Pub Date : 2013-10-01DOI: 10.1109/LDAV.2013.6675170
Ling Huang
In recent years online display advertising has grown at a rapid pace. Genome from Yahoo! is the big data buying solution for online display advertising. The goal of our platform is to identify the best opportunity to display an ad to a user who is most likely to take a desired action. Our system contains websites which are visited by several million users per day. The number of attributes related to user events is also of the order of several thousand. Visual analysis has emerged as a powerful technique to facilitate demonstrating data, filtering extreme cases and outliers, exploiting data details, and identifying data analysis tasks. With respect to large-scale online data, the paper presents some use cases on visual analysis at Genome from Yahoo!
{"title":"Visual analysis on online display advertising data","authors":"Ling Huang","doi":"10.1109/LDAV.2013.6675170","DOIUrl":"https://doi.org/10.1109/LDAV.2013.6675170","url":null,"abstract":"In recent years online display advertising has grown at a rapid pace. Genome from Yahoo! is the big data buying solution for online display advertising. The goal of our platform is to identify the best opportunity to display an ad to a user who is most likely to take a desired action. Our system contains websites which are visited by several million users per day. The number of attributes related to user events is also of the order of several thousand. Visual analysis has emerged as a powerful technique to facilitate demonstrating data, filtering extreme cases and outliers, exploiting data details, and identifying data analysis tasks. With respect to large-scale online data, the paper presents some use cases on visual analysis at Genome from Yahoo!","PeriodicalId":266607,"journal":{"name":"2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134312965","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}
Pub Date : 2013-06-06DOI: 10.1109/LDAV.2013.6675160
Matthieu Dorier, R. Sisneros, T. Peterka, Gabriel Antoniu, B. D. Semeraro
Reducing the amount of data stored by simulations will be of utmost importance for the next generation of large-scale computing. Accordingly, there is active research to shift analysis and visualization tasks to run in situ, that is, closer to the simulation via the sharing of some resources. This is beneficial as it can avoid the necessity of storing large amounts of data for post-processing. In this paper, we focus on the specific case of in situ visualization where analysis codes are collocated with the simulation's code and run on the same resources. It is important for an in situ technique to require minimum modifications to existing codes, be adaptable, and have a low impact on both run times and resource usage. We accomplish this through the Damaris/Viz framework, which provides in situ visualization support to the Damaris I/O middleware. The use of Damaris as a bridge to existing visualization packages allows us to (1) reduce code moditication to a minimum for existing simulations, (2) gather capabilities of several visualization tools to offer a unified data management interface, (3) use dedicated cores to hide the run time impact of in situ visualization and (4) efficiently use memory through a shared-memory-based communication model. Experiments are conducted on Blue Waters and Grid'5000 to visualize the CM1 atmospheric simulation and the Nek5000 CFD solver.
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