Pub Date : 1900-01-01DOI: 10.5220/0005900303190328
Emmanuel Kayode Akinshola Ogunshile
Outdoor advertising is an old industry and the only reliably growing advertising sector other than online advertising. However, for it to sustain this growth, media providers must supply a comparable means of tracking an advertisementâs effectiveness to online advertising. The problem is a continual and emerging area of research for large outdoor advertising corporations, and as a result of this, smaller companies looking to join the market miss out on providing clients with valuable metrics due to a lack of resources. In this paper, we discuss the processes undertaken to develop software to be used as a means of better understanding the potential effectiveness of a fleet of private car, taxi or bus advertisements. Each of the steps present unique challenges including big data visualisation, performance data aggregation and the inherent inconsistencies and unreliabilities produced by tracking fleets using GPS. We cover how we increased the metric aggregation algorithm performance by roughly 20x, built an algorithm and process to render data heat maps on the server side and how we built an algorithm to clean unwanted GPS âjitterâ.
{"title":"A Big Data Analysis System for Use in Vehicular Outdoor Advertising","authors":"Emmanuel Kayode Akinshola Ogunshile","doi":"10.5220/0005900303190328","DOIUrl":"https://doi.org/10.5220/0005900303190328","url":null,"abstract":"Outdoor advertising is an old industry and the only reliably growing advertising sector other than online \u0000 \u0000advertising. However, for it to sustain this growth, media providers must supply a comparable means of \u0000 \u0000tracking an advertisementâs effectiveness to online advertising. The problem is a continual and emerging area \u0000 \u0000of research for large outdoor advertising corporations, and as a result of this, smaller companies looking to \u0000 \u0000join the market miss out on providing clients with valuable metrics due to a lack of resources. In this paper, \u0000 \u0000we discuss the processes undertaken to develop software to be used as a means of better understanding the \u0000 \u0000potential effectiveness of a fleet of private car, taxi or bus advertisements. Each of the steps present unique \u0000 \u0000challenges including big data visualisation, performance data aggregation and the inherent inconsistencies \u0000 \u0000and unreliabilities produced by tracking fleets using GPS. We cover how we increased the metric aggregation \u0000 \u0000algorithm performance by roughly 20x, built an algorithm and process to render data heat maps on the server \u0000 \u0000side and how we built an algorithm to clean unwanted GPS âjitterâ.","PeriodicalId":230522,"journal":{"name":"Proceedings of the 6th International Conference on Cloud Computing and Services Science","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127282839","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}