Overview of turn data management platform for digital advertising

Elmeleegy, Hazem; Li, Yinan; Qi, Yan; Wilmot, Peter; Wu, Mingxi; Kolay, Santanu; Dasdan, Ali; Chen, Songting

doi:10.14778/2536222.2536238

Cited by 25 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Demand-side platforms (DSPs) are intermediaries, who provide the technology to bid for advertisers on such advertising exchanges. A number of papers describe bidding strategies and heuristics in display ad auctions (Feldman et al, 2008;Ghosh et al, 2009;Chen et al, 2011;Elmeleegy et al, 2013;Zhang et al, 2014Zhang et al, , 2016. Zhang et al (2014) gives an up-to-date overview.…”

Section: Introductionmentioning

confidence: 99%

Truthfulness and Approximation with Value-Maximizing Bidders

Fadaei

Bichler

2016

Algorithmic Game Theory

View full text Add to dashboard Cite

In markets such as digital advertising auctions, bidders want to maximize value rather than payoff. This is different to the utility functions typically assumed in auction theory and leads to different strategies and outcomes. We refer to bidders who maximize value as value bidders. While simple single-object auction formats are truthful, standard multi-object auction formats allow for manipulation. It is straightforward to show that there cannot be a truthful and revenue-maximizing deterministic auction mechanism with value bidders and general valuations. Approximation has been used as a means to achieve truthfulness, and we study which approximation ratios we can get from truthful approximation mechanisms. We show that the approximation ratio that can be achieved with a deterministic and truthful approximation mechanism with n bidders and m items cannot be higher than 1/n for general valuations. For randomized approximation mechanisms there is a framework with a ratio of O( √ m ǫ 3 ) with probability at least 1 − ǫ, for 0 < ǫ < 1. We provide better ratios for environments with restricted valuations.

show abstract

Section: Introductionmentioning

confidence: 99%

Truthfulness and Approximation with Value-Maximizing Bidders

Fadaei

Bichler

2016

Algorithmic Game Theory

View full text Add to dashboard Cite

show abstract

“…The Kangaroo system is implemented at Turn Inc to serve as part of its Data Management Platform (DMP) [12]. Turn DMP is a cloud-based service, particularly built for digital marketers.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The second component, which is the query engine, uses the partition selector to decide which partitions need to be scanned to answer a given query. The query engine is based on the Cheetah system [6], the data warehousing component used in Turn Data Management Platform (DMP) [12]. In addition to answering incoming queries, the query engine also logs all those queries, so that they can be later used by the partition planning process.…”

Section: System Architecturementioning

confidence: 99%

See 1 more Smart Citation

Kangaroo

Aly

Elmeleegy

et al. 2016

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Self Cite

View full text Add to dashboard Cite

Despite the importance and widespread use of range data, e.g., time intervals, spatial ranges, etc., little attention has been devoted to study the processing and querying of range data in the context of big data. The main challenge relies in the nature of the traditional index structures e.g., B-Tree and R-Tree, being centralized by nature, and hence are almost crippled when deployed in a distributed environment. To address this challenge, this paper presents Kangaroo, a system built on top of Hadoop to optimize the execution of range queries over range data. The main idea behind Kangaroo is to split the data into non-overlapping partitions in a way that minimizes the query execution time. Kangaroo is query workloadaware, i.e., results in partitioning layouts that minimize the query processing time of given query patterns. In this paper, we study the design challenges Kangaroo addresses in order to be deployed on top of a distributed file system, i.e., HDFS. We also study four different partitioning schemes that Kangaroo can support. With extensive experiments using real range data of more than one billion records and real query workload of more than 30,000 queries, we show that the partitioning schemes of Kangaroo can significantly reduce the I/O of range queries on range data.

show abstract

“…Some of the commercial solutions related to identifying a user's interests are provided by Lotame 1 , Oracle BlueKai DMP 2 , Adobe Audience Manager 3 and Salesforce DMP (formerly Krux) 4 . These solutions typically organize the marketers' audience data into categories and taxonomies of user-interests and segment the audience to generate various insights.…”

Section: Related Workmentioning

confidence: 99%

Identifying Short-term Interests from Mobile App Adoption Pattern

Gaind

Varshney

Goel

et al. 2019

CyS

View full text Add to dashboard Cite

With the increase in an average user's dependence on their mobile devices, the reliance on collecting his browsing history from mobile browsers has also increased. This browsing history is highly utilized in the advertising industry for providing targeted ads in the purview of inferring his short-term interests and pushing relevant ads. However, the major limitation of such an extraction from mobile browsers is that they reset when the browser is closed or when the device is shut down/restarted; thus rendering existing methods to identify the user's short-term interests on mobile devices users, ineffective. In this paper, we propose an alternative method to identify such short-term interests by analysing their mobile app adoption (installation/uninstallation) patterns over a period of time. Such a method can be highly effective in pinpointing the user's ephemeral inclinations like buying/renting an apartment, buying/selling a car or a sudden increased interest in shopping (possibly due to a recent salary bonus, he received). Subsequently, these derived interests are also used for targeted experiments. Our experiments result in up to 93.68% higher click-through rate in comparison to the ads shown without any user-interest knowledge. Also, up to 51% higher revenue in the long term is expected as a result of the application of our proposed algorithm. 1

show abstract

Overview of turn data management platform for digital advertising

Cited by 25 publications

References 10 publications

Truthfulness and Approximation with Value-Maximizing Bidders

Truthfulness and Approximation with Value-Maximizing Bidders

Kangaroo

Identifying Short-term Interests from Mobile App Adoption Pattern

Contact Info

Product

Resources

About