Pub Date : 2020-03-18DOI: 10.21203/rs.3.rs-17789/v1
Dianne S. V. Medeiros, H. N. C. Neto, Martin Andreoni Lopez, Luiz Claudio S. Magalhães, N. Fernandes, A. Vieira, E. F. Silva, D. M. F. Mattos
In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.
{"title":"A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges","authors":"Dianne S. V. Medeiros, H. N. C. Neto, Martin Andreoni Lopez, Luiz Claudio S. Magalhães, N. Fernandes, A. Vieira, E. F. Silva, D. M. F. Mattos","doi":"10.21203/rs.3.rs-17789/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-17789/v1","url":null,"abstract":"In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"11 1","pages":"1-48"},"PeriodicalIF":3.5,"publicationDate":"2020-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41494300","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 : 2020-02-21DOI: 10.1186/s13174-020-0122-y
Robson A. Campêlo, Marco A. Casanova, Dorgival O. Guedes, Alberto H. F. Laender
Cloud computing is a general term that involves delivering hosted services over the Internet. With the accelerated growth of the volume of data used by applications, many organizations have moved their data into cloud servers to provide scalable, reliable and highly available services. A particularly challenging issue that arises in the context of cloud storage systems with geographically-distributed data replication is how to reach a consistent state for all replicas. This survey reviews major aspects related to consistency issues in cloud data storage systems, categorizing recently proposed methods into three categories: (1) fixed consistency methods, (2) configurable consistency methods and (3) consistency monitoring methods.
{"title":"A brief survey on replica consistency in cloud environments","authors":"Robson A. Campêlo, Marco A. Casanova, Dorgival O. Guedes, Alberto H. F. Laender","doi":"10.1186/s13174-020-0122-y","DOIUrl":"https://doi.org/10.1186/s13174-020-0122-y","url":null,"abstract":"Cloud computing is a general term that involves delivering hosted services over the Internet. With the accelerated growth of the volume of data used by applications, many organizations have moved their data into cloud servers to provide scalable, reliable and highly available services. A particularly challenging issue that arises in the context of cloud storage systems with geographically-distributed data replication is how to reach a consistent state for all replicas. This survey reviews major aspects related to consistency issues in cloud data storage systems, categorizing recently proposed methods into three categories: (1) fixed consistency methods, (2) configurable consistency methods and (3) consistency monitoring methods.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"33 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517547","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 : 2019-12-01DOI: 10.1186/s13174-019-0120-0
Dhanielly P. R. de Lima, M. Gerosa, T. Conte, José Francisco de M. Netto
{"title":"What to expect, and how to improve online discussion forums: the instructors’ perspective","authors":"Dhanielly P. R. de Lima, M. Gerosa, T. Conte, José Francisco de M. Netto","doi":"10.1186/s13174-019-0120-0","DOIUrl":"https://doi.org/10.1186/s13174-019-0120-0","url":null,"abstract":"","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"10 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13174-019-0120-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65834162","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 : 2019-11-09DOI: 10.1186/s13174-019-0119-6
João Vitor Torres, Igor Drummond Alvarenga, Raouf Boutaba, Otto Carlos Muniz Bandeira Duarte
The huge amount of content names available in Named-Data Networking (NDN) challenges both the required routing table size and the techniques for locating and forwarding information. Content copies and content mobility exacerbate the scalability challenge to reach content in the new locations. We present and analyze the performance of a proposed Controller-based Routing Scheme, named CRoS-NDN, which preserves NDN features using the same interest and data packets. CRoS-NDN supports content mobility and provides fast content recovery from copies that do not belong to the consumer-producer path because it splits identity from location without incurring FIB size explosion or supposing prefix aggregation. It provides features similar to Content Distribution Networks (CDN) in NDN, and improves the routing efficiency. We compare our proposal with similar routing protocols and derive analytical expressions for lower-bound efficiency and upper-bound latency. We also conduct extensive simulations to evaluate results in data delivery efficiency and delay. The results show the robust behavior of the proposed scheme achieving the best efficiency and delay performance for a wide range of scenarios. Furthermore, CRoS-NDN results in low use of processing time and memory for a growing number of prefixes.
命名数据网络(NDN)中可用的大量内容名称既挑战了所需的路由表大小，也挑战了定位和转发信息的技术。内容复制和内容移动性加剧了在新位置访问内容的可伸缩性挑战。我们提出并分析了一种名为CRoS-NDN的基于控制器的路由方案的性能，该方案使用相同的兴趣和数据包保留了NDN的特征。cross - ndn支持内容移动性，并从不属于消费者-生产者路径的副本中提供快速的内容恢复，因为它将身份从位置分离，而不会导致FIB大小爆炸或假设前缀聚合。它提供了与NDN中的CDN (Content Distribution Networks)相似的特性，提高了路由效率。我们将我们的提议与类似的路由协议进行比较，并推导出下限效率和上限延迟的解析表达式。我们还进行了大量的模拟来评估数据传递效率和延迟的结果。结果表明，该方案具有良好的鲁棒性，可在多种场景下获得最佳的效率和延迟性能。此外，对于越来越多的前缀，cross - ndn导致处理时间和内存的使用减少。
{"title":"Evaluating CRoS-NDN: a comparative performance analysis of a controller-based routing scheme for named-data networking","authors":"João Vitor Torres, Igor Drummond Alvarenga, Raouf Boutaba, Otto Carlos Muniz Bandeira Duarte","doi":"10.1186/s13174-019-0119-6","DOIUrl":"https://doi.org/10.1186/s13174-019-0119-6","url":null,"abstract":"The huge amount of content names available in Named-Data Networking (NDN) challenges both the required routing table size and the techniques for locating and forwarding information. Content copies and content mobility exacerbate the scalability challenge to reach content in the new locations. We present and analyze the performance of a proposed Controller-based Routing Scheme, named CRoS-NDN, which preserves NDN features using the same interest and data packets. CRoS-NDN supports content mobility and provides fast content recovery from copies that do not belong to the consumer-producer path because it splits identity from location without incurring FIB size explosion or supposing prefix aggregation. It provides features similar to Content Distribution Networks (CDN) in NDN, and improves the routing efficiency. We compare our proposal with similar routing protocols and derive analytical expressions for lower-bound efficiency and upper-bound latency. We also conduct extensive simulations to evaluate results in data delivery efficiency and delay. The results show the robust behavior of the proposed scheme achieving the best efficiency and delay performance for a wide range of scenarios. Furthermore, CRoS-NDN results in low use of processing time and memory for a growing number of prefixes.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"1 1","pages":"1-24"},"PeriodicalIF":3.5,"publicationDate":"2019-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517521","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 : 2019-10-16DOI: 10.1186/s13174-019-0118-7
Lucas M. Ponce, Walter dos Santos, Wagner Meira, Dorgival Guedes, Daniele Lezzi, Rosa M. Badia
High-performance computing (HPC) and massive data processing (Big Data) are two trends that are beginning to converge. In that process, aspects of hardware architectures, systems support and programming paradigms are being revisited from both perspectives. This paper presents our experience on this path of convergence with the proposal of a framework that addresses some of the programming issues derived from such integration. Our contribution is the development of an integrated environment that integretes (i) COMPSs, a programming framework for the development and execution of parallel applications for distributed infrastructures; (ii) Lemonade, a data mining and analysis tool; and (iii) HDFS, the most widely used distributed file system for Big Data systems. To validate our framework, we used Lemonade to create COMPSs applications that access data through HDFS, and compared them with equivalent applications built with Spark, a popular Big Data framework. The results show that the HDFS integration benefits COMPSs by simplifying data access and by rearranging data transfer, reducing execution time. The integration with Lemonade facilitates COMPSs’s use and may help its popularization in the Data Science community, by providing efficient algorithm implementations for experts from the data domain that want to develop applications with a higher level abstraction.
{"title":"Upgrading a high performance computing environment for massive data processing","authors":"Lucas M. Ponce, Walter dos Santos, Wagner Meira, Dorgival Guedes, Daniele Lezzi, Rosa M. Badia","doi":"10.1186/s13174-019-0118-7","DOIUrl":"https://doi.org/10.1186/s13174-019-0118-7","url":null,"abstract":"High-performance computing (HPC) and massive data processing (Big Data) are two trends that are beginning to converge. In that process, aspects of hardware architectures, systems support and programming paradigms are being revisited from both perspectives. This paper presents our experience on this path of convergence with the proposal of a framework that addresses some of the programming issues derived from such integration. Our contribution is the development of an integrated environment that integretes (i) COMPSs, a programming framework for the development and execution of parallel applications for distributed infrastructures; (ii) Lemonade, a data mining and analysis tool; and (iii) HDFS, the most widely used distributed file system for Big Data systems. To validate our framework, we used Lemonade to create COMPSs applications that access data through HDFS, and compared them with equivalent applications built with Spark, a popular Big Data framework. The results show that the HDFS integration benefits COMPSs by simplifying data access and by rearranging data transfer, reducing execution time. The integration with Lemonade facilitates COMPSs’s use and may help its popularization in the Data Science community, by providing efficient algorithm implementations for experts from the data domain that want to develop applications with a higher level abstraction.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"176 2 1","pages":"1-18"},"PeriodicalIF":3.5,"publicationDate":"2019-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517524","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 : 2019-09-11DOI: 10.1186/s13174-019-0116-9
A. M. Souza, T. Braun, L. C. Botega, R. Cabral, Islene C. Garcia, L. Villas
{"title":"Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues","authors":"A. M. Souza, T. Braun, L. C. Botega, R. Cabral, Islene C. Garcia, L. Villas","doi":"10.1186/s13174-019-0116-9","DOIUrl":"https://doi.org/10.1186/s13174-019-0116-9","url":null,"abstract":"","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"6 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2019-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13174-019-0116-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65834126","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 : 2019-09-06DOI: 10.1186/s13174-019-0117-8
Aldri L. Santos, Christian A. V. Cervantes, Michele Nogueira, Burak Kantarci
As an integral component of the 5G communications, the massive Internet of Things (IoT) are vulnerable to various routing attacks due to their dynamic infrastructure, distinct computing resources, and heterogeneity of mobile objects. The sinkhole and selective forwarding attacks stand out among the most destructive ones for infrastructureless networks. Despite the countermeasures introduced by legacy intrusion detection systems (IDS), the massive IoT seeks novel solutions to address their unique requirements. This paper introduces DeTection of SinkHole And SelecTive ForwArding for Supporting SeCure routing for Internet of THIngs (THATACHI), a new IDS against sinkhole and selective forwarding attacks that target routing mechanism in massive and mobile IoT networks. To cope with the density and mobility challenges in the detection of attackers and ensuring reliability, THATACHI exploits watchdog, reputation and trust strategies. Our performance evaluation under an urban scenario shows that THATACHI can perform with a 99% detection rate, 6% of false negative and false positive rates. Moreover, when compared to its closest predecessor against sinkhole attacks for IoT, THATACHI runs with at least 50% less energy consumption.
{"title":"Clustering and reliability-driven mitigation of routing attacks in massive IoT systems","authors":"Aldri L. Santos, Christian A. V. Cervantes, Michele Nogueira, Burak Kantarci","doi":"10.1186/s13174-019-0117-8","DOIUrl":"https://doi.org/10.1186/s13174-019-0117-8","url":null,"abstract":"As an integral component of the 5G communications, the massive Internet of Things (IoT) are vulnerable to various routing attacks due to their dynamic infrastructure, distinct computing resources, and heterogeneity of mobile objects. The sinkhole and selective forwarding attacks stand out among the most destructive ones for infrastructureless networks. Despite the countermeasures introduced by legacy intrusion detection systems (IDS), the massive IoT seeks novel solutions to address their unique requirements. This paper introduces DeTection of SinkHole And SelecTive ForwArding for Supporting SeCure routing for Internet of THIngs (THATACHI), a new IDS against sinkhole and selective forwarding attacks that target routing mechanism in massive and mobile IoT networks. To cope with the density and mobility challenges in the detection of attackers and ensuring reliability, THATACHI exploits watchdog, reputation and trust strategies. Our performance evaluation under an urban scenario shows that THATACHI can perform with a 99% detection rate, 6% of false negative and false positive rates. Moreover, when compared to its closest predecessor against sinkhole attacks for IoT, THATACHI runs with at least 50% less energy consumption.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"60 1","pages":"1-17"},"PeriodicalIF":3.5,"publicationDate":"2019-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517589","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 : 2019-08-27DOI: 10.1186/s13174-019-0115-x
Yao Pan, Fangzhou Sun, Zhongwei Teng, Jules White, Douglas C. Schmidt, Jacob Staples, Lee Krause
Web applications are popular targets for cyber-attacks because they are network-accessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features, however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labeled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications. This paper provides three contributions to the study of autonomic intrusion detection systems. First, we evaluate the feasibility of an unsupervised/semi-supervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT), which autonomically monitors and characterizes the runtime behavior of web applications. Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with unlabeled request data is used to recognize anomalies by computing the reconstruction error of the request data. Third, we analyze the results of empirically testing RSMT on both synthetic datasets and production applications with intentional vulnerabilities. Our results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data.
{"title":"Detecting web attacks with end-to-end deep learning","authors":"Yao Pan, Fangzhou Sun, Zhongwei Teng, Jules White, Douglas C. Schmidt, Jacob Staples, Lee Krause","doi":"10.1186/s13174-019-0115-x","DOIUrl":"https://doi.org/10.1186/s13174-019-0115-x","url":null,"abstract":"Web applications are popular targets for cyber-attacks because they are network-accessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features, however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labeled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications. This paper provides three contributions to the study of autonomic intrusion detection systems. First, we evaluate the feasibility of an unsupervised/semi-supervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT), which autonomically monitors and characterizes the runtime behavior of web applications. Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with unlabeled request data is used to recognize anomalies by computing the reconstruction error of the request data. Third, we analyze the results of empirically testing RSMT on both synthetic datasets and production applications with intentional vulnerabilities. Our results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"2 1","pages":"1-22"},"PeriodicalIF":3.5,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517571","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 : 2019-08-03DOI: 10.1186/s13174-019-0114-y
Joahannes B. D. da Costa, Allan M. de Souza, Denis Rosário, Eduardo Cerqueira, Leandro A. Villas
Services that aim to make the current transportation system more secure, sustainable, and efficient constitute the Traffic Management Systems (TMS). Vehicular Ad hoc Networks (VANETs) exert a strong influence for TMS applications, due to TMS services require data, communication, and processing for operation. Besides, VANET allows direct communication between vehicles, and data are exchanged and processed between them. Several TMS services require disseminated information among decision-making vehicles. However, such dissemination is a challenging task, due to the specific characteristics of VANETs, such as short-range communication and high node mobility, resulting in several variations in their topology. In this article, we introduce an extensive analysis of our proposed data dissemination protocol based on complex networks’ metrics for urban VANET scenarios, called DDRX. Each vehicle must build a subgraph to identify the relay node to continue the dissemination process. Based on the local graph, it is possible to select the relay nodes based on complex networks’ metrics. Simulation results show that DDRX offers high efficiency in terms of coverage, number of transmitted packets, delay, and packet collisions compared to well-known data dissemination protocols. Also, DDRX provides significant improvements to a TMS that needs efficient data dissemination.
{"title":"Efficient data dissemination protocol based on complex networks’ metrics for urban vehicular networks","authors":"Joahannes B. D. da Costa, Allan M. de Souza, Denis Rosário, Eduardo Cerqueira, Leandro A. Villas","doi":"10.1186/s13174-019-0114-y","DOIUrl":"https://doi.org/10.1186/s13174-019-0114-y","url":null,"abstract":"Services that aim to make the current transportation system more secure, sustainable, and efficient constitute the Traffic Management Systems (TMS). Vehicular Ad hoc Networks (VANETs) exert a strong influence for TMS applications, due to TMS services require data, communication, and processing for operation. Besides, VANET allows direct communication between vehicles, and data are exchanged and processed between them. Several TMS services require disseminated information among decision-making vehicles. However, such dissemination is a challenging task, due to the specific characteristics of VANETs, such as short-range communication and high node mobility, resulting in several variations in their topology. In this article, we introduce an extensive analysis of our proposed data dissemination protocol based on complex networks’ metrics for urban VANET scenarios, called DDRX. Each vehicle must build a subgraph to identify the relay node to continue the dissemination process. Based on the local graph, it is possible to select the relay nodes based on complex networks’ metrics. Simulation results show that DDRX offers high efficiency in terms of coverage, number of transmitted packets, delay, and packet collisions compared to well-known data dissemination protocols. Also, DDRX provides significant improvements to a TMS that needs efficient data dissemination.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"1 1","pages":"1-13"},"PeriodicalIF":3.5,"publicationDate":"2019-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517546","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 : 2019-07-22DOI: 10.1186/s13174-019-0113-z
Rodrigo Pereira dos Santos, Giseli Rabello Lopes
Social networks were first investigated in social, educational and business areas. Academic interest in this field though has been growing since the mid twentieth century, given the increasing interaction among people, data dissemination and exchange of information. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. Key topics include contextualized analysis of social and information networks, crowdsourcing and crowdfunding, economics in networks, extraction and treatment of social data, mining techniques, modeling of user behavior and social networks, and software ecosystems. These topics have important areas of application in a wide range of fields, such as academia, politics, security, business, marketing, and science.
{"title":"Thematic series on Social Network Analysis and Mining","authors":"Rodrigo Pereira dos Santos, Giseli Rabello Lopes","doi":"10.1186/s13174-019-0113-z","DOIUrl":"https://doi.org/10.1186/s13174-019-0113-z","url":null,"abstract":"Social networks were first investigated in social, educational and business areas. Academic interest in this field though has been growing since the mid twentieth century, given the increasing interaction among people, data dissemination and exchange of information. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. Key topics include contextualized analysis of social and information networks, crowdsourcing and crowdfunding, economics in networks, extraction and treatment of social data, mining techniques, modeling of user behavior and social networks, and software ecosystems. These topics have important areas of application in a wide range of fields, such as academia, politics, security, business, marketing, and science.","PeriodicalId":46467,"journal":{"name":"Journal of Internet Services and Applications","volume":"1 1","pages":"1-4"},"PeriodicalIF":3.5,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517522","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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