Ahmad Slim, G. Heileman, Wisam Al-Doroubi, C. Abdallah
Many universities are working to improve their graduation rates. The factors that correlate to student success and hence graduation rates are many, varying from pre-institutional factors, including high school GPA and admissions scores, to institutional factors, including student support services and the quality of faculty. An essential institutional factor that is often overlooked is the structure of the curriculum. In this paper we consider the degree to which the underlying curriculum that a student must traverse in order to earn a degree impacts progress. Using data mining methods, complex network analysis and graph theory, this paper proposes a framework for analyzing university course enrollment networks at the program level. The analyses we provide are based on quantifying the importance of course enrollment sequences on a student's final GPA, a metric that is highly correlated to graduation rates. In particular, we investigate the orderings of courses enrollment sequences that best contribute to student performance and achievement. Experimental results, using data from the University of New Mexico, show that Electrical Engineering students who graduated with "high GPA" values tend to follow a common course enrollment sequence that is quite different than that of students who graduated with relatively "low GPA" values. This work may be useful to both students and decision makers at universities as it presents a robust framework for analyzing the ease of flow of students through curricula, which may lead to improvements that facilitate improved student success.
{"title":"The Impact of Course Enrollment Sequences on Student Success","authors":"Ahmad Slim, G. Heileman, Wisam Al-Doroubi, C. Abdallah","doi":"10.1109/AINA.2016.140","DOIUrl":"https://doi.org/10.1109/AINA.2016.140","url":null,"abstract":"Many universities are working to improve their graduation rates. The factors that correlate to student success and hence graduation rates are many, varying from pre-institutional factors, including high school GPA and admissions scores, to institutional factors, including student support services and the quality of faculty. An essential institutional factor that is often overlooked is the structure of the curriculum. In this paper we consider the degree to which the underlying curriculum that a student must traverse in order to earn a degree impacts progress. Using data mining methods, complex network analysis and graph theory, this paper proposes a framework for analyzing university course enrollment networks at the program level. The analyses we provide are based on quantifying the importance of course enrollment sequences on a student's final GPA, a metric that is highly correlated to graduation rates. In particular, we investigate the orderings of courses enrollment sequences that best contribute to student performance and achievement. Experimental results, using data from the University of New Mexico, show that Electrical Engineering students who graduated with \"high GPA\" values tend to follow a common course enrollment sequence that is quite different than that of students who graduated with relatively \"low GPA\" values. This work may be useful to both students and decision makers at universities as it presents a robust framework for analyzing the ease of flow of students through curricula, which may lead to improvements that facilitate improved student success.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125556793","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}
Tanveer Khan, Israr Ahmad, Waqas Aman, Irfan Azam, Z. Khan, U. Qasim, Sanam Avais, N. Javaid
Large propagation delay, high error rate, low band-width and limited energy in Underwater Sensor Networks (UWSNs) attract the attention of most researchers. In UWSNs, efficient utilization of energy is one of the major issue, as the replacement of energy sources in such environment is very expensive. In this paper, we have proposed a Cluster Depth Based Routing (cDBR) that is based on existing Depth Based Routing (DBR) protocol. In DBR, routing is based on the depth of the sensor nodes: the nodes having less depth are used as a forward nodes and consumes more energy as compared to the rest of nodes. As a result, nodes nearer to sink dies first because of more load. In cDBR, cluster based approach is used. In order to minimize the energy consumption, load among all the nodes are distributed equally. The energy consumption of each node is equally utilized as each node has equal probability to be selected as a Cluster Head (CH). This improves the stability period of network from DBR. In cDBR Cluster Heads (CHs) are used for forwarding packets that maximizes throughput of the network. We have compared our results with DBR and Energy Efficient DBR (EEDBR). The simulation result validates that cDBR achieves better stability period and high throughput comparatively to DBR and EEDBR.
{"title":"Clustering Depth Based Routing for Underwater Wireless Sensor Networks","authors":"Tanveer Khan, Israr Ahmad, Waqas Aman, Irfan Azam, Z. Khan, U. Qasim, Sanam Avais, N. Javaid","doi":"10.1109/AINA.2016.168","DOIUrl":"https://doi.org/10.1109/AINA.2016.168","url":null,"abstract":"Large propagation delay, high error rate, low band-width and limited energy in Underwater Sensor Networks (UWSNs) attract the attention of most researchers. In UWSNs, efficient utilization of energy is one of the major issue, as the replacement of energy sources in such environment is very expensive. In this paper, we have proposed a Cluster Depth Based Routing (cDBR) that is based on existing Depth Based Routing (DBR) protocol. In DBR, routing is based on the depth of the sensor nodes: the nodes having less depth are used as a forward nodes and consumes more energy as compared to the rest of nodes. As a result, nodes nearer to sink dies first because of more load. In cDBR, cluster based approach is used. In order to minimize the energy consumption, load among all the nodes are distributed equally. The energy consumption of each node is equally utilized as each node has equal probability to be selected as a Cluster Head (CH). This improves the stability period of network from DBR. In cDBR Cluster Heads (CHs) are used for forwarding packets that maximizes throughput of the network. We have compared our results with DBR and Energy Efficient DBR (EEDBR). The simulation result validates that cDBR achieves better stability period and high throughput comparatively to DBR and EEDBR.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128764834","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}
A. Alkalbani, Ahmed Mohamed Ghamry, F. Hussain, O. Hussain
With the rapid growth of cloud services, there has been a significant increase in the number of online consumer reviews and opinions on these services on different social media platforms. These reviews are a source of valuable information in regard to cloud market position and cloud consumer satisfaction. This study explores cloud consumers' reviews that reflect the user's experience with Software as a Service (SaaS) applications. The reviews were collected from different web portals, and around 4000 online reviews were analysed using sentiment analysis to identify the polarity of each review, that is, whether the sentiment being expressed is positive, negative, or neutral. Also, this research develops a model for predicting the sentiment of Software as a Service consumers' reviews using a supervised learning machine called a support vector machine (SVM). The sentiment results show that 62% of the reviews are positive which indicates that consumers are most likely satisfied with SaaS services. The results show that the prediction accuracy of the SVM-based Binary Occurrence approach (3-fold crossvalidation testing) is 92.30%, indicating it performs better in determining sentiment compared with other approaches (Term Occurrences, TFIDF). This work also provides valuable insight into online SaaS reviews and offers the research community the first SaaS polarity dataset.
{"title":"Sentiment Analysis and Classification for Software as a Service Reviews","authors":"A. Alkalbani, Ahmed Mohamed Ghamry, F. Hussain, O. Hussain","doi":"10.1109/AINA.2016.148","DOIUrl":"https://doi.org/10.1109/AINA.2016.148","url":null,"abstract":"With the rapid growth of cloud services, there has been a significant increase in the number of online consumer reviews and opinions on these services on different social media platforms. These reviews are a source of valuable information in regard to cloud market position and cloud consumer satisfaction. This study explores cloud consumers' reviews that reflect the user's experience with Software as a Service (SaaS) applications. The reviews were collected from different web portals, and around 4000 online reviews were analysed using sentiment analysis to identify the polarity of each review, that is, whether the sentiment being expressed is positive, negative, or neutral. Also, this research develops a model for predicting the sentiment of Software as a Service consumers' reviews using a supervised learning machine called a support vector machine (SVM). The sentiment results show that 62% of the reviews are positive which indicates that consumers are most likely satisfied with SaaS services. The results show that the prediction accuracy of the SVM-based Binary Occurrence approach (3-fold crossvalidation testing) is 92.30%, indicating it performs better in determining sentiment compared with other approaches (Term Occurrences, TFIDF). This work also provides valuable insight into online SaaS reviews and offers the research community the first SaaS polarity dataset.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127631183","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}
Elias Vathias, Dimitrios S. Nikolopoulos, S. Hadjiefthymiades
We establish a framework that can be used by Origin Servers (content-generating organizations) for claiming Content Delivery Network (CDN) resources in a fine-grained way. The basis of our work lies in the use of Stocks as well as a Secondary Market for the stock trading, tools and products commonly used in modern capital markets. Network and disk resources are being monitored through well-established techniques (Kernel Regression Estimators) in a given time frame (typically in the order to an hour). This information is dynamically updated to reflect transient behavior and longer-term trends. The model that we establish relies initially on the resource use forecasting that the kernels can provide but also on other complementary techniques. To cope with inaccuracies in the predictive capability of the prediction mechanism (that would imply inefficient use of the claimed resources) we establish a Secondary Market for the stock trading. This scheme allows the fast offloading of unused resources. Thus, all the involved players (including the CDN itself) benefit from the rationalized use of CDN resources.
{"title":"A Capital Market Metaphor for Content Delivery Network Resources","authors":"Elias Vathias, Dimitrios S. Nikolopoulos, S. Hadjiefthymiades","doi":"10.1109/AINA.2016.108","DOIUrl":"https://doi.org/10.1109/AINA.2016.108","url":null,"abstract":"We establish a framework that can be used by Origin Servers (content-generating organizations) for claiming Content Delivery Network (CDN) resources in a fine-grained way. The basis of our work lies in the use of Stocks as well as a Secondary Market for the stock trading, tools and products commonly used in modern capital markets. Network and disk resources are being monitored through well-established techniques (Kernel Regression Estimators) in a given time frame (typically in the order to an hour). This information is dynamically updated to reflect transient behavior and longer-term trends. The model that we establish relies initially on the resource use forecasting that the kernels can provide but also on other complementary techniques. To cope with inaccuracies in the predictive capability of the prediction mechanism (that would imply inefficient use of the claimed resources) we establish a Secondary Market for the stock trading. This scheme allows the fast offloading of unused resources. Thus, all the involved players (including the CDN itself) benefit from the rationalized use of CDN resources.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116714453","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}
Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.
{"title":"Ontology-Based Information Extraction for Knowledge Enrichment and Validation","authors":"Dhomas Hatta Fudholi, J. Rahayu, E. Pardede","doi":"10.1109/AINA.2016.70","DOIUrl":"https://doi.org/10.1109/AINA.2016.70","url":null,"abstract":"Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116136285","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}
Yufeng Wang, F. Tang, Yanqin Yang, Jie Li, Wenchao Xu, Jinsong Wu
The benefits of network layer cooperation cognitive radio networks have been gradually recognized in recent years. In this paper we consider the network layer cooperation in cognitive radio network, whereby primary users select some secondary users to relay packets, in return for more favourable spectrum access rules for secondary users. Under this cooperation scheme, we investigate how to enlarge the throughput of the whole network, where a QoS(Quality of Service)-guaranteed adaptive cooperation scheme is developed. Our scheme can guarantee the QoS demand of primary users and update its frequency division cooperation scheme dynamically according to the status of nodes. Our algorithm requires knowledge of only instantaneous queue lengths at secondary nodes and the predictable end-to-end delay. Simulation results reveal that our proposed scheme significantly outperforms previous works in terms of throughput.
{"title":"A QoS-Guaranteed Adaptive Cooperation Scheme in Cognitive Radio Network","authors":"Yufeng Wang, F. Tang, Yanqin Yang, Jie Li, Wenchao Xu, Jinsong Wu","doi":"10.1109/AINA.2016.128","DOIUrl":"https://doi.org/10.1109/AINA.2016.128","url":null,"abstract":"The benefits of network layer cooperation cognitive radio networks have been gradually recognized in recent years. In this paper we consider the network layer cooperation in cognitive radio network, whereby primary users select some secondary users to relay packets, in return for more favourable spectrum access rules for secondary users. Under this cooperation scheme, we investigate how to enlarge the throughput of the whole network, where a QoS(Quality of Service)-guaranteed adaptive cooperation scheme is developed. Our scheme can guarantee the QoS demand of primary users and update its frequency division cooperation scheme dynamically according to the status of nodes. Our algorithm requires knowledge of only instantaneous queue lengths at secondary nodes and the predictable end-to-end delay. Simulation results reveal that our proposed scheme significantly outperforms previous works in terms of throughput.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115762013","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}
Shinji Sakamoto, Tetsuya Oda, Makoto Ikeda, L. Barolli, F. Xhafa
With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented the Linearly Decreasing Vmax Method (LDVM) for our WMN-PSO simulation system. In this paper, we implement a new replacement method for mesh routers called Rational Decrement of Vmax Method (RDVM). We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. From the simulation results, we found that RDVM converges faster to best solution than LDVM.
{"title":"Implementation of a New Replacement Method in WMN-PSO Simulation System and Its Performance Evaluation","authors":"Shinji Sakamoto, Tetsuya Oda, Makoto Ikeda, L. Barolli, F. Xhafa","doi":"10.1109/AINA.2016.42","DOIUrl":"https://doi.org/10.1109/AINA.2016.42","url":null,"abstract":"With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented the Linearly Decreasing Vmax Method (LDVM) for our WMN-PSO simulation system. In this paper, we implement a new replacement method for mesh routers called Rational Decrement of Vmax Method (RDVM). We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. From the simulation results, we found that RDVM converges faster to best solution than LDVM.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125840681","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}
Together with the popularity of graph structure in real world data, graph analysis has become an attractive research topic. Triangle counting is one of typical graph mining tasks and plays a significant role in complex network analysis, with a wide range of applications in social network analysis, spam detection, and computer-aided design applications. Given the sensitive nature of graph data, the disclosure of graph content for the purpose of analysis may raise a major privacy concern to both individuals and organizations. In this paper, we propose a protocol for privacy-preserving triangle counting when the overall graph is distributed to different parties. We firstly present the solution in a two-party scenario and then extend the protocol to any number of participating parties. The graph structure is represented by an adjacency matrix, and cryptographic secure matrix computation is utilized to tackle the problem. A secure multi-party power of matrix sum protocol is proposed to serve as a building block for the solution. We have evaluated the computational complexity and scalability of the proposed protocols both analytically and empirically. The results show that the protocols are efficient and scalable for small and medium size data.
{"title":"Privacy-Preserving Triangle Counting in Distributed Graphs","authors":"H. Do, W. Ng","doi":"10.1109/AINA.2016.28","DOIUrl":"https://doi.org/10.1109/AINA.2016.28","url":null,"abstract":"Together with the popularity of graph structure in real world data, graph analysis has become an attractive research topic. Triangle counting is one of typical graph mining tasks and plays a significant role in complex network analysis, with a wide range of applications in social network analysis, spam detection, and computer-aided design applications. Given the sensitive nature of graph data, the disclosure of graph content for the purpose of analysis may raise a major privacy concern to both individuals and organizations. In this paper, we propose a protocol for privacy-preserving triangle counting when the overall graph is distributed to different parties. We firstly present the solution in a two-party scenario and then extend the protocol to any number of participating parties. The graph structure is represented by an adjacency matrix, and cryptographic secure matrix computation is utilized to tackle the problem. A secure multi-party power of matrix sum protocol is proposed to serve as a building block for the solution. We have evaluated the computational complexity and scalability of the proposed protocols both analytically and empirically. The results show that the protocols are efficient and scalable for small and medium size data.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"96 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856259","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}
L. F. Assis, Lucas P. Behnck, D. Doering, E. P. Freitas, C. Pereira, F. Horita, J. Ueyama, J. Albuquerque
Wireless Sensor Networks (WSN) are often composed of a wide range of sensor nodes, which may vary greatly in their type of hardware platform, as well as their sensing and mobility capabilities. The ability of a sensor to move is a particularly important feature in dynamic scenarios, since mobile sensors can fill the gap caused by the failures of those that are stationary, and thus extend the lifetime and span of a WSN. However, there remains the problems of intersensory communication in the field when integrating mobile sensors into the Sensor Web in dynamic scenarios since it does not have the necessary interoperability for automatically managing the different types of sensor data and activities involved in such scenarios. This paper tackles this problem by adopting an approach consisting of an enhanced messaging protocol and a dynamic sensor management component. In validating the proposal, two different realistic scenarios were simulated to evaluate the achieved results in terms of interoperability and performance. The results provided evidence that the proposal complies with Sensor Web standards as well as being suitable for near real-time data publication, and is thus able to support applications in dynamic scenarios.
{"title":"Dynamic Sensor Management: Extending Sensor Web for Near Real-Time Mobile Sensor Integration in Dynamic Scenarios","authors":"L. F. Assis, Lucas P. Behnck, D. Doering, E. P. Freitas, C. Pereira, F. Horita, J. Ueyama, J. Albuquerque","doi":"10.1109/AINA.2016.100","DOIUrl":"https://doi.org/10.1109/AINA.2016.100","url":null,"abstract":"Wireless Sensor Networks (WSN) are often composed of a wide range of sensor nodes, which may vary greatly in their type of hardware platform, as well as their sensing and mobility capabilities. The ability of a sensor to move is a particularly important feature in dynamic scenarios, since mobile sensors can fill the gap caused by the failures of those that are stationary, and thus extend the lifetime and span of a WSN. However, there remains the problems of intersensory communication in the field when integrating mobile sensors into the Sensor Web in dynamic scenarios since it does not have the necessary interoperability for automatically managing the different types of sensor data and activities involved in such scenarios. This paper tackles this problem by adopting an approach consisting of an enhanced messaging protocol and a dynamic sensor management component. In validating the proposal, two different realistic scenarios were simulated to evaluate the achieved results in terms of interoperability and performance. The results provided evidence that the proposal complies with Sensor Web standards as well as being suitable for near real-time data publication, and is thus able to support applications in dynamic scenarios.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130651800","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}
Cheng Xie, M. Chekol, Blerina Spahiu, Hongming Cai
Ontology matching is an important part of enabling the semantic web to reach its full potential. Most existing ontology matching methods are mainly based on linguistic information (label, name, title and comment) but from the results achieved it is realized that this information is not sufficient. The latest ontology matching research works are trying to deeply dig into the structural information of ontologies by using "similarity-flooding" method. However, there are several innate issues in similarity-flooding methods that lead to wrong matching results. In this paper, we report the problems of similarity-flooding in ontology matching and propose a novel method to effectively leverage the structural information of the ontology. The evaluation is conducted on OAEI ontology matching benchmarks from 2011 to 2015. The result shows that the proposed approach performs comparatively well with other state of the art matching systems.
{"title":"Leveraging Structural Information in Ontology Matching","authors":"Cheng Xie, M. Chekol, Blerina Spahiu, Hongming Cai","doi":"10.1109/AINA.2016.64","DOIUrl":"https://doi.org/10.1109/AINA.2016.64","url":null,"abstract":"Ontology matching is an important part of enabling the semantic web to reach its full potential. Most existing ontology matching methods are mainly based on linguistic information (label, name, title and comment) but from the results achieved it is realized that this information is not sufficient. The latest ontology matching research works are trying to deeply dig into the structural information of ontologies by using \"similarity-flooding\" method. However, there are several innate issues in similarity-flooding methods that lead to wrong matching results. In this paper, we report the problems of similarity-flooding in ontology matching and propose a novel method to effectively leverage the structural information of the ontology. The evaluation is conducted on OAEI ontology matching benchmarks from 2011 to 2015. The result shows that the proposed approach performs comparatively well with other state of the art matching systems.","PeriodicalId":438655,"journal":{"name":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131890259","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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