There has been a surge in opinion-sharing in the public domain. Some opinions greatly influence our decisions, e.g., the choice of purchase. Malicious parties or individuals exploit social media by generating fake reviews for opinion manipulation. This paper aims to investigate the phenomenon of online paid restaurant reviews by bloggers. Our research provides an insight into some characteristics of paid reviews and their authors. We then explore a set of features based on our observations and detect paid reviews and paid bloggers using supervised machine learning techniques. Experimental results show the effectiveness of our approach.
{"title":"Paid review and paid writer detection","authors":"Man-Chun Ko, Hen-Hsen Huang, Hsin-Hsi Chen","doi":"10.1145/3106426.3106433","DOIUrl":"https://doi.org/10.1145/3106426.3106433","url":null,"abstract":"There has been a surge in opinion-sharing in the public domain. Some opinions greatly influence our decisions, e.g., the choice of purchase. Malicious parties or individuals exploit social media by generating fake reviews for opinion manipulation. This paper aims to investigate the phenomenon of online paid restaurant reviews by bloggers. Our research provides an insight into some characteristics of paid reviews and their authors. We then explore a set of features based on our observations and detect paid reviews and paid bloggers using supervised machine learning techniques. Experimental results show the effectiveness of our approach.","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":"85927336","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}
Shreyansh P. Bhatt, B. Minnery, Srikanth Nadella, B. Bullemer, V. Shalin, A. Sheth
"Wisdom of Crowds" (WoC) refers to a form of collective intelligence in which the aggregate judgment of a group of individuals is, in most instances, superior to that of any one group member. For a crowd to be wise, its members must possess diverse knowledge and viewpoints. Such diversity leads to uncorrelated judgment errors that cancel out in aggregate. Yet despite the fact that diversity is known to be an essential ingredient in WoC, little research aims to measure and exploit diversity in human social systems for the purpose of maximizing crowd intelligence. Here we quantify the diversity of a group of individuals through semantic analysis of their social media (Twitter) communications. Focusing on the domain of fantasy sports, we show that virtual crowds of fantasy team owners selected based on the diversity of their tweet content can outperform both non-diverse and randomly sampled crowds. Our results suggest a new approach for intelligent crowd assembly in which measures of diversity extracted from online social media communications can guide the selection of crowd members. These results have implications for numerous domains that utilize aggregated judgments - from consumer reviews, to econometrics, to geopolitical forecasting and intelligence analysis.
{"title":"Enhancing crowd wisdom using measures of diversity computed from social media data","authors":"Shreyansh P. Bhatt, B. Minnery, Srikanth Nadella, B. Bullemer, V. Shalin, A. Sheth","doi":"10.1145/3106426.3106491","DOIUrl":"https://doi.org/10.1145/3106426.3106491","url":null,"abstract":"\"Wisdom of Crowds\" (WoC) refers to a form of collective intelligence in which the aggregate judgment of a group of individuals is, in most instances, superior to that of any one group member. For a crowd to be wise, its members must possess diverse knowledge and viewpoints. Such diversity leads to uncorrelated judgment errors that cancel out in aggregate. Yet despite the fact that diversity is known to be an essential ingredient in WoC, little research aims to measure and exploit diversity in human social systems for the purpose of maximizing crowd intelligence. Here we quantify the diversity of a group of individuals through semantic analysis of their social media (Twitter) communications. Focusing on the domain of fantasy sports, we show that virtual crowds of fantasy team owners selected based on the diversity of their tweet content can outperform both non-diverse and randomly sampled crowds. Our results suggest a new approach for intelligent crowd assembly in which measures of diversity extracted from online social media communications can guide the selection of crowd members. These results have implications for numerous domains that utilize aggregated judgments - from consumer reviews, to econometrics, to geopolitical forecasting and intelligence analysis.","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":"88750037","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}
Comparison shopping portals integrate product offers from large numbers of e-shops in order to support consumers in their buying decisions. Product offers often consist of a title and a free-text product description, both describing product attributes that are considered relevant by the specific vendor. In addition, product offers might contain structured or semi-structured product specifications in the form of HTML tables and HTML lists. As product specifications often cover more product attributes than free-text descriptions, being able to extract attribute-value pairs from these specifications is a critical prerequisite for achieving good results in tasks such as product matching, product categorisation, faceted product search, and product recommendation. In this paper, we present an approach for extracting attribute-value pairs from product specifications on the Web. We use supervised learning to classify the HTML tables and HTML lists within a web page as product specification or not. In order to extract attribute-value pairs from the HTML fragments identified by the specification detector, we again use supervised learning to classify columns as attribute column or value column. Compared to DEXTER, the current state-of-the-art approach for extracting attribute-value pairs from product specifications, we introduce several new features for specification detection and support the extraction of attribute-value pairs from specifications having more than two columns. This allows us to improve the F-score up to 10% for extracting attribute-value pairs from tables and up to 3% for lists. In addition, we report the results of using duplicate-based schema matching to align the product attribute schemata of 32 different e-shops. This experiment confirms the suitability of duplicate-based schema matching for product data integration.
{"title":"Extracting attribute-value pairs from product specifications on the web","authors":"P. Petrovski, Christian Bizer","doi":"10.1145/3106426.3106449","DOIUrl":"https://doi.org/10.1145/3106426.3106449","url":null,"abstract":"Comparison shopping portals integrate product offers from large numbers of e-shops in order to support consumers in their buying decisions. Product offers often consist of a title and a free-text product description, both describing product attributes that are considered relevant by the specific vendor. In addition, product offers might contain structured or semi-structured product specifications in the form of HTML tables and HTML lists. As product specifications often cover more product attributes than free-text descriptions, being able to extract attribute-value pairs from these specifications is a critical prerequisite for achieving good results in tasks such as product matching, product categorisation, faceted product search, and product recommendation. In this paper, we present an approach for extracting attribute-value pairs from product specifications on the Web. We use supervised learning to classify the HTML tables and HTML lists within a web page as product specification or not. In order to extract attribute-value pairs from the HTML fragments identified by the specification detector, we again use supervised learning to classify columns as attribute column or value column. Compared to DEXTER, the current state-of-the-art approach for extracting attribute-value pairs from product specifications, we introduce several new features for specification detection and support the extraction of attribute-value pairs from specifications having more than two columns. This allows us to improve the F-score up to 10% for extracting attribute-value pairs from tables and up to 3% for lists. In addition, we report the results of using duplicate-based schema matching to align the product attribute schemata of 32 different e-shops. This experiment confirms the suitability of duplicate-based schema matching for product data integration.","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":"84868417","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}
Somayeh Koohborfardhaghighi, J. Altmann, K. Tserpes
An employee profile record within a human resource management department includes information about the employee's past activities within the enterprise. These profile records are valuable sources of information for any enterprise. Using this information requires an intelligent enterprise information system. In this study, we emphasize the importance of having detailed analyses on the employees' knowledge base within an enterprise by applying dynamic social impact theory. We argue that the richer the knowledge base within an enterprise with respect to its human and social capital is, the more it can empower its employees to be creative and innovative during group works. We propose a framework for effectively modeling the ever-changing knowledge bases of big enterprises for delivering optimal and automated team composition techniques. Our discussions cover the complete pipeline from data management and knowledge modeling, via graph analysis, to decision support services.
{"title":"Social analytics framework for intelligent information systems based on a complex adaptive systems approach","authors":"Somayeh Koohborfardhaghighi, J. Altmann, K. Tserpes","doi":"10.1145/3106426.3109430","DOIUrl":"https://doi.org/10.1145/3106426.3109430","url":null,"abstract":"An employee profile record within a human resource management department includes information about the employee's past activities within the enterprise. These profile records are valuable sources of information for any enterprise. Using this information requires an intelligent enterprise information system. In this study, we emphasize the importance of having detailed analyses on the employees' knowledge base within an enterprise by applying dynamic social impact theory. We argue that the richer the knowledge base within an enterprise with respect to its human and social capital is, the more it can empower its employees to be creative and innovative during group works. We propose a framework for effectively modeling the ever-changing knowledge bases of big enterprises for delivering optimal and automated team composition techniques. Our discussions cover the complete pipeline from data management and knowledge modeling, via graph analysis, to decision support services.","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":"87365428","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}
Selecting 24 novels written by Mo Yan and Zhang Wei as corpus, This paper analyzed the stylistic features of Mo Yan and Zhang Wei's novels from the perspective of quantitative style. Features include the pauses in sentences, the relevance of context, the type/token ratio, the frequency of the word string, high-frequency words and text clustering. Through statistic analysis, it is found that Mo Yan and Zhang Wei's works have much in common, which are both very oral, creative and can use all kinds of linguistic materials. However, they are different from one another in the usage of sentence patterns and of words and in the attention of social life. Compared with Mo Yan's language features, Zhang Wei's is more changeable.
{"title":"Quantitative style analysis of Mo Yan and Zhang Wei's novels","authors":"Yunxuan Li, Weiyun Ji, Dekuan Xu","doi":"10.1145/3106426.3109045","DOIUrl":"https://doi.org/10.1145/3106426.3109045","url":null,"abstract":"Selecting 24 novels written by Mo Yan and Zhang Wei as corpus, This paper analyzed the stylistic features of Mo Yan and Zhang Wei's novels from the perspective of quantitative style. Features include the pauses in sentences, the relevance of context, the type/token ratio, the frequency of the word string, high-frequency words and text clustering. Through statistic analysis, it is found that Mo Yan and Zhang Wei's works have much in common, which are both very oral, creative and can use all kinds of linguistic materials. However, they are different from one another in the usage of sentence patterns and of words and in the attention of social life. Compared with Mo Yan's language features, Zhang Wei's is more changeable.","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":"82829799","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}
Ontology matching is the process of identifying alignment between ontologies with the objective to facilitate interoperability and knowledge integration. A limitation of state of the art ontology matching systems is that the produced alignments usually only contain equivalence relations, while other relations, such as subsumption relations, are often neglected. Furthermore, an ontology matching system is normally composed of a set of differently tuned matching algorithms, but their appropriateness and order of execution typically require human judgement. The COMPOSE framework identifies subsumption relations automatically using a three-stage matcher composition process. These three processes are ontology analysis, matcher selection, and matcher combination. Within this framework we integrate existing techniques for all three processes with novel ones and evaluate the feasibility of the framework in an experiment involving six acknowledged ontologies.
{"title":"Matcher composition for identification of subsumption relations in ontology matching","authors":"A. Vennesland","doi":"10.1145/3106426.3106503","DOIUrl":"https://doi.org/10.1145/3106426.3106503","url":null,"abstract":"Ontology matching is the process of identifying alignment between ontologies with the objective to facilitate interoperability and knowledge integration. A limitation of state of the art ontology matching systems is that the produced alignments usually only contain equivalence relations, while other relations, such as subsumption relations, are often neglected. Furthermore, an ontology matching system is normally composed of a set of differently tuned matching algorithms, but their appropriateness and order of execution typically require human judgement. The COMPOSE framework identifies subsumption relations automatically using a three-stage matcher composition process. These three processes are ontology analysis, matcher selection, and matcher combination. Within this framework we integrate existing techniques for all three processes with novel ones and evaluate the feasibility of the framework in an experiment involving six acknowledged ontologies.","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":"82881172","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}
In this paper we analysed how the behavior of an online financial community changed in times of geopolitical crises. In particular, we studied the behaviour and communication patterns of online investors before and after a military geopolitical event. We selected a set of 23 key-events belonging to the 2003 US-led invasion of Iraq, the Arab Spring and the first period of the Ukraine crisis, and we restricted our study to a set of eight so called war stocks. We studied the resilience of the community to information shocks by comparing the community composition, its sentiment and users' communication networks before and after an event at different time intervals. We found how community reaction is governed by ordered patterns. Experimental evidence suggested how in the after-math of an event the community did not lose its information sharing functionality. Communication networks showed a higher in-degree Gini index, connectivity and a rich-club effect. Discussions developed around central users acting as hubs. These backbone users were present both before and after an event, their sentiment were less volatile than other users, and they were previously recognized as local experts of a specific stock. As a further evidence of community resilience, the equilibrium of all the indicators analysed were restored after two weeks.
{"title":"Analysing the behaviour of online investors in times of geopolitical distress: a case study on war stocks","authors":"Pierpaolo Dondio, J. Usher","doi":"10.1145/3106426.3106510","DOIUrl":"https://doi.org/10.1145/3106426.3106510","url":null,"abstract":"In this paper we analysed how the behavior of an online financial community changed in times of geopolitical crises. In particular, we studied the behaviour and communication patterns of online investors before and after a military geopolitical event. We selected a set of 23 key-events belonging to the 2003 US-led invasion of Iraq, the Arab Spring and the first period of the Ukraine crisis, and we restricted our study to a set of eight so called war stocks. We studied the resilience of the community to information shocks by comparing the community composition, its sentiment and users' communication networks before and after an event at different time intervals. We found how community reaction is governed by ordered patterns. Experimental evidence suggested how in the after-math of an event the community did not lose its information sharing functionality. Communication networks showed a higher in-degree Gini index, connectivity and a rich-club effect. Discussions developed around central users acting as hubs. These backbone users were present both before and after an event, their sentiment were less volatile than other users, and they were previously recognized as local experts of a specific stock. As a further evidence of community resilience, the equilibrium of all the indicators analysed were restored after two weeks.","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":"91432665","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}
Understanding the expertise of users in social networking sites like Twitter is a key component for many applications such as user recommendation and talent seeking. A range of interactions between users on Twitter can provide important information that implicitly reflects a user's expertise. This paper proposes a learning model that tries to infer a user's topical expertise from Twitter using information such as tweets posted by the user and the characteristics of their followers. The model takes various types of user-related data from Twitter as input and considers their inference consistency in the process of learning. It aims to deliver accurate and effective inference results, even in cases where some types of data are missing for a user, e.g. the user has yet to post any tweets. The experiments reported in the paper were conducted on a large-scale Twitter dataset. Experimental results show that our model outperforms several baseline approaches and outperforms approaches which use only a single type of user data for inference.
{"title":"Inferring your expertise from Twitter: combining multiple types of user activity","authors":"Yu Xu, Dong Zhou, S. Lawless","doi":"10.1145/3106426.3106468","DOIUrl":"https://doi.org/10.1145/3106426.3106468","url":null,"abstract":"Understanding the expertise of users in social networking sites like Twitter is a key component for many applications such as user recommendation and talent seeking. A range of interactions between users on Twitter can provide important information that implicitly reflects a user's expertise. This paper proposes a learning model that tries to infer a user's topical expertise from Twitter using information such as tweets posted by the user and the characteristics of their followers. The model takes various types of user-related data from Twitter as input and considers their inference consistency in the process of learning. It aims to deliver accurate and effective inference results, even in cases where some types of data are missing for a user, e.g. the user has yet to post any tweets. The experiments reported in the paper were conducted on a large-scale Twitter dataset. Experimental results show that our model outperforms several baseline approaches and outperforms approaches which use only a single type of user data for inference.","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":"90950272","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}
This paper proposes an interaction strategy called the extended expectation-of-cooperation (EEoC) that is intended to spread cooperative activities in prisoner's dilemma situations over an entire agent network. Recently developed computer and communications applications run on the network and interact with each other as delegates of the owners, so they often encounter social dilemma situations. To improve social efficiency, they are required to cooperate, but one-sided cooperation is meaningless and loses some payoff due to a rip-off by defecting agents. The concept of EEoC is that when agents encounter mutual cooperation, they continue to cooperate a few times with the desire to see the emergence of cooperative behavior in their neighbors. EEoC is easy to implement in computer systems. We experimentally show that EEoC can effectively spread cooperative activities in dilemma situations in complete, Erdös-Rényi, and regular networks. We also clarify the robustness against defecting agents and the limitation of the EEoC strategy.
{"title":"Robust spread of cooperation by expectation-of-cooperation strategy with simple labeling method","authors":"Tomoaki Otsuka, T. Sugawara","doi":"10.1145/3106426.3106458","DOIUrl":"https://doi.org/10.1145/3106426.3106458","url":null,"abstract":"This paper proposes an interaction strategy called the extended expectation-of-cooperation (EEoC) that is intended to spread cooperative activities in prisoner's dilemma situations over an entire agent network. Recently developed computer and communications applications run on the network and interact with each other as delegates of the owners, so they often encounter social dilemma situations. To improve social efficiency, they are required to cooperate, but one-sided cooperation is meaningless and loses some payoff due to a rip-off by defecting agents. The concept of EEoC is that when agents encounter mutual cooperation, they continue to cooperate a few times with the desire to see the emergence of cooperative behavior in their neighbors. EEoC is easy to implement in computer systems. We experimentally show that EEoC can effectively spread cooperative activities in dilemma situations in complete, Erdös-Rényi, and regular networks. We also clarify the robustness against defecting agents and the limitation of the EEoC strategy.","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":"90993615","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}
This paper provides an overview on Data Mining (DM) technologies for the Internet of Things (IoT). IoT has become an active area of research, since IoT promises among other to improve quality of live and safety in Smart Cities, to make resource supply and waste management more efficient, and optimize traffic. DM is highly domain specific and depends on what is being mined for. For instance, if IoT is used to optimize traffic in a Smart City to reduce traffic jams and to find parking spaces quicker, different types of data needs to be collected and analysed from an eHealth solution, where IoT is used in a Smart Home to monitor the well being of patients or elderly people. IoT connects things that can collect numeric data from smart sensors, streaming data from cameras or route information on maps. Depending on the type of data, different techniques need to be adopted to analyse them. Also, many IoT applications analyse data from different devices and correlate them to make predictions about possible machine failures in production sites or looming emergency situations in Smart Buildings in a home security application. DM techniques need to handle the heterogeneity of IoT data, the large volumes of data and the speed at which they are produced. This paper explores the state of the art DM techniques for IoT.
{"title":"Data mining in IoT: data analysis for a new paradigm on the internet","authors":"Peter Wlodarczak, Mustafa A. Ally, J. Soar","doi":"10.1145/3106426.3115866","DOIUrl":"https://doi.org/10.1145/3106426.3115866","url":null,"abstract":"This paper provides an overview on Data Mining (DM) technologies for the Internet of Things (IoT). IoT has become an active area of research, since IoT promises among other to improve quality of live and safety in Smart Cities, to make resource supply and waste management more efficient, and optimize traffic. DM is highly domain specific and depends on what is being mined for. For instance, if IoT is used to optimize traffic in a Smart City to reduce traffic jams and to find parking spaces quicker, different types of data needs to be collected and analysed from an eHealth solution, where IoT is used in a Smart Home to monitor the well being of patients or elderly people. IoT connects things that can collect numeric data from smart sensors, streaming data from cameras or route information on maps. Depending on the type of data, different techniques need to be adopted to analyse them. Also, many IoT applications analyse data from different devices and correlate them to make predictions about possible machine failures in production sites or looming emergency situations in Smart Buildings in a home security application. DM techniques need to handle the heterogeneity of IoT data, the large volumes of data and the speed at which they are produced. This paper explores the state of the art DM techniques for IoT.","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":"85164134","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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