{"title":"How 802.1x Enhances Knowledge Extraction from Large Scale Campus WiFi Deployment","authors":"M. A. Setiawan","doi":"10.5220/0008366403910397","DOIUrl":null,"url":null,"abstract":"In recent years, the world has witnessed how internet connectivity is exponentially growing in cities around the world. Universitas Islam Indonesia (UII) as one of biggest private universities in Indonesia is also seeing the similar trend like the rest of the world. With more than 700 high density access points and roughly 30,000 users, most of internet connectivity in campus is provided from WiFi access. After 802.1x WiFi authentication-method deployment, UII saw an opportunity to utilise WiFi metadata as a source of business intelligence. Previously, many business processes or managerial decisions in the university were decided by some hidden assumptions and approximations. These assumptions and approximations sometimes created sub-optimal managerial decisions. To improve the strategic decision, we proposed an evidence-based management based on WiFi data. We utilise this data to extract spatial knowledge, movement behaviour, seamless attendance record, and traffic analysis for marketing purpose. The results show promising result where many of university decision is helped by the result given from the knowledge extraction system. Managements can act faster as information is elicited from tacit knowledge within WiFi metada in real time and more accurate.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0008366403910397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, the world has witnessed how internet connectivity is exponentially growing in cities around the world. Universitas Islam Indonesia (UII) as one of biggest private universities in Indonesia is also seeing the similar trend like the rest of the world. With more than 700 high density access points and roughly 30,000 users, most of internet connectivity in campus is provided from WiFi access. After 802.1x WiFi authentication-method deployment, UII saw an opportunity to utilise WiFi metadata as a source of business intelligence. Previously, many business processes or managerial decisions in the university were decided by some hidden assumptions and approximations. These assumptions and approximations sometimes created sub-optimal managerial decisions. To improve the strategic decision, we proposed an evidence-based management based on WiFi data. We utilise this data to extract spatial knowledge, movement behaviour, seamless attendance record, and traffic analysis for marketing purpose. The results show promising result where many of university decision is helped by the result given from the knowledge extraction system. Managements can act faster as information is elicited from tacit knowledge within WiFi metada in real time and more accurate.
近年来,世界见证了互联网连接在世界各地城市的指数级增长。印尼伊斯兰大学(Universitas Islam Indonesia, UII)作为印尼最大的私立大学之一,也看到了与世界其他地区类似的趋势。校园内有700多个高密度接入点和大约3万用户,大部分互联网连接都是通过WiFi接入提供的。在802.1x WiFi认证方法部署之后,UII看到了利用WiFi元数据作为商业智能来源的机会。以前,大学中的许多业务流程或管理决策都是由一些隐藏的假设和近似决定的。这些假设和近似有时会产生次优的管理决策。为了完善战略决策,我们提出了基于WiFi数据的循证管理。我们利用这些数据提取空间知识、运动行为、无缝考勤记录和流量分析,用于营销目的。结果表明,知识抽取系统的结果对高校决策有一定的帮助。WiFi元数据中的隐性知识可以实时、准确地获取信息,管理层可以更快地采取行动。