2017 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第2页

A Bootstrap Method for Automatic Rule Acquisition on Emotion Cause Extraction 一种基于自举法的情感原因自动提取规则获取方法

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.60

Shuntaro Yada, K. Ikeda, K. Hoashi, K. Kageura

引用次数: 29

On Analyzing Job Hop Behavior and Talent Flow Networks 跳槽行为与人才流动网络分析

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.172

R. J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo

{"title":"On Analyzing Job Hop Behavior and Talent Flow Networks","authors":"R. J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo","doi":"10.1109/ICDMW.2017.172","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.172","url":null,"abstract":"Analyzing job hopping behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow and organization competition. Traditionally, surveys are conducted on job seekers and employers to study job behavior. While surveys are good at getting direct user input to specially designed questions, they are often not scalable and timely enough to cope with fast-changing job landscape. In this paper, we present a data science approach to analyze job hops performed by about 490,000 working professionals located in a city using their publicly shared profiles. We develop several metrics to measure how much work experience is needed to take up a job and how recent/established the job is, and then examine how these metrics correlate with the propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Finally, we perform network analyses at the job and organization levels in order to derive insights on talent flow as well as job and organizational competitiveness.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122891398","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: 5

Online Detection of Anomalous Heterogeneous Graphs with Streaming Edges 带有流边的异常异构图的在线检测

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.133

L. Akoglu

{"title":"Online Detection of Anomalous Heterogeneous Graphs with Streaming Edges","authors":"L. Akoglu","doi":"10.1109/ICDMW.2017.133","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.133","url":null,"abstract":"Given a stream of heterogeneous edges, comprising different types of nodes and edges, which arrive in an interleaved fashion to multiple different graphs evolving simultaneously, how can we spot the anomalous graphs in real-time using only constant memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. In this talk, I will introduce STREAMSPOT, a clustering based anomaly detection approach for streaming heterogeneous graphs that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature. Specifically, we introduce a new similarity function for heterogeneous graphs that compares two graphs based on their relative frequency of local substructures, represented as short strings. This function lends itself to a vector representation of each graph, which is (a) fast to compute, and (b) amenable to a sketched version with bounded size that preserves the aforementioned similarity. STREAMSPOT exhibits desirable properties that a streaming application requires–it is (i) fully-streaming; processing the stream one edge at a time as it arrives, (ii) memory-efficient; requiring constant space for the sketches and the clustering, (iii) fast; taking constant time to update the graph sketches and the cluster summaries that can process over 100K edges per second, and (iv) online; scoring and flagging anomalies in real time. Experiments on datasets containing simulated system-call flow graphs from normal browser activity and various attack scenarios (ground truth) show that STREAMSPOT is high-performance; achieving above 95% detection accuracy with small delay, and competitive response time and memory usage.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121902706","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: 0

GPS Data Reflect Players’ Internal Load in Soccer GPS数据反映足球运动员的内部负荷

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.122

A. Rossi, E. Perri, A. Trecroci, Marco Savino, G. Alberti, M. Iaia

引用次数: 13

Familiarity and Strangeness of Objects: A MoDAT Requirement for Shikake Design 对象的熟悉性与陌生性:石客设计的MoDAT要求

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.84

N. Matsumura

引用次数: 0

An Adaptive Modeling Framework for Bivariate Data Streams with Applications to Change Detection in Cyber-Physical Systems 双变量数据流的自适应建模框架及其在信息物理系统中变化检测中的应用

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.151

Joshua Plasse, J. Noble, Kary L. Myers

{"title":"An Adaptive Modeling Framework for Bivariate Data Streams with Applications to Change Detection in Cyber-Physical Systems","authors":"Joshua Plasse, J. Noble, Kary L. Myers","doi":"10.1109/ICDMW.2017.151","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.151","url":null,"abstract":"Cyber-physical systems - systems that incorporate physical devices with cyber components - are appearing in diverse applications, and due to advances in data acquisition, are accompanied with large amounts of data. The interplay between the cyber and the physical components leaves such systems vulnerable to faults and intrusions, motivating the development of a general model that can efficiently and continuously monitor a cyber-physical system. To be of practical value, the model should be adaptive and equipped with the ability to detect changes in the system. This paper makes three contributions: (1) a new adaptive modeling framework for monitoring an arbitrary cyber-physical system in real-time using a flexible statistical distribution called the normal-gamma; (2) a novel streaming validation procedure, demonstrated on data streams from a cyber-physical system at Los Alamos National Laboratory, to justify the use of the normal-gamma and our new adaptive modeling approach; and (3) a new online change detection algorithm demonstrated on synthetic normal-gamma data streams.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128616435","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

Sentiment Extraction from Consumer-Generated Noisy Short Texts 从消费者生成的噪声短文本中提取情感

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.58

Hardik Meisheri, Kunal Ranjan, Lipika Dey

引用次数: 6

Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries 数字图书馆科学主题语料库扩展的实例语义搜索

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.103

Hussein T. Al-Natsheh, Lucie Martinet, Fabrice Muhlenbach, Fabien Rico, D. Zighed

引用次数: 8

Finding Suspicious Activities in Financial Transactions and Distributed Ledgers 在金融交易和分布式账本中发现可疑活动

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.109

R. Camino, R. State, Leandro Montero, Petko Valtchev

{"title":"Finding Suspicious Activities in Financial Transactions and Distributed Ledgers","authors":"R. Camino, R. State, Leandro Montero, Petko Valtchev","doi":"10.1109/ICDMW.2017.109","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.109","url":null,"abstract":"Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned regulations accordingly. Software tools are developed to detect suspicious transactions usually based on knowledge from experts in the domain, but as new criminal tactics emerge, detection mechanisms must be updated. Suspicious activity examples are scarce or nonexistent, hindering the use of supervised machine learning methods. In this paper, we describe a methodology for analyzing financial information without the use of ground truth. A user suspicion ranking is generated in order to facilitate human expert validation using an ensemble of anomaly detection algorithms. We apply our procedure over two case studies: one related to bank fund movements from a private company and the other concerning Ripple network transactions. We illustrate how both examples share interesting similarities and that the resulting user ranking leads to suspicious findings, showing that anomaly detection is a must in both traditional and modern payment systems.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133857406","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: 35

A Novel l0-Constrained Gaussian Graphical Model for Anomaly Localization 一种新的10约束高斯图模型用于异常定位

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.115

D. Phan, T. Idé, J. Kalagnanam, M. Menickelly, K. Scheinberg

引用次数: 2