{"title":"一个非程序员的增强和理解EZ代理日志的指南","authors":"S. Murphy","doi":"10.1108/pmm-08-2019-0034","DOIUrl":null,"url":null,"abstract":"\nPurpose\nLibraries throughout the world use OCLC’s EZproxy software to manage access to e-resources. When cleaned, processed, visualized and enhanced, these logs paint a valuable picture of a library’s impact on researcher’s lives. The purpose of this paper is to share techniques and procedures for enhancing and de-identifying EZproxy logs using Tableau, a data analytics and visualization software, and Tableau Prep, a tool used for cleaning, combining and shaping data for analysis.\n\n\nDesign/methodology/approach\nIn February 2018, The Ohio State University Libraries established an automated daily process to extract and clean EZproxy log files. The assessment librarian created a series of procedures in Tableau and Tableau Prep to union, parse and enhance these files by adding information such as user major, user status (faculty, graduate or undergraduate) and the title of the requested resource. She last stripped the data set of identifiers and applied best practices for maintaining confidentiality to visualize the data.\n\n\nFindings\nThe data set is currently 1.5m rows and growing. The visualizations may be filtered by date, user status and user department/major where applicable. Safeguards are in place to limit data presentation when filters might reveal a user’s identity.\n\n\nOriginality/value\nTableau used in concert with Tableau Prep allows an assessment librarian to clean and combine data from various sources. Once procedures for cleaning and combining data sources are established, the data driving visualizations can be set to refresh on a set schedule. This expedites the ability of librarians to derive actionable insights from EZproxy data and to share the library’s positive impact on researcher’s lives.\n","PeriodicalId":44583,"journal":{"name":"Performance Measurement and Metrics","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/pmm-08-2019-0034","citationCount":"0","resultStr":"{\"title\":\"A non-programmers guide to enhancing and making sense of EZ Proxy logs\",\"authors\":\"S. Murphy\",\"doi\":\"10.1108/pmm-08-2019-0034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nLibraries throughout the world use OCLC’s EZproxy software to manage access to e-resources. When cleaned, processed, visualized and enhanced, these logs paint a valuable picture of a library’s impact on researcher’s lives. The purpose of this paper is to share techniques and procedures for enhancing and de-identifying EZproxy logs using Tableau, a data analytics and visualization software, and Tableau Prep, a tool used for cleaning, combining and shaping data for analysis.\\n\\n\\nDesign/methodology/approach\\nIn February 2018, The Ohio State University Libraries established an automated daily process to extract and clean EZproxy log files. The assessment librarian created a series of procedures in Tableau and Tableau Prep to union, parse and enhance these files by adding information such as user major, user status (faculty, graduate or undergraduate) and the title of the requested resource. She last stripped the data set of identifiers and applied best practices for maintaining confidentiality to visualize the data.\\n\\n\\nFindings\\nThe data set is currently 1.5m rows and growing. The visualizations may be filtered by date, user status and user department/major where applicable. Safeguards are in place to limit data presentation when filters might reveal a user’s identity.\\n\\n\\nOriginality/value\\nTableau used in concert with Tableau Prep allows an assessment librarian to clean and combine data from various sources. Once procedures for cleaning and combining data sources are established, the data driving visualizations can be set to refresh on a set schedule. This expedites the ability of librarians to derive actionable insights from EZproxy data and to share the library’s positive impact on researcher’s lives.\\n\",\"PeriodicalId\":44583,\"journal\":{\"name\":\"Performance Measurement and Metrics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2019-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1108/pmm-08-2019-0034\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Performance Measurement and Metrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/pmm-08-2019-0034\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance Measurement and Metrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/pmm-08-2019-0034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
A non-programmers guide to enhancing and making sense of EZ Proxy logs
Purpose
Libraries throughout the world use OCLC’s EZproxy software to manage access to e-resources. When cleaned, processed, visualized and enhanced, these logs paint a valuable picture of a library’s impact on researcher’s lives. The purpose of this paper is to share techniques and procedures for enhancing and de-identifying EZproxy logs using Tableau, a data analytics and visualization software, and Tableau Prep, a tool used for cleaning, combining and shaping data for analysis.
Design/methodology/approach
In February 2018, The Ohio State University Libraries established an automated daily process to extract and clean EZproxy log files. The assessment librarian created a series of procedures in Tableau and Tableau Prep to union, parse and enhance these files by adding information such as user major, user status (faculty, graduate or undergraduate) and the title of the requested resource. She last stripped the data set of identifiers and applied best practices for maintaining confidentiality to visualize the data.
Findings
The data set is currently 1.5m rows and growing. The visualizations may be filtered by date, user status and user department/major where applicable. Safeguards are in place to limit data presentation when filters might reveal a user’s identity.
Originality/value
Tableau used in concert with Tableau Prep allows an assessment librarian to clean and combine data from various sources. Once procedures for cleaning and combining data sources are established, the data driving visualizations can be set to refresh on a set schedule. This expedites the ability of librarians to derive actionable insights from EZproxy data and to share the library’s positive impact on researcher’s lives.
期刊介绍:
■Quantitative and qualitative analysis ■Benchmarking ■The measurement and role of information in enhancing organizational effectiveness ■Quality techniques and quality improvement ■Training and education ■Methods for performance measurement and metrics ■Standard assessment tools ■Using emerging technologies ■Setting standards or service quality