Pub Date : 2015-02-01DOI: 10.1109/ICOSC.2015.7050833
Fengjiao Wang, Guan Wang, Shuyang Lin, Philip S. Yu
Recent years, social network has attracted many attentions from research communities in data mining, social science and mobile etc, since users can create different types of information due to different actions and the information gives us the opportunities to better understand the insights of people's social lives. Co-clustering is an important technique to detect patterns and phenomena of two types of closely related objects. For example, in a location based social network, places can be clustered with regards to location and category, respectively and users can be clustered w.r.t. their location and interests, respectively. Therefore, there are usually some latent goals behind a co-clustering application. However, traditionally, co-clustering methods are not specifically designed to handle multiple goals. That leaves certain drawbacks, i.e., it cannot guarantee that objects satisfying each individual goal would be clustered into the same cluster. However, in many cases, clusters of objects meeting the same goal is required, e.g., a user may want to search places within one category but in different locations. In this paper, we propose a goal-oriented co-clustering model, which could generate co-clusterings with regards to different goals simultaneously. By this method, we could get co-clusterings containing objects with desired aspects of information from the original data source. Seed features sets are pre-selected to represent goals of co-clusterings. By generating expanded feature sets from seed feature sets, the proposed model concurrently co-clustering objects and assigning other features to different feature clusters.
{"title":"Concurrent goal-oriented co-clustering generation in social networks","authors":"Fengjiao Wang, Guan Wang, Shuyang Lin, Philip S. Yu","doi":"10.1109/ICOSC.2015.7050833","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050833","url":null,"abstract":"Recent years, social network has attracted many attentions from research communities in data mining, social science and mobile etc, since users can create different types of information due to different actions and the information gives us the opportunities to better understand the insights of people's social lives. Co-clustering is an important technique to detect patterns and phenomena of two types of closely related objects. For example, in a location based social network, places can be clustered with regards to location and category, respectively and users can be clustered w.r.t. their location and interests, respectively. Therefore, there are usually some latent goals behind a co-clustering application. However, traditionally, co-clustering methods are not specifically designed to handle multiple goals. That leaves certain drawbacks, i.e., it cannot guarantee that objects satisfying each individual goal would be clustered into the same cluster. However, in many cases, clusters of objects meeting the same goal is required, e.g., a user may want to search places within one category but in different locations. In this paper, we propose a goal-oriented co-clustering model, which could generate co-clusterings with regards to different goals simultaneously. By this method, we could get co-clusterings containing objects with desired aspects of information from the original data source. Seed features sets are pre-selected to represent goals of co-clusterings. By generating expanded feature sets from seed feature sets, the proposed model concurrently co-clustering objects and assigning other features to different feature clusters.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129131357","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 : 2015-02-01DOI: 10.1109/ICOSC.2015.7050793
M. Vora
In a service oriented world, performance plays a vital role in the success of any IT system. For an application running in a production environment, whenever there is a change in the workload or workload pattern, utilization of major server resources like cpus, disks, memory, network etc. will also change. In this paper, we are extending our methodology to estimate the server resource utilization for any given workload pattern by extracting the optimal information from the historic production logs (application logs and resource utilization or system monitoring logs) using a specifically designed genetic algorithm. Across all experimental validations, we find the average absolute error in estimating utilization of server resources was less than 15%. Unlike traditional approaches to estimate overall resource utilization, method presented here, neither requires to estimate service demands for each individual application functions nor does it require to benchmark individual business transactions. Only necessary input to the model is the application logs containing the information about the throughput (for example an access log in case of web application) and system monitoring logs containing aggregate resource utilization information.
{"title":"Workload pattern analysis","authors":"M. Vora","doi":"10.1109/ICOSC.2015.7050793","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050793","url":null,"abstract":"In a service oriented world, performance plays a vital role in the success of any IT system. For an application running in a production environment, whenever there is a change in the workload or workload pattern, utilization of major server resources like cpus, disks, memory, network etc. will also change. In this paper, we are extending our methodology to estimate the server resource utilization for any given workload pattern by extracting the optimal information from the historic production logs (application logs and resource utilization or system monitoring logs) using a specifically designed genetic algorithm. Across all experimental validations, we find the average absolute error in estimating utilization of server resources was less than 15%. Unlike traditional approaches to estimate overall resource utilization, method presented here, neither requires to estimate service demands for each individual application functions nor does it require to benchmark individual business transactions. Only necessary input to the model is the application logs containing the information about the throughput (for example an access log in case of web application) and system monitoring logs containing aggregate resource utilization information.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968558","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 : 2015-02-01DOI: 10.1109/ICOSC.2015.7050818
Qingliang Miao, Yao Meng, Lu Fang, Fumihito Nishino, N. Igata
Scientific publication management services are changing drastically. On the one hand, researchers demand intelligent search services to discover scientific publications. On the other hand, publishers need to incorporate semantic information to better organize their digital assets and make publications more discoverable. For this purpose, we investigate how to manage scientific publications using Linked Data and introduce FELinker, an entity linking component that links scientific publications with DBPedia. In particular, this paper introduces advantages of linking scientific publications with Linked Data, discusses major challenges, and outlines the proposed method. Experiment shows the proposed method could get promising performance in scientific publication linkage.
{"title":"Link scientific publications using linked data","authors":"Qingliang Miao, Yao Meng, Lu Fang, Fumihito Nishino, N. Igata","doi":"10.1109/ICOSC.2015.7050818","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050818","url":null,"abstract":"Scientific publication management services are changing drastically. On the one hand, researchers demand intelligent search services to discover scientific publications. On the other hand, publishers need to incorporate semantic information to better organize their digital assets and make publications more discoverable. For this purpose, we investigate how to manage scientific publications using Linked Data and introduce FELinker, an entity linking component that links scientific publications with DBPedia. In particular, this paper introduces advantages of linking scientific publications with Linked Data, discusses major challenges, and outlines the proposed method. Experiment shows the proposed method could get promising performance in scientific publication linkage.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124237986","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 : 2015-02-01DOI: 10.1109/ICOSC.2015.7050839
Krishna Sapkota, Pathmeswaran Raju, William J. Byrne, C. Chapman
Bioenergy is a renewable energy generated from biomass, while biofuel is a hydrocarbon fuel that is produced from biomass. Recently, bioenergy and biofuel projects are encouraged and supported by many governments and organizations in various ways such as providing incentives, technical supports, information, and decision support tools. Economic model is one of the decision support tools, which helps to estimate the costs and earnings involved in a project. It is constructed with various elements such as concepts, relations, logics, constants and equations. In current economic models, all the elements are hard coded into some programming code, which makes the model less reusable and extendable. To address the issue, we present an ontology-based economic model in this paper. In particular, we have leveraged the Semantic Web technologies to represent the knowledge about the bioenergy and biofuel economics and inferred the equations and other values required for economic calculations. The case study has been carried out in two of the INTERREG Projects and found promising results.
{"title":"Ontology-based economic models for bioenergy and biofuel projects","authors":"Krishna Sapkota, Pathmeswaran Raju, William J. Byrne, C. Chapman","doi":"10.1109/ICOSC.2015.7050839","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050839","url":null,"abstract":"Bioenergy is a renewable energy generated from biomass, while biofuel is a hydrocarbon fuel that is produced from biomass. Recently, bioenergy and biofuel projects are encouraged and supported by many governments and organizations in various ways such as providing incentives, technical supports, information, and decision support tools. Economic model is one of the decision support tools, which helps to estimate the costs and earnings involved in a project. It is constructed with various elements such as concepts, relations, logics, constants and equations. In current economic models, all the elements are hard coded into some programming code, which makes the model less reusable and extendable. To address the issue, we present an ontology-based economic model in this paper. In particular, we have leveraged the Semantic Web technologies to represent the knowledge about the bioenergy and biofuel economics and inferred the equations and other values required for economic calculations. The case study has been carried out in two of the INTERREG Projects and found promising results.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134516140","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 : 2015-02-01DOI: 10.1109/ICOSC.2015.7050860
Yuan-Chih Yu
When disaster strikes the urban community, residents may suffer life-threatening, environmental impact, and economic loss. Meanwhile, the cellular and Internet services are prone to be fail because the disaster may cause the network infrastructure damage. As the communication service is so important for the disaster response, we suggest an emergency social networking solution, called ECSN, to conquer such crisis. ECSN is a community-based emergent social networking service suitable for dealing with the tasks on disaster response. To provide the ECSN service, we construct a Disaster Response Portal dedicated designed for disaster management. From the software architecture perspective, it has mobile client agents and server-side services working together to realize the concept of “Community Social Networking”. Through the Disaster Response Portal, the local disaster rescue and response can be integrated with nationwide disaster management. Also, disaster response can be easily planned and manageable at a community scope and benefits other emergent measures taken by disaster prevention, mitigation, preparedness, and recovery. After simulation experiments validate, the result shows the system can work well on the problem domain. Most importantly, the total solution creates a new practicable model for the mobility of urban disaster response.
{"title":"A mobile social networking service for urban community disaster response","authors":"Yuan-Chih Yu","doi":"10.1109/ICOSC.2015.7050860","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050860","url":null,"abstract":"When disaster strikes the urban community, residents may suffer life-threatening, environmental impact, and economic loss. Meanwhile, the cellular and Internet services are prone to be fail because the disaster may cause the network infrastructure damage. As the communication service is so important for the disaster response, we suggest an emergency social networking solution, called ECSN, to conquer such crisis. ECSN is a community-based emergent social networking service suitable for dealing with the tasks on disaster response. To provide the ECSN service, we construct a Disaster Response Portal dedicated designed for disaster management. From the software architecture perspective, it has mobile client agents and server-side services working together to realize the concept of “Community Social Networking”. Through the Disaster Response Portal, the local disaster rescue and response can be integrated with nationwide disaster management. Also, disaster response can be easily planned and manageable at a community scope and benefits other emergent measures taken by disaster prevention, mitigation, preparedness, and recovery. After simulation experiments validate, the result shows the system can work well on the problem domain. Most importantly, the total solution creates a new practicable model for the mobility of urban disaster response.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115658262","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 : 2015-02-01DOI: 10.1109/ICOSC.2015.7050813
Carmelo Spiccia, A. Augello, G. Pilato, G. Vassallo
Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network language models.
{"title":"A word prediction methodology for automatic sentence completion","authors":"Carmelo Spiccia, A. Augello, G. Pilato, G. Vassallo","doi":"10.1109/ICOSC.2015.7050813","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050813","url":null,"abstract":"Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network language models.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121924075","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 : 2015-02-01DOI: 10.1109/ICOSC.2015.7050859
Shailja Sharma, J. Lather, M. Dave
The Current description standards for Web Services such as WSDL and UDDI have a significant drawback of being restricted to the syntactic aspects of service. A service provider registers a service in the universal repository i.e. UDDI so that the service consumers can search and discover the required service that meets the user functional requirements from thousands of registered services. Matching the user request with all services in a particular category of the repository is a cumbersome task. Semantic approaches are required to further assist the user in discovering relevant services. In this paper, we have proposed a semantic approach that gives ranked list of services based on the web based relatedness score and helps the users in the selection of potentially relevant and semantically similar services within a category. The proposed approach has been implemented on 80 OWLS services and the results have shown that the approach gives ranked list of services with ease of the selection process for the user.
{"title":"Google based hybrid approach for discovering services","authors":"Shailja Sharma, J. Lather, M. Dave","doi":"10.1109/ICOSC.2015.7050859","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050859","url":null,"abstract":"The Current description standards for Web Services such as WSDL and UDDI have a significant drawback of being restricted to the syntactic aspects of service. A service provider registers a service in the universal repository i.e. UDDI so that the service consumers can search and discover the required service that meets the user functional requirements from thousands of registered services. Matching the user request with all services in a particular category of the repository is a cumbersome task. Semantic approaches are required to further assist the user in discovering relevant services. In this paper, we have proposed a semantic approach that gives ranked list of services based on the web based relatedness score and helps the users in the selection of potentially relevant and semantically similar services within a category. The proposed approach has been implemented on 80 OWLS services and the results have shown that the approach gives ranked list of services with ease of the selection process for the user.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132458953","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 : 2015-02-01DOI: 10.1109/ICOSC.2015.7050814
D. Dou, Hao Wang, Haishan Liu
Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.
{"title":"Semantic data mining: A survey of ontology-based approaches","authors":"D. Dou, Hao Wang, Haishan Liu","doi":"10.1109/ICOSC.2015.7050814","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050814","url":null,"abstract":"Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127429812","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 : 2014-08-31DOI: 10.1109/ICOSC.2015.7050794
Udaya Raj Dhungana, S. Shakya, K. Baral, Bharat Sharma
This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy word based on the clue words. The related words for each sense of a polysemy word as well as single sense word are referred to as the clue words. The conventional WordNet organises nouns, verbs, adjectives and adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the structure of WordNet, we developed a new model of WordNet that organizes the different senses of polysemy words as well as the single sense words based on the clue words. These clue words for each sense of a polysemy word as well as for single sense word are used to disambiguate the correct meaning of the polysemy word in the given context using knowledge-based Word Sense Disambiguation (WSD) algorithms. The clue word can be a noun, verb, adjective or adverb.
{"title":"Word Sense Disambiguation using WSD specific WordNet of polysemy words","authors":"Udaya Raj Dhungana, S. Shakya, K. Baral, Bharat Sharma","doi":"10.1109/ICOSC.2015.7050794","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050794","url":null,"abstract":"This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy word based on the clue words. The related words for each sense of a polysemy word as well as single sense word are referred to as the clue words. The conventional WordNet organises nouns, verbs, adjectives and adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the structure of WordNet, we developed a new model of WordNet that organizes the different senses of polysemy words as well as the single sense words based on the clue words. These clue words for each sense of a polysemy word as well as for single sense word are used to disambiguate the correct meaning of the polysemy word in the given context using knowledge-based Word Sense Disambiguation (WSD) algorithms. The clue word can be a noun, verb, adjective or adverb.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123313424","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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