Somayeh Koohborfardhaghighi, J. P. Romero, Sira Maliphol, Yulin Liu, J. Altmann
In this study, we explore the emergence of cooperative behavior in the prisoner's dilemma evolutionary game. In particular, we investigate the effect of bounded rationality of individuals on the networking topology (i.e., the individuals' personal networks). For this, we highlight the evolutionary dynamics of cooperation on top of different graph topologies with respect to their baseline properties such as average shortest path length and clustering coefficient. In addition, we test the effect of a new variable, called memory of interactions, on the changes in behavior and decision-making of the players as well as the networking outcome. For this purpose, we use agent-based modeling, which allows studying how changes in the environment or changes of properties of networked actors affect the evolutionary dynamics of cooperation among them. The results of our analysis confirm that the networking topology and the memory duration are important in affecting the emergence of cooperative behavior of players. They also impact the total utility that can be obtained from playing the Prisoner's Dilemma evolutionary game. Although the Prisoner's Dilemma game simulations tend towards full cooperation, if they are run over graph topologies with short average shortest path lengths and low clustering coefficients, the number of steps needed to reach equilibrium increases. This new result provides an understanding of the interactions of actors in a game.
{"title":"How bounded rationality of individuals in social interactions impacts evolutionary dynamics of cooperation","authors":"Somayeh Koohborfardhaghighi, J. P. Romero, Sira Maliphol, Yulin Liu, J. Altmann","doi":"10.1145/3106426.3106511","DOIUrl":"https://doi.org/10.1145/3106426.3106511","url":null,"abstract":"In this study, we explore the emergence of cooperative behavior in the prisoner's dilemma evolutionary game. In particular, we investigate the effect of bounded rationality of individuals on the networking topology (i.e., the individuals' personal networks). For this, we highlight the evolutionary dynamics of cooperation on top of different graph topologies with respect to their baseline properties such as average shortest path length and clustering coefficient. In addition, we test the effect of a new variable, called memory of interactions, on the changes in behavior and decision-making of the players as well as the networking outcome. For this purpose, we use agent-based modeling, which allows studying how changes in the environment or changes of properties of networked actors affect the evolutionary dynamics of cooperation among them. The results of our analysis confirm that the networking topology and the memory duration are important in affecting the emergence of cooperative behavior of players. They also impact the total utility that can be obtained from playing the Prisoner's Dilemma evolutionary game. Although the Prisoner's Dilemma game simulations tend towards full cooperation, if they are run over graph topologies with short average shortest path lengths and low clustering coefficients, the number of steps needed to reach equilibrium increases. This new result provides an understanding of the interactions of actors in a game.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89964637","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}
P-Rank is a simple and captivating link-based similarity measure that extends SimRank by exploiting both in- and out-links for similarity computation. However, the existing work of P-Rank computation is expensive in terms of time and space cost and cannot efficiently support similarity computation in large information networks. For tackling this problem, in this paper, we propose an optimization technique for fast P-Rank computation in information networks by adopting the spiritual of partial sums. We write P-Rank equation based on partial sums and further approximate this equation by setting a threshold for ignoring the small similarity scores during iterative similarity computation. An optimized similarity computation algorithm is developed, which reduces the computation cost by skipping the similarity scores smaller than the give threshold during accumulation operations. And the accuracy loss estimation under the threshold is given through extensive mathematical analysis. Extensive experiments demonstrate the effectiveness and efficiency of our proposed approach through comparing with the straightforward P-Rank computation algorithm.
{"title":"Partial sums-based P-Rank computation in information networks","authors":"Jinhua Wang, Mingxi Zhang, Zhenying He, Wei Wang","doi":"10.1145/3106426.3109447","DOIUrl":"https://doi.org/10.1145/3106426.3109447","url":null,"abstract":"P-Rank is a simple and captivating link-based similarity measure that extends SimRank by exploiting both in- and out-links for similarity computation. However, the existing work of P-Rank computation is expensive in terms of time and space cost and cannot efficiently support similarity computation in large information networks. For tackling this problem, in this paper, we propose an optimization technique for fast P-Rank computation in information networks by adopting the spiritual of partial sums. We write P-Rank equation based on partial sums and further approximate this equation by setting a threshold for ignoring the small similarity scores during iterative similarity computation. An optimized similarity computation algorithm is developed, which reduces the computation cost by skipping the similarity scores smaller than the give threshold during accumulation operations. And the accuracy loss estimation under the threshold is given through extensive mathematical analysis. Extensive experiments demonstrate the effectiveness and efficiency of our proposed approach through comparing with the straightforward P-Rank computation algorithm.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72784413","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}
D. Calvaresi, Mauro Marinoni, A. Sturm, M. Schumacher, G. Buttazzo
Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application domains particularly include three major characteristics: intelligence, autonomy and real-time behavior. Multi-Agent Systems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to comply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from current theories, standards, and technological implementations. In particular, internal agent schedulers, communication middlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.
{"title":"The challenge of real-time multi-agent systems for enabling IoT and CPS","authors":"D. Calvaresi, Mauro Marinoni, A. Sturm, M. Schumacher, G. Buttazzo","doi":"10.1145/3106426.3106518","DOIUrl":"https://doi.org/10.1145/3106426.3106518","url":null,"abstract":"Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application domains particularly include three major characteristics: intelligence, autonomy and real-time behavior. Multi-Agent Systems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to comply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from current theories, standards, and technological implementations. In particular, internal agent schedulers, communication middlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80884933","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}
Matrix factorization (MF) models and their extensions are widely used in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose mixture models which combine the technology of MF and the embedding. We show that some of these models significantly improve the performance over the state-of-the-art models on two real-world datasets, and explain how the mixture models improve the quality of recommendations.
{"title":"Exploiting user and item embedding in latent factor models for recommendations","authors":"Zhaoqiang Li, Jiajin Huang, N. Zhong","doi":"10.1145/3106426.3109437","DOIUrl":"https://doi.org/10.1145/3106426.3109437","url":null,"abstract":"Matrix factorization (MF) models and their extensions are widely used in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose mixture models which combine the technology of MF and the embedding. We show that some of these models significantly improve the performance over the state-of-the-art models on two real-world datasets, and explain how the mixture models improve the quality of recommendations.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74224907","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}
For many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining---and specially, association rule mining or web mining---is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships---in the form of association rules---among frequently occurring patterns. These algorithms include level-wise Apriori-based algorithms, tree-based algorithms, hyperlinked array structure based algorithms, and vertical mining algorithms. While these algorithms are popular, they suffer from some drawbacks. Moreover, as we are living in the era of big data, high volumes of a wide variety of valuable data of different veracity collected at a high velocity post another challenges to data science and big data analytics. To deal with these big data while avoiding the drawbacks of existing algorithms, we present a bitwise parallel association rule mining system for web mining and recommendation in this paper. Evaluation results show the effectiveness and practicality of our parallel algorithm---which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them---in real-life web applications.
{"title":"Bitwise parallel association rule mining for web page recommendation","authors":"C. Leung, Fan Jiang, Adam G. M. Pazdor","doi":"10.1145/3106426.3106542","DOIUrl":"https://doi.org/10.1145/3106426.3106542","url":null,"abstract":"For many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining---and specially, association rule mining or web mining---is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships---in the form of association rules---among frequently occurring patterns. These algorithms include level-wise Apriori-based algorithms, tree-based algorithms, hyperlinked array structure based algorithms, and vertical mining algorithms. While these algorithms are popular, they suffer from some drawbacks. Moreover, as we are living in the era of big data, high volumes of a wide variety of valuable data of different veracity collected at a high velocity post another challenges to data science and big data analytics. To deal with these big data while avoiding the drawbacks of existing algorithms, we present a bitwise parallel association rule mining system for web mining and recommendation in this paper. Evaluation results show the effectiveness and practicality of our parallel algorithm---which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them---in real-life web applications.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73521252","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}
Recommending courses to online students is a fundamental and also challenging issue in MOOCs. Not exactly like recommendation in traditional online systems, students who enrolled the same course may have very different purposes and with very different backgrounds. For example, one may want to study "data mining" after studying the course of "big data analytics" because the former is a prerequisite course of the latter, while some other may choose "data mining" simply because of curiosity. Employing the complete data from XuetangX1, one of the largest MOOCs in China, we conduct a systematic investigation on the problem of student behavior modeling for course recommendation. We design a content-aware algorithm framework using content based users' access behaviors to extract user-specific latent information to represent students' interest profile. We also leverage the demographics and course prerequisite relation to better reveal users' potential choice. Finally, we develop a course recommendation algorithm based on the user interest, demographic profiles and course prerequisite relation using collaborative filtering strategy. Experiment results demonstrate that the proposed algorithm performs much better than several baselines (over 2X by MRR). We have deployed the recommendation algorithm onto the platform XuetangX as a new feature, which significantly helps improve the course recommendation performance (+24.6% by click rate) comparing with the recommendation strategy previously used in the system.
{"title":"Guess you like: course recommendation in MOOCs","authors":"Xia Jing, Jie Tang","doi":"10.1145/3106426.3106478","DOIUrl":"https://doi.org/10.1145/3106426.3106478","url":null,"abstract":"Recommending courses to online students is a fundamental and also challenging issue in MOOCs. Not exactly like recommendation in traditional online systems, students who enrolled the same course may have very different purposes and with very different backgrounds. For example, one may want to study \"data mining\" after studying the course of \"big data analytics\" because the former is a prerequisite course of the latter, while some other may choose \"data mining\" simply because of curiosity. Employing the complete data from XuetangX1, one of the largest MOOCs in China, we conduct a systematic investigation on the problem of student behavior modeling for course recommendation. We design a content-aware algorithm framework using content based users' access behaviors to extract user-specific latent information to represent students' interest profile. We also leverage the demographics and course prerequisite relation to better reveal users' potential choice. Finally, we develop a course recommendation algorithm based on the user interest, demographic profiles and course prerequisite relation using collaborative filtering strategy. Experiment results demonstrate that the proposed algorithm performs much better than several baselines (over 2X by MRR). We have deployed the recommendation algorithm onto the platform XuetangX as a new feature, which significantly helps improve the course recommendation performance (+24.6% by click rate) comparing with the recommendation strategy previously used in the system.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82193482","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}
Norms in multi-agent systems are used as a mechanism to regulate the behavior of autonomous and heterogeneous agents and to maintain the social order of the society of agents. Norms describe actions that must be performed, actions that can be performed and actions that cannot be performed by a given entity in a certain situation. One of the challenges in designing and managing systems governed by norms is that they can conflict with another. Two norms are in conflict when the fulfillment of one causes the violation of the other. When that happens, whatever the agent does or refrains from doing will lead to a social constraint being broken. Several researches have been proposed mechanisms to detect conflicts between norms. However, there is a kind of normative conflict not investigated yet in the design phase, here called runtime conflicts, that can only be detected if we know information about the runtime execution of the system. This paper presents two approaches based on execution scenarios to detect normative conflicts that depends on execution order of runtime events in multi-agent systems. In the first approach, the system designer are able to provide examples of execution scenarios and evaluate the conflicts that may arise if those scenarios would be executed in the system. In the second approach, the conflict checker identifies potential normative conflicts by switching the position order of the runtime events referred in the norm conditions.
{"title":"Detection of normative conflict that depends on execution order of runtime events in multi-agent systems","authors":"Mairon Belchior, V. Silva","doi":"10.1145/3106426.3106509","DOIUrl":"https://doi.org/10.1145/3106426.3106509","url":null,"abstract":"Norms in multi-agent systems are used as a mechanism to regulate the behavior of autonomous and heterogeneous agents and to maintain the social order of the society of agents. Norms describe actions that must be performed, actions that can be performed and actions that cannot be performed by a given entity in a certain situation. One of the challenges in designing and managing systems governed by norms is that they can conflict with another. Two norms are in conflict when the fulfillment of one causes the violation of the other. When that happens, whatever the agent does or refrains from doing will lead to a social constraint being broken. Several researches have been proposed mechanisms to detect conflicts between norms. However, there is a kind of normative conflict not investigated yet in the design phase, here called runtime conflicts, that can only be detected if we know information about the runtime execution of the system. This paper presents two approaches based on execution scenarios to detect normative conflicts that depends on execution order of runtime events in multi-agent systems. In the first approach, the system designer are able to provide examples of execution scenarios and evaluate the conflicts that may arise if those scenarios would be executed in the system. In the second approach, the conflict checker identifies potential normative conflicts by switching the position order of the runtime events referred in the norm conditions.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87620447","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}
Professional networks are a specialized class of social networks that are particularly aimed at forming and strengthening professional connections and have become a vital component of professional success and growth. In this paper, we present a holistic model to jointly represent different heterogenous relationships between pairs of individuals, user actions and their respective propagations to characterize influence in online professional networks. Previous work on influence in social networks typically only consider a single action type in characterizing influence. Our model is capable of representing and combining different kinds of information users assimilate in the network and compute pairwise values of influence taking the different types of actions into account. We evaluate our models on data from the largest professional network, LinkedIn and show the effectiveness of the inferred influence scores in predicting user actions. We further demonstrate that modeling different user actions, node features, and edge relationships between users leads to around 20% increase in precision at top k in predicting user actions, when compared to the current state-of-the-art model.
{"title":"Multi-relational influence models for online professional networks","authors":"Arti Ramesh, Mario Rodríguez, L. Getoor","doi":"10.1145/3106426.3106531","DOIUrl":"https://doi.org/10.1145/3106426.3106531","url":null,"abstract":"Professional networks are a specialized class of social networks that are particularly aimed at forming and strengthening professional connections and have become a vital component of professional success and growth. In this paper, we present a holistic model to jointly represent different heterogenous relationships between pairs of individuals, user actions and their respective propagations to characterize influence in online professional networks. Previous work on influence in social networks typically only consider a single action type in characterizing influence. Our model is capable of representing and combining different kinds of information users assimilate in the network and compute pairwise values of influence taking the different types of actions into account. We evaluate our models on data from the largest professional network, LinkedIn and show the effectiveness of the inferred influence scores in predicting user actions. We further demonstrate that modeling different user actions, node features, and edge relationships between users leads to around 20% increase in precision at top k in predicting user actions, when compared to the current state-of-the-art model.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88139777","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}
Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, we present a dataset of user comments, using crowdsourcing for labelling. Since abusive content can be ambiguous and subjective to the individual reader, we propose an aggregated mechanism for assessing different opinions from different labellers. In addition, instead of the typical binary categories of abusive or not, we introduce a third class of 'undecided' to capture the real life scenario of instances that are neither blatantly abusive nor clearly harmless. We have performed preliminary experiments on this dataset using best practice techniques in text classification. Finally, we have evaluated the detection performance of various feature groups, namely syntactic, semantic and context-based features. Results show these features can increase our classifier performance by 18% in detection of abusive content.
{"title":"Presenting a labelled dataset for real-time detection of abusive user posts","authors":"Hao Chen, Susan Mckeever, Sarah Jane Delany","doi":"10.1145/3106426.3106456","DOIUrl":"https://doi.org/10.1145/3106426.3106456","url":null,"abstract":"Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, we present a dataset of user comments, using crowdsourcing for labelling. Since abusive content can be ambiguous and subjective to the individual reader, we propose an aggregated mechanism for assessing different opinions from different labellers. In addition, instead of the typical binary categories of abusive or not, we introduce a third class of 'undecided' to capture the real life scenario of instances that are neither blatantly abusive nor clearly harmless. We have performed preliminary experiments on this dataset using best practice techniques in text classification. Finally, we have evaluated the detection performance of various feature groups, namely syntactic, semantic and context-based features. Results show these features can increase our classifier performance by 18% in detection of abusive content.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90538339","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 need for cross-analyzing JSON objects representing heterogeneous geo-referenced information coming from multiple sources, such as open data published on the Web by public administrations and crowd-sourced posts and images from social networks, is becoming common for studying, predicting and planning social dynamics. Nevertheless, although NoSQL databases have emerged as a de facto standard means to store JSON objects, a query language that can be easily used by not-programmers to manipulate and correlate such data is still missing. Furthermore, when the information is geo-referenced, we also need both spatial analysis and mapping facilities. In the paper, we motivate the need for a novel flexible framework, named J-CO, that provides a query language, named J-CO-QL, enabling novel declarative (spatial) queries for JSON objects. We will illustrate the basic concepts of the proposal and the possible use of its spatial and non-spatial operators for cross-analyzing open data and crowd-sourced information. This framework is powered by a plug-in for QGIS that can be used to write and execute queries on MongoDB databases.
{"title":"A flexible framework to cross-analyze heterogeneous multi-source geo-referenced information: the J-CO-QL proposal and its implementation","authors":"Gloria Bordogna, Daniele E. Ciriello, G. Psaila","doi":"10.1145/3106426.3106537","DOIUrl":"https://doi.org/10.1145/3106426.3106537","url":null,"abstract":"The need for cross-analyzing JSON objects representing heterogeneous geo-referenced information coming from multiple sources, such as open data published on the Web by public administrations and crowd-sourced posts and images from social networks, is becoming common for studying, predicting and planning social dynamics. Nevertheless, although NoSQL databases have emerged as a de facto standard means to store JSON objects, a query language that can be easily used by not-programmers to manipulate and correlate such data is still missing. Furthermore, when the information is geo-referenced, we also need both spatial analysis and mapping facilities. In the paper, we motivate the need for a novel flexible framework, named J-CO, that provides a query language, named J-CO-QL, enabling novel declarative (spatial) queries for JSON objects. We will illustrate the basic concepts of the proposal and the possible use of its spatial and non-spatial operators for cross-analyzing open data and crowd-sourced information. This framework is powered by a plug-in for QGIS that can be used to write and execute queries on MongoDB databases.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77873976","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：