ACM Journal of Data and Information Quality (JDIQ)最新文献

Editorial: Special Issue on Deep Learning for Data Quality 社论:关于数据质量的深度学习专题

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-06-24 DOI: 10.1145/3513135

Donatello Santoro, Saravanan Thirumuruganathan, Paolo Papotti

引用次数: 0

AI Compliance – Challenges of Bridging Data Science and Law 人工智能合规性-连接数据科学和法律的挑战

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-05-05 DOI: 10.1145/3531532

P. Hacker, Felix Naumann, Tobias Friedrich, Stefan Grundmann, Anja Lehmann, Herbert Zech

引用次数: 1

Design and Implementation of a Historical German Firm-level Financial Database 德国历史企业财务数据库的设计与实现

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-04-30 DOI: 10.1145/3531533

Dennis Gram, Pantelis Karapanagiotis, Marius Liebald, U. Walz

引用次数: 0

Contextual Data Cleaning with Ontology Functional Dependencies 基于本体功能依赖的上下文数据清理

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-04-21 DOI: 10.1145/3524303

Zheng Zheng, Longtao Zheng, Morteza Alipourlangouri, Fei Chiang, Lukasz Golab, Jaroslaw Szlichta, S. Baskaran

引用次数: 2

Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs 正诊断过程二部图上使用距离池的负保险索赔生成

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-04-18 DOI: 10.1145/3531347

Md Enamul Haque, M. E. Tozal

引用次数: 0

A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling 基于聚类-标记的少镜头学习方法及其在图像数据自动标注中的应用

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-03-04 DOI: 10.1145/3491232

Renzhi Wu, Nilaksh Das, Sanya Chaba, Sakshi Gandhi, Duen Horng Chau, Xu Chu

{"title":"A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling","authors":"Renzhi Wu, Nilaksh Das, Sanya Chaba, Sakshi Gandhi, Duen Horng Chau, Xu Chu","doi":"10.1145/3491232","DOIUrl":"https://doi.org/10.1145/3491232","url":null,"abstract":"Few-shot learning (FSL) aims at learning to generalize from only a small number of labeled examples for a given target task. Most current state-of-the-art FSL methods typically have two limitations. First, they usually require access to a source dataset (in a similar domain) with abundant labeled examples, which may not always be possible due to privacy concerns and copyright issues. Second, they typically do not offer any estimation of the generalization error on the target FSL task, because the handful of labeled examples must be used for training and cannot spare a validation subset. In this article, we propose a cluster-then-label approach to perform few-shot learning. Our approach does not require access to the labeled source dataset and provides an estimation of generalization error. We show empirically, on four benchmark datasets, that our approach provides competitive predictive performance to state-of-the-art FSL approaches and our generalization error estimation is accurate. Finally, we explore the application of our proposed method to automatic image data labeling. We compare our method with existing automatic data labeling systems. The end-to-end performance of our method outperforms the state-of-the-art automatic data labeling system Snuba by 26% and is only 7% away from the fully supervised upper bound.","PeriodicalId":299504,"journal":{"name":"ACM Journal of Data and Information Quality (JDIQ)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129567449","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: 6

Machine Learning and Data Cleaning: Which Serves the Other? 机器学习和数据清理:谁为谁服务?

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-03-04 DOI: 10.1145/3506712

I. Ilyas, Theodoros Rekatsinas

引用次数: 9

Which Conference Is That? A Case Study in Computer Science 那是哪个会议?计算机科学案例研究

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-03-04 DOI: 10.1145/3519031

C. Demetrescu, Irene Finocchi, Andrea Ribichini, M. Schaerf

引用次数: 1

A Web Information Extraction Framework with Adaptive and Failure Prediction Feature 一种具有自适应和故障预测特征的Web信息提取框架

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-02-18 DOI: 10.1145/3495008

Sudhir Kumar Patnaik, C. Babu

{"title":"A Web Information Extraction Framework with Adaptive and Failure Prediction Feature","authors":"Sudhir Kumar Patnaik, C. Babu","doi":"10.1145/3495008","DOIUrl":"https://doi.org/10.1145/3495008","url":null,"abstract":"The amount of information available on the internet today requires effective information extraction and processing to offer hyper-personalized user experiences. Inability to extract information by using traditional and machine learning techniques due to dynamic changes in website layout pose significant challenges to the technical community to keep up with such changes. The focus of existing machine learning-based information extraction framework is only on information extraction by using core extraction logic that is susceptible to website changes, thus missing out core features such as ability to handle proactive failure prediction and intelligent information extraction capabilities. The aim of this article is to build a robust and intelligent information extraction framework with the ability not only to proactively predict website failure but also automatically extract information using deep-learning techniques using You Only Look Once and Long Short-term Memory (LSTM) networks. The proactive detection using LSTM detects new location of the web page due to layout changes and enables automatic extraction of information of the new web page. A real-world case with retail website for intelligent information extraction and an offline experimentation environment is setup to demonstrate proactive failure prediction and automatic extraction resulting in high failure prediction, precision and recall of object detection and information extraction.","PeriodicalId":299504,"journal":{"name":"ACM Journal of Data and Information Quality (JDIQ)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133574605","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

Editorial: Special Issue on Data Transparency—Uses Cases and Applications 社论:数据透明度专题——用例和应用

ACM Journal of Data and Information Quality (JDIQ) Pub Date : 2022-02-11 DOI: 10.1145/3494455

M. Barhamgi, E. Bertino

{"title":"Editorial: Special Issue on Data Transparency—Uses Cases and Applications","authors":"M. Barhamgi, E. Bertino","doi":"10.1145/3494455","DOIUrl":"https://doi.org/10.1145/3494455","url":null,"abstract":"Advances in Artificial Intelligence (AI) and mobile and Internet technologies have been progressively reshaping our lives over the past few years. The applications of the Internet of Things and cyber-physical systems today touch almost all aspects of our daily lives, including healthcare (e.g., remote patient monitoring environments), leisure (e.g., smart entertainment spaces), and work (e.g., smart manufacturing and asset management). For many of us, social media have become the rule rather than the exception as the way to interact, socialize, and exchange information. AI-powered systems have become a reality and started to affect our lives in important ways. These systems and services collect huge amounts of data about us and exploit it for various purposes that could affect our lives positively or negatively. Even though most of these systems claim to abide by data protection regulations and ethics, data misuse incidents keep making the headlines. In this new digital world, data transparency for end users is becoming a fundamental aspect to consider when designing, implementing, and deploying a system, service, or software [1, 3, 4]. Transparency allows users to track down and follow how their data are collected, transmitted, stored, processed, exploited, and serviced. It also allows them to verify how fairly they are treated by algorithms, software, and systems that affect their lives. Data transparency is a complex concept that is interpreted and approached in different ways by different research communities and bodies. A comprehensive definition of data transparency is proposed by Bertino et al. as “the ability of subjects to effectively gain access to all information related to data used in processes and decisions that affect the subjects” [2].","PeriodicalId":299504,"journal":{"name":"ACM Journal of Data and Information Quality (JDIQ)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127726514","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: 1