Pub Date : 2017-04-01DOI: 10.1109/ICWR.2017.7959321
Muna Eslami Jeyd, Alireza Yari
Today, Mobile Cloud Computing has been widely used and can send complex computations to the stronger server with more resources and get results from them to overcome the limitations of existing mobile devices, such as battery level, the amount of CPU and memory. Local mobile clouds, which consist of the mobile devices, are used as a suitable solution to support real-time applications, especially?. Due to share bandwidth and computing resources across all mobile devices, a task scheduling is required to ensure that multiple mobile devices can effectively assign works to local mobile clouds in such way that the time limitation is considered and the amount of remaining energy is estimated for reducing energy consumption. In this paper, we suggest energy-aware and adaptive task scheduler. The task scheduler discovers resources based on controlling messages periodically. This method, with an estimation of task completion time, calculates energy consumption and the amount of remaining energy in each processing node. Then, it schedules current work with a possible adaptive method at the processing node and sets time limitation in order to improve network efficiency under unpredictable conditions. The results of tests carried out on the proposed method compared to existing methods show that the proposed method has the lowest energy consumption per successful task. Moreover, the proposed method has scalability and high flexibility and can be deployed on any network.
{"title":"Advanced adaptive probabilities and energy aware algorithm for scheduling tasks in MCC","authors":"Muna Eslami Jeyd, Alireza Yari","doi":"10.1109/ICWR.2017.7959321","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959321","url":null,"abstract":"Today, Mobile Cloud Computing has been widely used and can send complex computations to the stronger server with more resources and get results from them to overcome the limitations of existing mobile devices, such as battery level, the amount of CPU and memory. Local mobile clouds, which consist of the mobile devices, are used as a suitable solution to support real-time applications, especially?. Due to share bandwidth and computing resources across all mobile devices, a task scheduling is required to ensure that multiple mobile devices can effectively assign works to local mobile clouds in such way that the time limitation is considered and the amount of remaining energy is estimated for reducing energy consumption. In this paper, we suggest energy-aware and adaptive task scheduler. The task scheduler discovers resources based on controlling messages periodically. This method, with an estimation of task completion time, calculates energy consumption and the amount of remaining energy in each processing node. Then, it schedules current work with a possible adaptive method at the processing node and sets time limitation in order to improve network efficiency under unpredictable conditions. The results of tests carried out on the proposed method compared to existing methods show that the proposed method has the lowest energy consumption per successful task. Moreover, the proposed method has scalability and high flexibility and can be deployed on any network.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123476321","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 : 2017-04-01DOI: 10.1109/ICWR.2017.7959297
Zahra Aminolroaya, Ali Katanforoush
Social media place where people communicate and share their ideas provide rich information for social network analysis. There are various analyses such as information diffusion modeling and community detection which are used to analyze data of social networks. In this paper, we investigate some novel aspects of hashtag diffusion among Iranian communities in Instagram in the period of the last legislative election in Iran. After data preparation, we analyze the validation of three different assumptions. First, we study the effects of follower-followee relations in the spread of the campaign hashtags. Based on the timestamps of the posts, we invoke NetRate method to estimate information diffusion rates over edges of follower-followee network. Then, by application of Louvain method as a community detection algorithm, we investigate the relation of community membership and contagion transmission rate. Finally, we study observed topical preferences in network communities. Results show the flow of information from followees to followers with a significant rate of diffusion over the whole network. However, being part of a specific community does not contribute to be exposed to a cascade faster than others. While the communities were defined based on modularity maximization and no information related to hashtags involved, a topical preference also is observed within the communities' hashtags which had the same orientation as observed in two major political parties of Iran.
{"title":"How Iranian Instagram users act for parliament election campaign? A study based on followee network","authors":"Zahra Aminolroaya, Ali Katanforoush","doi":"10.1109/ICWR.2017.7959297","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959297","url":null,"abstract":"Social media place where people communicate and share their ideas provide rich information for social network analysis. There are various analyses such as information diffusion modeling and community detection which are used to analyze data of social networks. In this paper, we investigate some novel aspects of hashtag diffusion among Iranian communities in Instagram in the period of the last legislative election in Iran. After data preparation, we analyze the validation of three different assumptions. First, we study the effects of follower-followee relations in the spread of the campaign hashtags. Based on the timestamps of the posts, we invoke NetRate method to estimate information diffusion rates over edges of follower-followee network. Then, by application of Louvain method as a community detection algorithm, we investigate the relation of community membership and contagion transmission rate. Finally, we study observed topical preferences in network communities. Results show the flow of information from followees to followers with a significant rate of diffusion over the whole network. However, being part of a specific community does not contribute to be exposed to a cascade faster than others. While the communities were defined based on modularity maximization and no information related to hashtags involved, a topical preference also is observed within the communities' hashtags which had the same orientation as observed in two major political parties of Iran.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"930 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127018521","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 : 2017-04-01DOI: 10.1109/ICWR.2017.7959302
H. Tabealhojeh, B. Shadgar, M. Tashakori
This paper presents a practical study of the Persian ontology matching. Ontology matching has a key role to develop the semantic web. Although many attempts are done to develop Persian ontologies, but the Persian ontology matching problem is still unresolved. This paper addresses the challenges of the Persian ontology matching. One of the most important prerequisites of design and develop efficient ontology matchers is standard benchmark datasets that allow a fair evaluation and comparison between different matchers. First, we generated a benchmark dataset for Persian ontology matching that we named it PersianFarm. PersianFarm is developed according to OntoFarm, the multilingual dataset of the Ontology Alignment Evaluation Initiative (OAEI). It consists of seven Persian ontologies and eleven reference alignments between them. Next, we evaluate a wide range of similarity metrics such as string based, structural and context-based similarities against PersianFarm dataset. Finally, different similarity metrics have been selected and combined to develop an appropriate Persian ontology matcher. The results that reported as F-measure rate, show that the mixture of similarities achieved reasonable results to match the concepts.
{"title":"Persian ontology matching: Challenges, dataset generation and similarity combination","authors":"H. Tabealhojeh, B. Shadgar, M. Tashakori","doi":"10.1109/ICWR.2017.7959302","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959302","url":null,"abstract":"This paper presents a practical study of the Persian ontology matching. Ontology matching has a key role to develop the semantic web. Although many attempts are done to develop Persian ontologies, but the Persian ontology matching problem is still unresolved. This paper addresses the challenges of the Persian ontology matching. One of the most important prerequisites of design and develop efficient ontology matchers is standard benchmark datasets that allow a fair evaluation and comparison between different matchers. First, we generated a benchmark dataset for Persian ontology matching that we named it PersianFarm. PersianFarm is developed according to OntoFarm, the multilingual dataset of the Ontology Alignment Evaluation Initiative (OAEI). It consists of seven Persian ontologies and eleven reference alignments between them. Next, we evaluate a wide range of similarity metrics such as string based, structural and context-based similarities against PersianFarm dataset. Finally, different similarity metrics have been selected and combined to develop an appropriate Persian ontology matcher. The results that reported as F-measure rate, show that the mixture of similarities achieved reasonable results to match the concepts.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134498966","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 : 2017-04-01DOI: 10.1109/ICWR.2017.7959301
E. Hamzei, F. Hakimpour
In our modern world, search engines have been proposed as one of the challenging research areas. One of the main issues in search engines studies is human computer interaction, which its aim is to understand user's needs. If there is no right query processing approach, the results will be invalid in most cases. Therefore, in this paper we present a new approach to process and analyze the queries for spatial search engines. Our algorithm is implemented in the three steps, including: iterative segmentation of the query, sub-queries processing by finding appropriate candidates for the location-names, the location-types and spatial relationships and finally checking the relationships among these candidates in validation phase. Generally using our method has two major advantages as the search engines can provide the capability of spatial analysis based on the specific process which leads to a better interaction between the users and the search application in geospatial realm and secondly because of the disambiguation technique, user reaches the more desirable result.
{"title":"Entity recognition and disambiguation for natural-language spatial search queries","authors":"E. Hamzei, F. Hakimpour","doi":"10.1109/ICWR.2017.7959301","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959301","url":null,"abstract":"In our modern world, search engines have been proposed as one of the challenging research areas. One of the main issues in search engines studies is human computer interaction, which its aim is to understand user's needs. If there is no right query processing approach, the results will be invalid in most cases. Therefore, in this paper we present a new approach to process and analyze the queries for spatial search engines. Our algorithm is implemented in the three steps, including: iterative segmentation of the query, sub-queries processing by finding appropriate candidates for the location-names, the location-types and spatial relationships and finally checking the relationships among these candidates in validation phase. Generally using our method has two major advantages as the search engines can provide the capability of spatial analysis based on the specific process which leads to a better interaction between the users and the search application in geospatial realm and secondly because of the disambiguation technique, user reaches the more desirable result.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124593531","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 : 2017-04-01DOI: 10.1109/ICWR.2017.7959319
M. Nosrati
Process modeling is a suitable tool for improving the business processes. Successful process modeling strongly depends on correct requirements engineering. In this paper, we proposed a combination approach for requirements elicitation for developing business models. To do this, BORE (Business-Oriented Requirements Engineering) method is utilized as the base of our work and it is enriched by the important features of the BDD (Business-driven development) method, in order to make the proposed approach appropriate for modeling the more complex processes. As the main result, our method eventuates in exact requirements elicitation that adapts the customers' needs. Also, it let us avoid any rework in the modeling of process. In this paper, we conduct a case study for the paper submission and publication system of a journal. The results of this study not only give a good experience of real world application of proposed approach on a web-based system, also it approves the proficiency of this approach for modeling the complex systems with many sub-processes and complicated relationships.
{"title":"Exact requirements engineering for developing business process models","authors":"M. Nosrati","doi":"10.1109/ICWR.2017.7959319","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959319","url":null,"abstract":"Process modeling is a suitable tool for improving the business processes. Successful process modeling strongly depends on correct requirements engineering. In this paper, we proposed a combination approach for requirements elicitation for developing business models. To do this, BORE (Business-Oriented Requirements Engineering) method is utilized as the base of our work and it is enriched by the important features of the BDD (Business-driven development) method, in order to make the proposed approach appropriate for modeling the more complex processes. As the main result, our method eventuates in exact requirements elicitation that adapts the customers' needs. Also, it let us avoid any rework in the modeling of process. In this paper, we conduct a case study for the paper submission and publication system of a journal. The results of this study not only give a good experience of real world application of proposed approach on a web-based system, also it approves the proficiency of this approach for modeling the complex systems with many sub-processes and complicated relationships.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126997785","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 : 2017-04-01DOI: 10.1109/ICWR.2017.7959316
M. Kargar, Abbas Oveissi
Today people use the Internet to find the answer to their questions. They mostly rather ask other users on Community Question Answering (CQA) sites for an answer than just searching the web. However, as Social Media becomes more popular, users tend to ask their questions on these networks, and ignore the benefits CQA sites offer. On the other hand, automatic Question Answering (QA) systems are unable to comprehend questions including images and implementing necessary algorithms for such systems is expensive. In this paper, we propose QA process based on Crowd sourcing, which runs on a QA open system. The system benefits from Crowd sourcing advantages, besides automation techniques. The model is operational and we have demonstrated that questions could be received from different heterogeneous sources, if the suitable procedures are used, and that the answer is obtained from the crowd in the proposed process based on Crowd sourcing. Moreover, the first Iranian crowd sourcing platform for complicated tasks is implemented, which could be used as a basis for future research.
{"title":"An open model for question answering systems based on Crowdsourcing","authors":"M. Kargar, Abbas Oveissi","doi":"10.1109/ICWR.2017.7959316","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959316","url":null,"abstract":"Today people use the Internet to find the answer to their questions. They mostly rather ask other users on Community Question Answering (CQA) sites for an answer than just searching the web. However, as Social Media becomes more popular, users tend to ask their questions on these networks, and ignore the benefits CQA sites offer. On the other hand, automatic Question Answering (QA) systems are unable to comprehend questions including images and implementing necessary algorithms for such systems is expensive. In this paper, we propose QA process based on Crowd sourcing, which runs on a QA open system. The system benefits from Crowd sourcing advantages, besides automation techniques. The model is operational and we have demonstrated that questions could be received from different heterogeneous sources, if the suitable procedures are used, and that the answer is obtained from the crowd in the proposed process based on Crowd sourcing. Moreover, the first Iranian crowd sourcing platform for complicated tasks is implemented, which could be used as a basis for future research.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115927658","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 : 2017-04-01DOI: 10.1109/ICWR.2017.7959305
M. Mesbahi, A. Rahmani, M. Hosseinzadeh
Cloud computing data centers offer high available and reliable infrastructures for hosting critical applications and data. These data centers host hundreds of thousands physical machines to response to incoming workload as job executing. In this paper, we analyze the Google cloud cluster properties to investigate the relationship among machine failures, updates, and job failures. We present the statistical properties of Google machines and job failures and attempt to correlate them during a 29-day period behave. We classify the machine and job failures per day and represent a reliability model for Google cluster machines using the Continues Time Markov Chains.
{"title":"Cloud dependability analysis: Characterizing Google cluster infrastructure reliability","authors":"M. Mesbahi, A. Rahmani, M. Hosseinzadeh","doi":"10.1109/ICWR.2017.7959305","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959305","url":null,"abstract":"Cloud computing data centers offer high available and reliable infrastructures for hosting critical applications and data. These data centers host hundreds of thousands physical machines to response to incoming workload as job executing. In this paper, we analyze the Google cloud cluster properties to investigate the relationship among machine failures, updates, and job failures. We present the statistical properties of Google machines and job failures and attempt to correlate them during a 29-day period behave. We classify the machine and job failures per day and represent a reliability model for Google cluster machines using the Continues Time Markov Chains.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127637326","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 : 2017-04-01DOI: 10.1109/ICWR.2017.7959299
S. Mobasheri, M. Shamsfard
This work addresses the problem of enabling machines to perform scientific tasks, e.g. reasoning based on scientific laws and definitions, recognizing inter-dependence of scientific domains, and answering queries about science corpus. The building blocks of science, such as scientific terms, laws, problems, solutions, theories and disciplines are traditionally represented as single, atomic nodes in scientific ontologies. This makes it difficult to distinguish those constituents and use them properly in the automation of scientific activities. We support the idea of adding structure to the representation of different constituents of science corpus. The structure of a scientific law, for instance, would be different from that of a solution to a given scientific problem. It is shown through examples that considering those different structures can help in reasoning about scientific knowledge. Moreover, the domain- independent aspects of different constituents of science have the potential to be factored out in a meta-ontology. This meta-science can also contain general reasoning machinery about science.
{"title":"A proposed representation framework for semantic science","authors":"S. Mobasheri, M. Shamsfard","doi":"10.1109/ICWR.2017.7959299","DOIUrl":"https://doi.org/10.1109/ICWR.2017.7959299","url":null,"abstract":"This work addresses the problem of enabling machines to perform scientific tasks, e.g. reasoning based on scientific laws and definitions, recognizing inter-dependence of scientific domains, and answering queries about science corpus. The building blocks of science, such as scientific terms, laws, problems, solutions, theories and disciplines are traditionally represented as single, atomic nodes in scientific ontologies. This makes it difficult to distinguish those constituents and use them properly in the automation of scientific activities. We support the idea of adding structure to the representation of different constituents of science corpus. The structure of a scientific law, for instance, would be different from that of a solution to a given scientific problem. It is shown through examples that considering those different structures can help in reasoning about scientific knowledge. Moreover, the domain- independent aspects of different constituents of science have the potential to be factored out in a meta-ontology. This meta-science can also contain general reasoning machinery about science.","PeriodicalId":304897,"journal":{"name":"2017 3th International Conference on Web Research (ICWR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127965491","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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