SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining最新文献_第3页

The DevOps Paradigm with Cloud Data Analytics for Green Business Applications 绿色商业应用的DevOps范例与云数据分析

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2018-05-29 DOI: 10.1145/3229329.3229334

Michael J. Pawlish, A. Varde

引用次数: 8

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper. 机器学习模型构建和数据挖掘算法执行的进展指标:立场文件。

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-12-01 DOI: 10.1145/3166054.3166057

Gang Luo

引用次数: 16

On the Measurement and Prediction of Web Content Utility: A Review Web内容效用的测量与预测研究综述

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-11-21 DOI: 10.1145/3166054.3166056

Yuan Yao, Hanghang Tong, Feng Xu, Jian Lu

引用次数: 1

Delve: A Dataset-Driven Scholarly Search and Analysis System Delve:一个数据集驱动的学术搜索和分析系统

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-11-21 DOI: 10.1145/3166054.3166059

Uchenna Akujuobi, Xiangliang Zhang

{"title":"Delve: A Dataset-Driven Scholarly Search and Analysis System","authors":"Uchenna Akujuobi, Xiangliang Zhang","doi":"10.1145/3166054.3166059","DOIUrl":"https://doi.org/10.1145/3166054.3166059","url":null,"abstract":"Research and experimentation in various scientific fields are based on the observation, analysis and benchmarking on datasets. The advancement of research and development has thus, strengthened the importance of dataset access. However, without enough knowledge of relevant datasets, researchers usually have to go through a process which we term manual dataset retrieval\". With the accelerated rate of scholarly publications, manually finding the relevant dataset for a given research area based on its usage or popularity is increasingly becoming more and more difficult and tedious. In this paper, we present Delve, a web-based dataset retrieval and document analysis system. Unlike traditional academic search engines and dataset repositories, Delve is dataset driven and provides a medium for dataset retrieval based on the suitability or usage in a given field. It also visualizes dataset and document citation relationship, and enables users to analyze a scientific document by uploading its full PDF. In this paper, we first discuss the reasons why the scientific community needs a system like Delve. We then proceed to introduce its internal design and explain how Delve works and how it is beneficial to researchers of all levels","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"31 1","pages":"36-46"},"PeriodicalIF":0.0,"publicationDate":"2017-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75291029","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: 26

Intelligent Disaster Response via Social Media Analysis A Survey 基于社会媒体分析的智能灾难响应调查

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-09-01 DOI: 10.1145/3137597.3137602

Tahora H. Nazer, G. Xue, Yusheng Ji, Huan Liu

引用次数: 71

Collaborative Filtering for Binary, Positiveonly Data 二进制、纯正数据的协同过滤

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-09-01 DOI: 10.1145/3137597.3137599

Koen Verstrepen, Kanishka Bhaduri, B. Cule, Bart Goethals

引用次数: 25

Common Pitfalls in Training and Evaluating Recommender Systems 培训和评估推荐系统的常见缺陷

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-09-01 DOI: 10.1145/3137597.3137601

Hung-Hsuan Chen, Chu-An Chung, Hsin-Chien Huang, Wen Tsui

引用次数: 20

Supervised Learning Techniques in Mobile Device Apps for Androids android移动设备应用程序中的监督学习技术

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-03-22 DOI: 10.1145/3068777.3068782

Priyanka Basavaraju, A. Varde

{"title":"Supervised Learning Techniques in Mobile Device Apps for Androids","authors":"Priyanka Basavaraju, A. Varde","doi":"10.1145/3068777.3068782","DOIUrl":"https://doi.org/10.1145/3068777.3068782","url":null,"abstract":"Mobile devices have become an integral part of our daily lives. Most people carry smartphones today almost everywhere; and have other mobile devices such as tablets, often more convenient than full-fledged laptops for work transit, short trips etc. This had led to development of apps for mobile devices, easy to download and access anywhere anytime. An important field improving human experiences on mobile devices is machine learning. This constitutes technqiues involving acquisition of knowledge, skills and understanding by machines from examples, guidance, experience or reflection to learn analogous to humans. Among learning paradigms herein, supervised learning comprises situations where labeled training samples are provided to administer the process, making it more regulated, similar to human instructors providing such examples with notions of correctness to guide human learners. Supervised learning techniques are useful in designing mobile apps as they entail guided examples capturing specific human needs and their reasoning in activities, e.g., classification. This paper gives a comprehensive review of a few useful supervised learning approaches along with their implementation in mobile apps, focusing on Androids as they constitute over 50% of the global smartphone market. It includes description of the approaches and portrays interesting Android apps deploying them, addressing classification and regression problems. We discuss the contributions and critiques of the apps and also present open issues with the potential for further research in related areas. This paper is expected to be useful to students, researchers and developers in mobile computing, human computer interaction, data mining and machine learning.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"48 1","pages":"18-29"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79086647","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: 21

Data Science for Food, Energy and Water: A Workshop Report 粮食、能源和水的数据科学:研讨会报告

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-03-22 DOI: 10.1145/3068777.3068779

N. Abe, Yiqun Xie, S. Shekhar, C. Apté, Vipin Kumar, M. Tuinstra, Ranga Raju Vatsavai

引用次数: 5

User Identity Linkage across Online Social Networks: A Review 跨在线社交网络的用户身份关联研究综述

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2017-03-22 DOI: 10.1145/3068777.3068781

Kai Shu, Suhang Wang, Jiliang Tang, R. Zafarani, Huan Liu

{"title":"User Identity Linkage across Online Social Networks: A Review","authors":"Kai Shu, Suhang Wang, Jiliang Tang, R. Zafarani, Huan Liu","doi":"10.1145/3068777.3068781","DOIUrl":"https://doi.org/10.1145/3068777.3068781","url":null,"abstract":"The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"42 1","pages":"5-17"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91112234","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: 217