Pub Date : 2014-12-15DOI: 10.1109/CLOUDCOM.2014.78
Chonho Lee, Liu Yi, Li Tan, Weihan Goh, Bu-Sung Lee, C. Yeo
This paper investigates network traffic before and after a vulnerability called Heart bleed becomes a public issue around March to May, 2014. To detect anomalies and potential threats due to the vulnerability, a wavelet entropy-based change-point detection method is proposed and compared with three other methods: prediction-based, clustering-based and Fourier transform-based. We show that the proposed wavelet entropy-based method outperforms the others in terms of ease of parameter setting, false alarm and detection accuracy. Using the proposed method and a visualization tool, we have studied Heart bleed vulnerability and successfully captured changes in packet volume and flow.
{"title":"A Wavelet Entropy-Based Change Point Detection on Network Traffic: A Case Study of Heartbleed Vulnerability","authors":"Chonho Lee, Liu Yi, Li Tan, Weihan Goh, Bu-Sung Lee, C. Yeo","doi":"10.1109/CLOUDCOM.2014.78","DOIUrl":"https://doi.org/10.1109/CLOUDCOM.2014.78","url":null,"abstract":"This paper investigates network traffic before and after a vulnerability called Heart bleed becomes a public issue around March to May, 2014. To detect anomalies and potential threats due to the vulnerability, a wavelet entropy-based change-point detection method is proposed and compared with three other methods: prediction-based, clustering-based and Fourier transform-based. We show that the proposed wavelet entropy-based method outperforms the others in terms of ease of parameter setting, false alarm and detection accuracy. Using the proposed method and a visualization tool, we have studied Heart bleed vulnerability and successfully captured changes in packet volume and flow.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126318147","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.95
Nouf Alkhater, G. Wills, R. Walters
Cloud computing is paradigm that has emerged to deliver IT services to consumers as a utility service over the Internet. In developing countries, particularly Saudi Arabia, cloud computing is still not widely adopted. As a result, this study seeks to investigate the most influential factors that can encourage an organisation to use the cloud or which might impede them from moving to it. This research proposes an integrated model that incorporates aspects of the Technology-Organisation-Environment (TOE) framework and integrates the critical factors from existing theories along with other factors to examine the impact of this variable on the adoption decision of enterprises. Future work will be focused on confirming the proposed model.
{"title":"Factors Influencing an Organisation's Intention to Adopt Cloud Computing in Saudi Arabia","authors":"Nouf Alkhater, G. Wills, R. Walters","doi":"10.1109/CloudCom.2014.95","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.95","url":null,"abstract":"Cloud computing is paradigm that has emerged to deliver IT services to consumers as a utility service over the Internet. In developing countries, particularly Saudi Arabia, cloud computing is still not widely adopted. As a result, this study seeks to investigate the most influential factors that can encourage an organisation to use the cloud or which might impede them from moving to it. This research proposes an integrated model that incorporates aspects of the Technology-Organisation-Environment (TOE) framework and integrates the critical factors from existing theories along with other factors to examine the impact of this variable on the adoption decision of enterprises. Future work will be focused on confirming the proposed model.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"371 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122346275","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.129
Amelie Chi Zhou, Bingsheng He
Resource provisioning is an important and complicated problem for scientific workflows in Infrastructure-as-a-service (IaaS) clouds. Scientists are facing the complexities resulting from the diverse cloud offerings, complex workflow structures and characteristics as well as various user requirements on budget and performance. In this paper, we review the related work on the cost-aware optimizations of workflows in IaaS clouds and summarize the underlying research issues. Existing studies are not effective enough on finding good solutions to workflow optimization problems due to the complexity of workflows and the cloud dynamics. The heuristics proposed in the existing work are specifically designed for certain applications or certain budget and performance requirements. To address those issues, we propose a flexible and effective optimization system to simplify the resource provisioning for scientific workflows in IaaS clouds. The system adopts a probabilistic QoS notion to obtain good optimization results in the dynamic cloud environment and a cloud- and workflow-specific declarative language to specify various workflow optimization problems. We summarize our ongoing work and present some preliminary results on real-world scientific workflows. The experimental results demonstrate the effectiveness of our system on monetary cost optimizations and its capability to solve a wide class of optimization problems for scientific workflows.
{"title":"Simplified Resource Provisioning for Workflows in IaaS Clouds","authors":"Amelie Chi Zhou, Bingsheng He","doi":"10.1109/CloudCom.2014.129","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.129","url":null,"abstract":"Resource provisioning is an important and complicated problem for scientific workflows in Infrastructure-as-a-service (IaaS) clouds. Scientists are facing the complexities resulting from the diverse cloud offerings, complex workflow structures and characteristics as well as various user requirements on budget and performance. In this paper, we review the related work on the cost-aware optimizations of workflows in IaaS clouds and summarize the underlying research issues. Existing studies are not effective enough on finding good solutions to workflow optimization problems due to the complexity of workflows and the cloud dynamics. The heuristics proposed in the existing work are specifically designed for certain applications or certain budget and performance requirements. To address those issues, we propose a flexible and effective optimization system to simplify the resource provisioning for scientific workflows in IaaS clouds. The system adopts a probabilistic QoS notion to obtain good optimization results in the dynamic cloud environment and a cloud- and workflow-specific declarative language to specify various workflow optimization problems. We summarize our ongoing work and present some preliminary results on real-world scientific workflows. The experimental results demonstrate the effectiveness of our system on monetary cost optimizations and its capability to solve a wide class of optimization problems for scientific workflows.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116112279","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.31
Craig A. Lee, N. Desai, Andrew Brethorst
As distributed, on-line communities are increasingly supported by the global, interconnected computing infrastructure, methods must be developed to securely manage their interactions. The virtual organization (VO) concept provides a security and discovery context whereby collaboration across multiple administrative domains can be enabled while enforcing joint security policies. In the era of cloud computing, VOs can be used to manage "community clouds", i.e., Cloud federations. In this paper, we describe a method for re-purposing the Open Stack Keystone service to act as a VO Management System (VOMS) called Key VOMS. With minor changes, it can be used to manage access to services that are registered for use by members of any given VO. These services can be arbitrary infrastructure-level or application-level services. This is illustrated by using Key VOMS to manage access to a set of RSS feed topics. While very flexible, the use of an external, third-party, such as Key VOMS, raises fundamental semantic interoperability and trust delegation issues that must be addressed in future work.
{"title":"A Keystone-Based Virtual Organization Management System","authors":"Craig A. Lee, N. Desai, Andrew Brethorst","doi":"10.1109/CloudCom.2014.31","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.31","url":null,"abstract":"As distributed, on-line communities are increasingly supported by the global, interconnected computing infrastructure, methods must be developed to securely manage their interactions. The virtual organization (VO) concept provides a security and discovery context whereby collaboration across multiple administrative domains can be enabled while enforcing joint security policies. In the era of cloud computing, VOs can be used to manage \"community clouds\", i.e., Cloud federations. In this paper, we describe a method for re-purposing the Open Stack Keystone service to act as a VO Management System (VOMS) called Key VOMS. With minor changes, it can be used to manage access to services that are registered for use by members of any given VO. These services can be arbitrary infrastructure-level or application-level services. This is illustrated by using Key VOMS to manage access to a set of RSS feed topics. While very flexible, the use of an external, third-party, such as Key VOMS, raises fundamental semantic interoperability and trust delegation issues that must be addressed in future work.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124876006","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.130
N. Hieu, M. D. Francesco, Antti Ylä-Jääski
Resources used in a cloud data center could be spread across a large number of servers that are not fully utilized. This situation results in significant operational costs which are directly related to the power consumption of active servers. Virtual machine migration enables reducing the number of active servers by consolidating the load on a limited amount of nodes. Several schemes have actually been proposed to consolidate virtual machines on the minimum number of physical servers in order to reduce power consumption. However, most of the existing solutions only consider a limited trade off among multiple types of resources, thus resulting in unnecessarily activated physical servers. This article proposes a multi-resource selection (MRS) scheme for consolidating virtual machines in cloud data centers. With MRS, each physical server is first characterized in terms of multiple types of resources and then classified through its overall resource utilization. Based on the MRS scheme, a balanced multiple-resource utilization algorithm is also used to spread the load across different types of resources while consolidating virtual machines. The proposed solution is evaluated through simulations on both synthetic and real-world workloads. Experimental results show that the proposed approach outperforms several existing schemes in terms of the number of active physical servers and the utilization of multiple resources.
{"title":"A Multi-resource Selection Scheme for Virtual Machine Consolidation in Cloud Data Centers","authors":"N. Hieu, M. D. Francesco, Antti Ylä-Jääski","doi":"10.1109/CloudCom.2014.130","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.130","url":null,"abstract":"Resources used in a cloud data center could be spread across a large number of servers that are not fully utilized. This situation results in significant operational costs which are directly related to the power consumption of active servers. Virtual machine migration enables reducing the number of active servers by consolidating the load on a limited amount of nodes. Several schemes have actually been proposed to consolidate virtual machines on the minimum number of physical servers in order to reduce power consumption. However, most of the existing solutions only consider a limited trade off among multiple types of resources, thus resulting in unnecessarily activated physical servers. This article proposes a multi-resource selection (MRS) scheme for consolidating virtual machines in cloud data centers. With MRS, each physical server is first characterized in terms of multiple types of resources and then classified through its overall resource utilization. Based on the MRS scheme, a balanced multiple-resource utilization algorithm is also used to spread the load across different types of resources while consolidating virtual machines. The proposed solution is evaluated through simulations on both synthetic and real-world workloads. Experimental results show that the proposed approach outperforms several existing schemes in terms of the number of active physical servers and the utilization of multiple resources.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130126186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The feasibility of the cloud computing paradigm is examined from the High Performance Computing (HPC) viewpoint. The impact of virtualization is evaluated on our latest private cloud, the AIST Super Green Cloud, which provides elastic virtual clusters interconnected by Infini Band. Performance is measured by using typical HPC benchmark programs, both on physical and virtual cluster computing clusters. The results of the micro benchmarks indicate that the virtual clusters suffer from the scalability issue on almost all MPI collective functions. The relative performance gradually becomes worse as the number of nodes increases. On the other hand, the benchmarks based on actual applications, including LINPACK, OpenMX, and Graph 500, show that the virtualization overhead is about 5% even when the number of nodes increase to 128. This observation leads to our optimistic conclusions on the feasibility of the HPC Cloud.
{"title":"Exploring the Performance Impact of Virtualization on an HPC Cloud","authors":"Nuttapong Chakthranont, Phonlawat Khunphet, Ryousei Takano, Tsutomu Ikegami","doi":"10.1109/CloudCom.2014.71","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.71","url":null,"abstract":"The feasibility of the cloud computing paradigm is examined from the High Performance Computing (HPC) viewpoint. The impact of virtualization is evaluated on our latest private cloud, the AIST Super Green Cloud, which provides elastic virtual clusters interconnected by Infini Band. Performance is measured by using typical HPC benchmark programs, both on physical and virtual cluster computing clusters. The results of the micro benchmarks indicate that the virtual clusters suffer from the scalability issue on almost all MPI collective functions. The relative performance gradually becomes worse as the number of nodes increases. On the other hand, the benchmarks based on actual applications, including LINPACK, OpenMX, and Graph 500, show that the virtualization overhead is about 5% even when the number of nodes increase to 128. This observation leads to our optimistic conclusions on the feasibility of the HPC Cloud.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"10 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114020718","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 : 2014-12-15DOI: 10.1109/CLOUDCOM.2014.96
L. Mossucca, I. Zinno, S. Elefante, C. Luca, V. Casola, O. Terzo, F. Casu, R. Lanari
The advanced Differential SAR Interferometers (DInSAR) methodologies are widely used for the investigation of Earth's surface deformation phenomena. In particular, the advanced DInSAR approach referred to as Small Baseline Subset (SBAS) technique is able to produce deformation velocity maps and the corresponding displacement time-series from a temporal sequence of space borne SAR acquisitions. Considering the already huge SAR data archives as well the upcoming massive data flow coming from the SENTINEL satellite constellation, cloud computing can be a valid solution to carry out DInSAR analyses thanks to its scalability and flexibility features. In this paper, the focus is given on the migration of the whole parallel version of the SBAS technique, namely P-SBAS, to a cloud environment by taking into account different parameters that influence processing time. Experimental tests that have been performed using both private and public cloud are also presented.
{"title":"Cloud Platform for Scientific Advances in Earth Surface Interferometric SAR Image Analysis","authors":"L. Mossucca, I. Zinno, S. Elefante, C. Luca, V. Casola, O. Terzo, F. Casu, R. Lanari","doi":"10.1109/CLOUDCOM.2014.96","DOIUrl":"https://doi.org/10.1109/CLOUDCOM.2014.96","url":null,"abstract":"The advanced Differential SAR Interferometers (DInSAR) methodologies are widely used for the investigation of Earth's surface deformation phenomena. In particular, the advanced DInSAR approach referred to as Small Baseline Subset (SBAS) technique is able to produce deformation velocity maps and the corresponding displacement time-series from a temporal sequence of space borne SAR acquisitions. Considering the already huge SAR data archives as well the upcoming massive data flow coming from the SENTINEL satellite constellation, cloud computing can be a valid solution to carry out DInSAR analyses thanks to its scalability and flexibility features. In this paper, the focus is given on the migration of the whole parallel version of the SBAS technique, namely P-SBAS, to a cloud environment by taking into account different parameters that influence processing time. Experimental tests that have been performed using both private and public cloud are also presented.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122533652","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.170
K. Kritikos, Jörg Domaschka, A. Rossini
The benefits of cloud computing have led to a proliferation of infrastructures and platforms covering the provisioning and deployment requirements of many cloud-based applications. However, the requirements of an application may change during its life cycle. Therefore, its provisioning and deployment should be adapted so that the application can deliver its target quality of service throughout its entire life cycle. Existing solutions typically support only simple adaptation scenarios, whereby scalability rules map conditions on fixed metrics to a single scaling action targeting a single cloud environment (e.g., Scale out an application component). However, these solutions fail to support complex adaptation scenarios, whereby scalability rules could map conditions on custom metrics to multiple scaling actions targeting multi-cloud environments. In this paper, we propose the Scalability Rule Language (SRL), a language for specifying scalability rules that support such complex adaptation scenarios of multi-cloud applications. SRL provides Eclipse-based tool support, thus allowing modellers not only to specify scalability rules but also to syntactically and semantically validate them. Moreover, SRL is well integrated with the Cloud Modelling Language (Cloud ML), thus allowing modellers to associate their scalability rules with the components and virtual machines of provisioning and deployment models.
{"title":"SRL: A Scalability Rule Language for Multi-cloud Environments","authors":"K. Kritikos, Jörg Domaschka, A. Rossini","doi":"10.1109/CloudCom.2014.170","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.170","url":null,"abstract":"The benefits of cloud computing have led to a proliferation of infrastructures and platforms covering the provisioning and deployment requirements of many cloud-based applications. However, the requirements of an application may change during its life cycle. Therefore, its provisioning and deployment should be adapted so that the application can deliver its target quality of service throughout its entire life cycle. Existing solutions typically support only simple adaptation scenarios, whereby scalability rules map conditions on fixed metrics to a single scaling action targeting a single cloud environment (e.g., Scale out an application component). However, these solutions fail to support complex adaptation scenarios, whereby scalability rules could map conditions on custom metrics to multiple scaling actions targeting multi-cloud environments. In this paper, we propose the Scalability Rule Language (SRL), a language for specifying scalability rules that support such complex adaptation scenarios of multi-cloud applications. SRL provides Eclipse-based tool support, thus allowing modellers not only to specify scalability rules but also to syntactically and semantically validate them. Moreover, SRL is well integrated with the Cloud Modelling Language (Cloud ML), thus allowing modellers to associate their scalability rules with the components and virtual machines of provisioning and deployment models.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133290354","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.42
Soramichi Akiyama, Takahiro Hirofuchi, S. Honiden
Energy efficiency of cloud data centers is of great concern today and has been tackled by many researchers. Dynamic VM placement is a well-known strategy to improve energy efficiency of a data center. Virtual machines (VMs) under light load are consolidated into a small number of physical machines (PMs) to turn idle PMs into low-power states. Although live migration is essential for dynamic VM placement, former studies have not yet revealed how energy overhead of live migration has impact on energy efficiency of dynamic VM placement. To tackle this problem, we conducted integrated simulation of energy overhead of live migration and dynamic VM placement sing Sim Grid. We used three dynamic VM placement policies and two live migration mechanisms (existing pre-copy and an accelerated mechanism invented by us) to thoroughly evaluate the energy overhead. The results showed that in the worst case energy overhead of live migration occupies 5.8% of total energy consumption of a data center.
{"title":"Evaluating Impact of Live Migration on Data Center Energy Saving","authors":"Soramichi Akiyama, Takahiro Hirofuchi, S. Honiden","doi":"10.1109/CloudCom.2014.42","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.42","url":null,"abstract":"Energy efficiency of cloud data centers is of great concern today and has been tackled by many researchers. Dynamic VM placement is a well-known strategy to improve energy efficiency of a data center. Virtual machines (VMs) under light load are consolidated into a small number of physical machines (PMs) to turn idle PMs into low-power states. Although live migration is essential for dynamic VM placement, former studies have not yet revealed how energy overhead of live migration has impact on energy efficiency of dynamic VM placement. To tackle this problem, we conducted integrated simulation of energy overhead of live migration and dynamic VM placement sing Sim Grid. We used three dynamic VM placement policies and two live migration mechanisms (existing pre-copy and an accelerated mechanism invented by us) to thoroughly evaluate the energy overhead. The results showed that in the worst case energy overhead of live migration occupies 5.8% of total energy consumption of a data center.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133325438","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.69
Zhaoxia Wang, Victor Joo Chuan Tong, Xin Xin, H. Chin
Anomaly detection in sentiment analysis refers to detecting abnormal opinions, sentiment patterns or special temporal aspects of such patterns in a collection of data. The anomalies detected may be due to sudden sentiment changes hidden in large amounts of text. If these anomalies are undetected or poorly managed, the consequences may be severe, e.g. A business whose customers reveal negative sentiments and will no longer support the establishment. Social media platforms, such as Twitter, provide a vast source of information, which includes user feedback, opinion and information on most issues. Many organizations also leverage social media platforms to publish information about events, products, services, policies and other topics frequently. Thus, analyzing social media data to identify abnormal events in a timely manner is a beneficial topic. It will enable the businesses and government organizations to intervene early or adopt proper strategies if needed. However, it is also a challenge due to the diversity and size of social media data. In this study, we survey existing anomaly analysis as well as sentiment analysis methods and analyze their limitations and challenges. To tackle the challenges, an enhanced sentiment classification method is proposed and discussed. We study the possibility of employing the proposed method to perform anomaly detection through sentiment analysis on social media data. We tested the applicability and robustness of the method through sentiment analysis on tweet data. The results demonstrate the capabilities of the proposed method and provide meaningful insights into this research area.
{"title":"Anomaly Detection through Enhanced Sentiment Analysis on Social Media Data","authors":"Zhaoxia Wang, Victor Joo Chuan Tong, Xin Xin, H. Chin","doi":"10.1109/CloudCom.2014.69","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.69","url":null,"abstract":"Anomaly detection in sentiment analysis refers to detecting abnormal opinions, sentiment patterns or special temporal aspects of such patterns in a collection of data. The anomalies detected may be due to sudden sentiment changes hidden in large amounts of text. If these anomalies are undetected or poorly managed, the consequences may be severe, e.g. A business whose customers reveal negative sentiments and will no longer support the establishment. Social media platforms, such as Twitter, provide a vast source of information, which includes user feedback, opinion and information on most issues. Many organizations also leverage social media platforms to publish information about events, products, services, policies and other topics frequently. Thus, analyzing social media data to identify abnormal events in a timely manner is a beneficial topic. It will enable the businesses and government organizations to intervene early or adopt proper strategies if needed. However, it is also a challenge due to the diversity and size of social media data. In this study, we survey existing anomaly analysis as well as sentiment analysis methods and analyze their limitations and challenges. To tackle the challenges, an enhanced sentiment classification method is proposed and discussed. We study the possibility of employing the proposed method to perform anomaly detection through sentiment analysis on social media data. We tested the applicability and robustness of the method through sentiment analysis on tweet data. The results demonstrate the capabilities of the proposed method and provide meaningful insights into this research area.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116993690","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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