SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining最新文献_第2页

Urban Human Mobility: Data-Driven Modeling and Prediction 城市人口流动:数据驱动的建模与预测

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2019-05-13 DOI: 10.1145/3331651.3331653

Jinzhong Wang, Xiangjie Kong, Feng Xia, Lijun Sun

{"title":"Urban Human Mobility: Data-Driven Modeling and Prediction","authors":"Jinzhong Wang, Xiangjie Kong, Feng Xia, Lijun Sun","doi":"10.1145/3331651.3331653","DOIUrl":"https://doi.org/10.1145/3331651.3331653","url":null,"abstract":"Human mobility is a multidisciplinary field of physics and computer science and has drawn a lot of attentions in recent years. Some representative models and prediction approaches have been proposed for modeling and predicting human mobility. However, multi-source heterogeneous data from handheld terminals, GPS, and social media, provides a new driving force for exploring urban human mobility patterns from a quantitative and microscopic perspective. The studies of human mobility modeling and prediction play a vital role in a series of applications such as urban planning, epidemic control, location-based services, and intelligent transportation management. In this survey, we review human mobility models based on a human-centric angle in a datadriven context. Specifically, we characterize human mobility patterns from individual, collective, and hybrid levels. Meanwhile, we survey human mobility prediction methods from four aspects and then describe recent development respectively. Finally, we discuss some open issues that provide a helpful reference for researchers' future direction. This review not only lays a solid foundation for beginners who want to acquire a quick understanding of human mobility but also provides helpful information for researchers on how to develop a unified human mobility model.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"59 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84613621","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}

引用次数: 61

Co-Designing Learning Materials to Empower Laypersons to Better Understand Big Data and Big Data Methods 共同设计学习材料，帮助外行更好地理解大数据和大数据方法

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2019-05-13 DOI: 10.1145/3331651.3331659

L. Schilling, Griselda Pena-Jackson, S. Russell, J. Corral, Bethany M. Kwan, Julie Ressalam

引用次数: 1

Budding Data Scientists Hackathon 崭露头角的数据科学家黑客马拉松

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2019-05-13 DOI: 10.1145/3331651.3331658

ChuaHui Xiang, ChuaEe-Ling, SooKenneth

引用次数: 2

Women Data Science Leaders in Russia 俄罗斯女性数据科学领袖

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2019-05-13 DOI: 10.1145/3331651.3331660

A. Suvorova, V. Ivaniushina, Alina Bakhitova, Anastasiya Kuznetsova

引用次数: 0

Data Science Summer Academy for Chicago Public School Students 芝加哥公立学校学生数据科学暑期学院

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2019-05-13 DOI: 10.1145/3331651.3331661

B. Mobasher, L. Dettori, D. Raicu, R. Settimi, Nasim Sonboli, M. Stettler

引用次数: 9

SIGKDD Impact Program 2018 2018年SIGKDD影响计划

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2019-05-13 DOI: 10.1145/3331651.3331657

R. Sosič, R. Ghani, J. Leskovec

引用次数: 0

Research Issues of Outlier Detection in Trajectory Streams Using GPUs 基于gpu的轨迹流异常点检测研究

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2018-12-11 DOI: 10.1145/3299986.3299989

Eleazar Leal, L. Gruenwald

{"title":"Research Issues of Outlier Detection in Trajectory Streams Using GPUs","authors":"Eleazar Leal, L. Gruenwald","doi":"10.1145/3299986.3299989","DOIUrl":"https://doi.org/10.1145/3299986.3299989","url":null,"abstract":"The widespread availability of sensors like GPS and traffic cameras has made it possible to collect large amounts of spatio-temporal data. One such type of data are trajectories, each of which consists of a time-ordered sequence of positions that a moving object occupies in space as time goes by. Trajectories can be streamed in real time from sensors, and because of this, they capture the current state of moving objects. For this reason, trajectories can be used in applications such as the real-time detection of senior citizens who have just fallen or who have just gotten lost outdoors, the real-time detection of drunk drivers, and the real-time detection of enemy forces in the battlefield. These applications involve the identification of trajectories with anomalous behaviors, and require fast processing in order to take immediate preventive action. However, outlier detection poses challenges stemming from both the complexity of the data and of the task. One way to address this is through parallel architectures like GPUs. In this paper, we present the problem of outlier detection in trajectory streams, and discuss the research issues that should be addressed by new outlier detection techniques for trajectory streams on GPUs.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"33 1","pages":"13-20"},"PeriodicalIF":0.0,"publicationDate":"2018-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85289669","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}

引用次数: 7

Progress Indication for Machine Learning Model Building: A Feasibility Demonstration. 机器学习模型构建的进展指示:可行性论证。

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2018-12-01 DOI: 10.1145/3299986.3299988

Gang Luo

引用次数: 3

Broad Learning:: An Emerging Area in Social Network Analysis 广泛学习:社会网络分析的新兴领域

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2018-05-29 DOI: 10.1145/3229329.3229333

Jiawei Zhang, Philip S. Yu

{"title":"Broad Learning:: An Emerging Area in Social Network Analysis","authors":"Jiawei Zhang, Philip S. Yu","doi":"10.1145/3229329.3229333","DOIUrl":"https://doi.org/10.1145/3229329.3229333","url":null,"abstract":"Looking from a global perspective, the landscape of online social networks is highly fragmented. A large number of online social networks have appeared, which can provide users with various types of services. Generally, information available in these online social networks is of diverse categories, which can be represented as heterogeneous social networks (HSNs) formally. Meanwhile, in such an age of online social media, users usually participate in multiple online social networks simultaneously, who can act as the anchors aligning different social networks together. So multiple HSNs not only represent information in each social network, but also fuse information from multiple networks.\u0000 Formally, the online social networks sharing common users are named as the aligned social networks, and these shared users are called the anchor users. The heterogeneous information generated by users' social activities in the multiple aligned social networks provides social network practitioners and researchers with the opportunities to study individual user's social behaviors across multiple social platforms simultaneously. This paper presents a comprehensive survey about the latest research works on multiple aligned HSNs studies based on the broad learning setting, which covers 5 major research tasks, including network alignment, link prediction, community detection, information diffusion and network embedding respectively.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"10 1","pages":"24-50"},"PeriodicalIF":0.0,"publicationDate":"2018-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87905202","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}

引用次数: 15

A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction] 演化数据异常检测研究综述[及其在森林火险预测中的应用]

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2018-05-29 DOI: 10.1145/3229329.3229332

Mahsa Salehi, Lida Rashidi

{"title":"A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]","authors":"Mahsa Salehi, Lida Rashidi","doi":"10.1145/3229329.3229332","DOIUrl":"https://doi.org/10.1145/3229329.3229332","url":null,"abstract":"Traditionally most of the anomaly detection algorithms have been designed for 'static' datasets, in which all the observations are available at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in 'evolving' data. Over the last two decades, many algorithms have been proposed to detect anomalies in evolving data. Some of them consider scenarios where a sequence of objects (called data streams) with one or multiple features evolves over time. Whereas the others concentrate on more complex scenarios, where streaming objects with one or multiple features have causal/non-causal relationships with each other. The latter can be represented as evolving graphs. In this paper, we categorize existing strategies for detecting anomalies in both scenarios including the state-of-the-art techniques. Since label information is mostly unavailable in real-world applications when data evolves, we review the unsupervised approaches in this paper. We then present an interesting application example, i.e., forest re risk prediction, and conclude the paper with future research directions in this eld for researchers and industry.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"3 1","pages":"13-23"},"PeriodicalIF":0.0,"publicationDate":"2018-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73862908","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}

引用次数: 63