Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)最新文献

Interactive Data Cleaning for Real-Time Streaming Applications 实时流应用的交互式数据清理

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605229

Timo Räth, Ngozichukwuka Onah, K. Sattler

引用次数: 0

Facilitating Dependency Exploration in Computational Notebooks 促进计算机笔记本的依赖探索

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605222

C. Brown, Hamed Alhoori, D. Koop

引用次数: 0

Overlay Spreadsheets 覆盖电子表格

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605220

Oliver Kennedy, Boris Glavic, Mike Brachmann

引用次数: 0

Camera-First Form Filling: Reducing the Friction in Climate Hazard Reporting 相机优先填表:减少气候灾害报告中的摩擦

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605218

Kristina Wolf, Dominik Winecki, Arnab Nandi

{"title":"Camera-First Form Filling: Reducing the Friction in Climate Hazard Reporting","authors":"Kristina Wolf, Dominik Winecki, Arnab Nandi","doi":"10.1145/3597465.3605218","DOIUrl":"https://doi.org/10.1145/3597465.3605218","url":null,"abstract":"The effective reporting of climate hazards, such as flash floods, hurricanes, and earthquakes, is critical. To quickly and correctly assess the situation and deploy resou rces, emergency services often rely on citizen reports that must be timely, comprehensive, and accurate. The pervasive availability and use of smartphone cameras allow the transmission of dynamic incident information from citizens in near-real-time. While high-quality reporting is beneficial, generating such reports can place an additional burden on citizens who are already suffering from the stress of a climate-related disaster. Furthermore, reporting methods are often challenging to use, due to their length and complexity. In this paper, we explore reducing the friction of climate hazard reporting by automating parts of the form-filling process. By building on existing computer vision and natural language models, we demonstrate the automated generation of a full-form hazard impact assessment report from a single photograph. Our proposed data pipeline can be integrated with existing systems and used with geospatial data solutions, such as flood hazard maps.","PeriodicalId":92279,"journal":{"name":"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84066372","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

DIG: The Data Interface Grammar DIG:数据接口语法

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605223

Yiru Chen, Jeffery Tao, Eugene Wu

引用次数: 0

SliceLens SliceLens

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605217

Daniel Kerrigan, Enrico Bertini

{"title":"SliceLens","authors":"Daniel Kerrigan, Enrico Bertini","doi":"10.1145/3597465.3605217","DOIUrl":"https://doi.org/10.1145/3597465.3605217","url":null,"abstract":"SliceLens is a tool for exploring labeled, tabular, machine learning datasets. To explore a dataset, the user selects combinations of features in the dataset that they are interested in. The tool splits those features into bins and then visualizes the label distributions for the subsets of data created by the intersections of the bins. SliceLens guides the user in determining which feature combinations to explore. Guidance is based on a user-selected rating metric, which assigns a score to the subsets created by a given combination of features. The purpose of the metrics are to detect interesting patterns in the subsets, such as subsets that have high label purity or an uneven distribution of errors. SliceLens uses the metrics to guide the user towards combinations of features that create potentially interesting subsets in two ways. First, SliceLens assigns a rating to each feature based on the subsets that would be created by selecting that feature. This incremental guidance can help the user determine which feature to select next. Second, SliceLens can suggest combinations of features ranked according to the chosen metric, which the user can then cycle through.","PeriodicalId":92279,"journal":{"name":"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)","volume":"219 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74658116","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

Aggregation Consistency Errors in Semantic Layers and How to Avoid Them 语义层中的聚合一致性错误及其避免方法

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605224

Zezhou Huang, Pavan Kalyan Damalapati, Eugene Wu

{"title":"Aggregation Consistency Errors in Semantic Layers and How to Avoid Them","authors":"Zezhou Huang, Pavan Kalyan Damalapati, Eugene Wu","doi":"10.1145/3597465.3605224","DOIUrl":"https://doi.org/10.1145/3597465.3605224","url":null,"abstract":"Analysts often struggle with analyzing data from multiple tables in a database due to their lack of knowledge on how to join and aggregate the data. To address this, data engineers pre-specify \"semantic layers\" which include the join conditions and \"metrics\" of interest with aggregation functions and expressions. However, joins can cause \"aggregation consistency issues\". For example, analysts may observe inflated total revenue caused by double counting from join fanouts. Existing BI tools rely on heuristics for deduplication, resulting in imprecise and challenging-to-understand outcomes. To overcome these challenges, we propose \"weighing\" as a core primitive to counteract join fanouts. \"Weighing\" has been used in various areas, such as market attribution and order management, ensuring metrics consistency (e.g., total revenue remains the same) even for many-to-many joins. The idea is to assign equal weight to each join key group (rather than each tuple) and then distribute the weights among tuples. Implementing weighing techniques necessitates user input; therefore, we recommend a human-in-the-loop framework that enables users to iteratively explore different strategies and visualize the results.","PeriodicalId":92279,"journal":{"name":"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77290909","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: 2

Approximate Query Answering over Open Data 开放数据上的近似查询回答

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605227

Mengqi Zhang, Pranay Mundra, Chukwubuikem Chikweze, F. Nargesian, G. Weikum

引用次数: 0

VALUE 价值

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605225

Kaustav Bhattacharjee, Aritra Dasgupta

引用次数: 0

Visualizing a Tabular Data Repository to Facilitate Descriptive Tag Augmentation for New Tables 可视化表格数据存储库以促进新表的描述性标记增强

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI: 10.1145/3597465.3605226

Jianhao Cao, T. Munzner, R. Pottinger

引用次数: 0