streaming system. In this paper, we evaluate a P2P streaming system. By using our implemented P2P streaming system, we investigate situations that its system is effective. As a result of our evaluation, we confirmed that interruption time is reduced effectively.","PeriodicalId":355775,"journal":{"name":"2011 Sixth International Conference on Digital Information Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115379764","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：