Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.116
Márcia Baptista, Hugo Damas, Joana Dimas, Guilherme Raimundo, R. Prada, C. Martinho, P. A. Santos, Jorge F. Peña
It has been pointed that public goods games lack experimental research in topics such as the study of the interaction of groups versus the interaction of individuals and effects of social identity and decision framing. Furthermore, the number of computational frameworks proposed to date to deploy these type of experiments is reduced. In this context, we propose the INVITE computational framework to serve as a useful research tool. The motivation behind the proposed framework is therefore straightforward: to allow researchers to be able to configure without difficulty public goods experiments where they can test their hypothesis regarding the behaviour of individuals, simulate behaviour of automated artificial intelligence and study the interaction between virtual agents and real persons. The greatest advantage of the INVITE framework is that it provides a high level of flexibility in the configuration of game theoretical paradigms. It is possible to configure simple structures such as the 2-player prisoner's dilemma or stag hunt as well as some of the most complex forms of inter-group conflicts such as team games. Accordingly, this framework allows an effortless parametrization, assisted by a tailored configuration tool, of a myriad of public goods games. Moreover, the 3D video game configured in the framework places the players in an immersive and engaging virtual environment where real-life conditions can be replicated and some circumstances, difficult to reproduce in real-life, such as life-threatening situations, can also be simulated. Given its characteristics, the proposed framework aims to represent an important contribution to the study of behaviour of both virtual and human players in scenarios of social conflict.
{"title":"A Serious Game Based on a Public Goods Experiment","authors":"Márcia Baptista, Hugo Damas, Joana Dimas, Guilherme Raimundo, R. Prada, C. Martinho, P. A. Santos, Jorge F. Peña","doi":"10.1109/SocialCom.2013.116","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.116","url":null,"abstract":"It has been pointed that public goods games lack experimental research in topics such as the study of the interaction of groups versus the interaction of individuals and effects of social identity and decision framing. Furthermore, the number of computational frameworks proposed to date to deploy these type of experiments is reduced. In this context, we propose the INVITE computational framework to serve as a useful research tool. The motivation behind the proposed framework is therefore straightforward: to allow researchers to be able to configure without difficulty public goods experiments where they can test their hypothesis regarding the behaviour of individuals, simulate behaviour of automated artificial intelligence and study the interaction between virtual agents and real persons. The greatest advantage of the INVITE framework is that it provides a high level of flexibility in the configuration of game theoretical paradigms. It is possible to configure simple structures such as the 2-player prisoner's dilemma or stag hunt as well as some of the most complex forms of inter-group conflicts such as team games. Accordingly, this framework allows an effortless parametrization, assisted by a tailored configuration tool, of a myriad of public goods games. Moreover, the 3D video game configured in the framework places the players in an immersive and engaging virtual environment where real-life conditions can be replicated and some circumstances, difficult to reproduce in real-life, such as life-threatening situations, can also be simulated. Given its characteristics, the proposed framework aims to represent an important contribution to the study of behaviour of both virtual and human players in scenarios of social conflict.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830599","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.49
Claudia Wagner, S. Asur, J. Hailpern
Finding the ''right people'' is a central aspect of social media systems. Twitter has millions of users who have varied interests, professions and personalities. For those in fields such as advertising and marketing, it is important to identify certain characteristics of users to target. However, Twitter users do not generally provide sufficient information about themselves on their profile which makes this task difficult. In response, this work sets out to automatically infer professions (e.g., musicians, health sector workers, technicians) and personality related attributes (e.g., creative, innovative, funny) for Twitter users based on features extracted from their content, their interaction networks, attributes of their friends and their activity patterns. We develop a comprehensive set of latent features that are then employed to perform efficient classification of users along these two dimensions (profession and personality). Our experiments on a large sample of Twitter users demonstrate both a high overall accuracy in detecting profession and personality related attributes as well as highlighting the benefits and pitfalls of various types of features for particular categories of users.
{"title":"Religious Politicians and Creative Photographers: Automatic User Categorization in Twitter","authors":"Claudia Wagner, S. Asur, J. Hailpern","doi":"10.1109/SocialCom.2013.49","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.49","url":null,"abstract":"Finding the ''right people'' is a central aspect of social media systems. Twitter has millions of users who have varied interests, professions and personalities. For those in fields such as advertising and marketing, it is important to identify certain characteristics of users to target. However, Twitter users do not generally provide sufficient information about themselves on their profile which makes this task difficult. In response, this work sets out to automatically infer professions (e.g., musicians, health sector workers, technicians) and personality related attributes (e.g., creative, innovative, funny) for Twitter users based on features extracted from their content, their interaction networks, attributes of their friends and their activity patterns. We develop a comprehensive set of latent features that are then employed to perform efficient classification of users along these two dimensions (profession and personality). Our experiments on a large sample of Twitter users demonstrate both a high overall accuracy in detecting profession and personality related attributes as well as highlighting the benefits and pitfalls of various types of features for particular categories of users.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916608","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.81
Jehwan Oh, Z. Borbora, Dhruv Sharma, J. Srivastava
The objective of this work is to detect the use of automated programs, known as game bots, based on social interactions in MMORPGs. Online games, especially MMORPGs, have become extremely popular among internet users in the recent years. Not only the popularity but also security threats such as the use of game bots and identity theft have grown manifold. As bot players can obtain unjustified assets without corresponding efforts, the gaming community does not allow players to use game bots. However, the task of identifying game bots is not an easy one because of the velocity and variety of their evolution in mimicking human behavior. Existing methods for detecting game bots have a few drawbacks like reducing immersion of players, low detection accuracy rate, and collision with other security programs. We propose a novel method for detecting game bots based on the fact that humans and game bots tend to form their social network in contrasting ways. In this work we focus particularly on the in game mentoring network from amongst several social networks. We construct a couple of new features based on eigenvector centrality to capture this intuition and establish their importance for detecting game bots. The results show a significant increase in the classification accuracy of various classifiers with the introduction of these features.
{"title":"Bot Detection Based on Social Interactions in MMORPGs","authors":"Jehwan Oh, Z. Borbora, Dhruv Sharma, J. Srivastava","doi":"10.1109/SocialCom.2013.81","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.81","url":null,"abstract":"The objective of this work is to detect the use of automated programs, known as game bots, based on social interactions in MMORPGs. Online games, especially MMORPGs, have become extremely popular among internet users in the recent years. Not only the popularity but also security threats such as the use of game bots and identity theft have grown manifold. As bot players can obtain unjustified assets without corresponding efforts, the gaming community does not allow players to use game bots. However, the task of identifying game bots is not an easy one because of the velocity and variety of their evolution in mimicking human behavior. Existing methods for detecting game bots have a few drawbacks like reducing immersion of players, low detection accuracy rate, and collision with other security programs. We propose a novel method for detecting game bots based on the fact that humans and game bots tend to form their social network in contrasting ways. In this work we focus particularly on the in game mentoring network from amongst several social networks. We construct a couple of new features based on eigenvector centrality to capture this intuition and establish their importance for detecting game bots. The results show a significant increase in the classification accuracy of various classifiers with the introduction of these features.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126392523","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.33
V. Singh, Laura Freeman, B. Lepri, A. Pentland
Human spending behavior is essentially social. This work motivates and grounds the use of mobile phone based social interaction features for classifying spending behavior. Using a data set involving 52 adults (26 couples) living in a community for over a year, we find that social behavior measured via face-to-face interaction, call, and SMS logs, can be used to predict the spending behavior for couples in terms of their propensity to explore diverse businesses, become loyal customers, and overspend. Our results show that mobile phone based social interaction patterns can provide more predictive power on spending behavior than often-used personality based features. Obtaining novel insights on spending behavior using social-computing frameworks can be of vital importance to economists, marketing professionals, and policy makers.
{"title":"Predicting Spending Behavior Using Socio-mobile Features","authors":"V. Singh, Laura Freeman, B. Lepri, A. Pentland","doi":"10.1109/SocialCom.2013.33","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.33","url":null,"abstract":"Human spending behavior is essentially social. This work motivates and grounds the use of mobile phone based social interaction features for classifying spending behavior. Using a data set involving 52 adults (26 couples) living in a community for over a year, we find that social behavior measured via face-to-face interaction, call, and SMS logs, can be used to predict the spending behavior for couples in terms of their propensity to explore diverse businesses, become loyal customers, and overspend. Our results show that mobile phone based social interaction patterns can provide more predictive power on spending behavior than often-used personality based features. Obtaining novel insights on spending behavior using social-computing frameworks can be of vital importance to economists, marketing professionals, and policy makers.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126559967","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 : 2013-09-08DOI: 10.1109/SOCIALCOM.2013.37
Konstantin Kuzmin, S. Y. Shah, B. Szymanski
Social networks consist of various communities that host members sharing common characteristics. Often some members of one community are also members of other communities. Such shared membership of different communities leads to overlapping communities. Detecting such overlapping communities is a challenging and computationally intensive problem. In this paper, we investigate the usability of high performance computing in the area of social networks and community detection. We present highly scalable variants of a community detection algorithm called Speaker-listener Label Propagation Algorithm (SLPA). We show that despite of irregular data dependencies in the computation, parallel computing paradigms can significantly speed up the detection of overlapping communities of social networks which is computationally expensive. We show by experiments, how various parallel computing architectures can be utilized to analyze large social network data on both shared memory machines and distributed memory machines, such as IBM Blue Gene.
社交网络由各种各样的社区组成，这些社区的成员拥有共同的特征。通常一个社区的一些成员也是其他社区的成员。不同社区的这种共享成员导致了重叠的社区。检测这样的重叠社区是一个具有挑战性和计算密集型的问题。在本文中，我们研究了高性能计算在社交网络和社区检测领域的可用性。我们提出了一个社区检测算法的高度可扩展的变体，称为扬声器-侦听器标签传播算法(SLPA)。我们表明，尽管在计算中存在不规则的数据依赖关系，但并行计算范式可以显着加快对计算昂贵的社交网络重叠社区的检测。我们通过实验展示了如何利用各种并行计算架构来分析共享内存机器和分布式内存机器(如IBM Blue Gene)上的大型社交网络数据。
{"title":"Parallel Overlapping Community Detection with SLPA","authors":"Konstantin Kuzmin, S. Y. Shah, B. Szymanski","doi":"10.1109/SOCIALCOM.2013.37","DOIUrl":"https://doi.org/10.1109/SOCIALCOM.2013.37","url":null,"abstract":"Social networks consist of various communities that host members sharing common characteristics. Often some members of one community are also members of other communities. Such shared membership of different communities leads to overlapping communities. Detecting such overlapping communities is a challenging and computationally intensive problem. In this paper, we investigate the usability of high performance computing in the area of social networks and community detection. We present highly scalable variants of a community detection algorithm called Speaker-listener Label Propagation Algorithm (SLPA). We show that despite of irregular data dependencies in the computation, parallel computing paradigms can significantly speed up the detection of overlapping communities of social networks which is computationally expensive. We show by experiments, how various parallel computing architectures can be utilized to analyze large social network data on both shared memory machines and distributed memory machines, such as IBM Blue Gene.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126020526","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.80
Eli Omernick, S. Sood
The scale of participation on social news sites has challenged community managers, leaving them unable to detect and remove all inappropriate content by hand. Automated insult and profanity detection systems have helped, but have failed to address the problem of why this content is contributed in the first place. That is, what implications do interface design choices have on the content being generated? One such design choice is whether or not a site allows anonymous comments. What impact does allowing anonymity have on the quality or quantity of participation on a site? This case study analyses the impact of anonymity on a technology social news site, TechCrunch.com. TechCrunch is ideal for this study in that it underwent a shift from allowing anonymous comments (using the Disqus commenting platform) to disallowing them (using the Facebook commenting platform) in March of 2011. We compare the quality of anonymous and real identity comments through measures of reading level, relevance to the target article, negativity and presence of swear words and anger words. We couple this qualitative analysis with a quantitative analysis of the change in participation to give a complete picture of the impact of anonymity in this online community, with the end goal of informing design on similar social news sites.
{"title":"The Impact of Anonymity in Online Communities","authors":"Eli Omernick, S. Sood","doi":"10.1109/SocialCom.2013.80","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.80","url":null,"abstract":"The scale of participation on social news sites has challenged community managers, leaving them unable to detect and remove all inappropriate content by hand. Automated insult and profanity detection systems have helped, but have failed to address the problem of why this content is contributed in the first place. That is, what implications do interface design choices have on the content being generated? One such design choice is whether or not a site allows anonymous comments. What impact does allowing anonymity have on the quality or quantity of participation on a site? This case study analyses the impact of anonymity on a technology social news site, TechCrunch.com. TechCrunch is ideal for this study in that it underwent a shift from allowing anonymous comments (using the Disqus commenting platform) to disallowing them (using the Facebook commenting platform) in March of 2011. We compare the quality of anonymous and real identity comments through measures of reading level, relevance to the target article, negativity and presence of swear words and anger words. We couple this qualitative analysis with a quantitative analysis of the change in participation to give a complete picture of the impact of anonymity in this online community, with the end goal of informing design on similar social news sites.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"PC-28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126678201","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.27
Noseong Park, Michael Ovelgönne, V. S. Subrahmanian
Classical centrality measures like betweenness, closeness, eigenvector, and degree centrality are application and user independent. They are also independent of graph semantics. However, in many applications, users have a clear idea of who they consider important in graphs where vertices and edges have properties, and the goal of this paper is to enable them to bring their knowledge to the table in defining centrality in graphs. We propose a novel combination of sub graph matching queries which have been studied extensively in the context of both RDF and social networks, and scoring functions. The resulting SMAC framework allows a user to define what he thinks are central vertices in a network via user-defined sub graph patterns and certain mathematical measures he specifies. We formally define SMAC queries and develop algorithms to compute answers to such queries. We test our algorithms on real-world data sets from CiteSeerX, Flickr, YouTube, and IMDb containing over 6M vertices and 15M edges and show that our algorithms work well in practice.
{"title":"SMAC: Subgraph Matching and Centrality in Huge Social Networks","authors":"Noseong Park, Michael Ovelgönne, V. S. Subrahmanian","doi":"10.1109/SocialCom.2013.27","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.27","url":null,"abstract":"Classical centrality measures like betweenness, closeness, eigenvector, and degree centrality are application and user independent. They are also independent of graph semantics. However, in many applications, users have a clear idea of who they consider important in graphs where vertices and edges have properties, and the goal of this paper is to enable them to bring their knowledge to the table in defining centrality in graphs. We propose a novel combination of sub graph matching queries which have been studied extensively in the context of both RDF and social networks, and scoring functions. The resulting SMAC framework allows a user to define what he thinks are central vertices in a network via user-defined sub graph patterns and certain mathematical measures he specifies. We formally define SMAC queries and develop algorithms to compute answers to such queries. We test our algorithms on real-world data sets from CiteSeerX, Flickr, YouTube, and IMDb containing over 6M vertices and 15M edges and show that our algorithms work well in practice.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116574867","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.131
M. S. Uddin, Piraveenan Mahendra, Arif Khan, Babak Amiri
A longitudinal social network evolves over time through the creation and/or deletion of links among a set of actors (e.g. individuals or organisations). Longitudinal social networks are studied by network science and social science researchers to understand network evolution, trend propagation, friendship and belief formation, diffusion of innovations, the spread of deviant behaviour and more. In the current literature, there are different approaches and methods (e.g. Sampson's approach and the Markov model) to study the dynamics of longitudinal social networks. These approaches and methods have mainly been utilised to explore evolutionary changes of longitudinal social networks from one state to another and to explain the underlying reasons for these changes. However, they cannot quantify the level of dynamicity of the over time network changes and the contribution of individual network members (i.e. actors) to these changes. In this study, we first develop a set of measures to quantify different aspects of the dynamicity of a longitudinal social network. We then apply these measures, in order to conduct empirical investigations, to two different longitudinal social networks. Finally, we discuss the implications of the application of these measures and possible future research directions of this study.
{"title":"Conceptual Quantification of the Dynamicity of Longitudinal Social Networks","authors":"M. S. Uddin, Piraveenan Mahendra, Arif Khan, Babak Amiri","doi":"10.1109/SocialCom.2013.131","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.131","url":null,"abstract":"A longitudinal social network evolves over time through the creation and/or deletion of links among a set of actors (e.g. individuals or organisations). Longitudinal social networks are studied by network science and social science researchers to understand network evolution, trend propagation, friendship and belief formation, diffusion of innovations, the spread of deviant behaviour and more. In the current literature, there are different approaches and methods (e.g. Sampson's approach and the Markov model) to study the dynamics of longitudinal social networks. These approaches and methods have mainly been utilised to explore evolutionary changes of longitudinal social networks from one state to another and to explain the underlying reasons for these changes. However, they cannot quantify the level of dynamicity of the over time network changes and the contribution of individual network members (i.e. actors) to these changes. In this study, we first develop a set of measures to quantify different aspects of the dynamicity of a longitudinal social network. We then apply these measures, in order to conduct empirical investigations, to two different longitudinal social networks. Finally, we discuss the implications of the application of these measures and possible future research directions of this study.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125262680","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.109
Salim Jouili, Aldemar Reynaga
Diverse applications including cyber security, social networks, protein networks, recommendation systems or citation networks work with inherently graph-structured data. The graphs modeling the data of these applications are large by nature so the efficient processing of them becomes challenging. In this paper we present imGraph, a graph system that addresses the challenge of efficient processing of large graphs by using a distributed in-memory storage. We use this type of storage to obtain fast random data access which is mostly required for graph exploration. imGraph uses a native graph data model to ease the implementation of graph algorithms. On top of it, we design and implement a traversal engine that achieves high performance by efficient memory access, distribution of the work load, and optimizations on network communications. We run a set of experiments on real graph datasets of different sizes to asses the performance of imGraph in relation to other graph systems. The results show that imGraph gets better performance on traversals on large graphs than its counterparts.
{"title":"imGraph: A Distributed In-Memory Graph Database","authors":"Salim Jouili, Aldemar Reynaga","doi":"10.1109/SocialCom.2013.109","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.109","url":null,"abstract":"Diverse applications including cyber security, social networks, protein networks, recommendation systems or citation networks work with inherently graph-structured data. The graphs modeling the data of these applications are large by nature so the efficient processing of them becomes challenging. In this paper we present imGraph, a graph system that addresses the challenge of efficient processing of large graphs by using a distributed in-memory storage. We use this type of storage to obtain fast random data access which is mostly required for graph exploration. imGraph uses a native graph data model to ease the implementation of graph algorithms. On top of it, we design and implement a traversal engine that achieves high performance by efficient memory access, distribution of the work load, and optimizations on network communications. We run a set of experiments on real graph datasets of different sizes to asses the performance of imGraph in relation to other graph systems. The results show that imGraph gets better performance on traversals on large graphs than its counterparts.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122488878","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 : 2013-09-08DOI: 10.1109/SocialCom.2013.83
M. Asim, D. Llewellyn-Jones, Brett Lempereur, Bo Zhou, Q. Shi, M. Merabti
The Future Internet will be populated by not just data and devices, but also services. Service-Oriented Architecture (SOA) approaches are allowing new ways for users and developers to manage, control and benefit from the services that are being made available. However, SOA-based applications are highly dynamic and liable to change significantly at runtime. This justifies the need for monitoring composed services throughout the lifetime of service execution. In this paper we present a novel framework for monitoring services at runtime and ensuring that services behave as they have promised. The framework enables monitoring of both atomic and composite services and offers high flexibility through the use of Complex Event Processing (CEP). Services are defined as Business Process Modeling Notation (BPMN) processes which can then be monitored during execution. The use of CEP and monitoring points that are inserted automatically between services allows the method to be more flexible and accurate than existing techniques.
{"title":"Event Driven Monitoring of Composite Services","authors":"M. Asim, D. Llewellyn-Jones, Brett Lempereur, Bo Zhou, Q. Shi, M. Merabti","doi":"10.1109/SocialCom.2013.83","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.83","url":null,"abstract":"The Future Internet will be populated by not just data and devices, but also services. Service-Oriented Architecture (SOA) approaches are allowing new ways for users and developers to manage, control and benefit from the services that are being made available. However, SOA-based applications are highly dynamic and liable to change significantly at runtime. This justifies the need for monitoring composed services throughout the lifetime of service execution. In this paper we present a novel framework for monitoring services at runtime and ensuring that services behave as they have promised. The framework enables monitoring of both atomic and composite services and offers high flexibility through the use of Complex Event Processing (CEP). Services are defined as Business Process Modeling Notation (BPMN) processes which can then be monitored during execution. The use of CEP and monitoring points that are inserted automatically between services allows the method to be more flexible and accurate than existing techniques.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131613052","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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