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Data Pipeline Performance

If data scientists had their way, we would buy petabytes of memory and never think about IO performance or serialization ever again. Such is the influence of accountants, however, that few companies have realized this otherwise brilliant strategy; instead, whether the workflow is written in Hadoop or in bash, it usually amounts to “stream data in,...

Fast Indirect Sorting in Java

Fast indirect sorting in Java I was recently writing some performance-sensitive code in which I had a double array of distances (one per element), and I wanted to get a list of elements sorted by distance: double[] distances = { d1, d2, ..., dN }; Element[] elements = { e1, e2, ..., eN }; // do...

Investigating Low Quality Location Data #2 - Suspicious Activity Over Greenland

Note: For an in-depth, technical look into the research process, please refer to the lab notes companion to this post. A significant percentage of locations reported in the mobile ad ecosystem - anywhere from 30% to 70% - is of insufficient quality for use in location based mobile ad targeting, measurement, or analytics. In our previous...

Investigating Low Quality Location Data #2 - Suspicious Activity Over Greenland - Lab Notes

Note: This is a companion post to Investigating Low Quality Location Data #2 - Suspicious Activity Over Greenland Audience data validation is a crucial part of delivering accurate behavioral profiles. After seeing a suspiciously high number of mobile ads with locations over Greenland, the Arctic Circle, and the middle of the ocean, the curious engineers at...

Investigating Various Pathologies of Low Quality Location Data #1 - App Permissions

Note: This article has also been published in GeoMarketing here A significant percentage of location data in the mobile ad ecosystem - anywhere from 30% - 70% - is of insufficient quality for appropriate use in location based mobile ad targeting, measurement, or analytics. In a previous post, Validating Mobile Ad Location Data at Factual, we...