FeatherCast

Processing Planetary Sized Datasets – Tim Park

In my group at Microsoft, we have worked with the United Nations, Guide Dogs for the Blind in the UK, several automotive companies, and Strí_er on a number of projects involving high scale geospatial data.

In this talk, I’ll share some of the best practices and patterns that have come out of those experiences: best practices for storing and indexing geospatial data at scale, incremental ingestion and slice processing of the data, and efficiently building and presenting progressive levels of detail.

The audience will walk away with an understanding of how to efficiently summarize data over a geographic area, general methods for doing ingestion with Apache Kafka (or other event ingestion systems), and incremental updates to large scale datasets with Apache Spark, and best practices around visualizing this data on the frontend.

Speakers
Tim Park

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Geospatial Track: Crowd Learning for Indoor Navigation – Thomas Burgess

indoo.rs enables location based services for indoor applications. With indoo.rs, developers can add new features to their products, including having locations trigger events, track assets, showing closest routes to other places. For this, we use WiFi/beacon radio infrastructure, mobile devices and our cloud which produce lots of geospatial time series data. The real-time indoor navigation fuses independent movement from custom 9D sensor fusion and position estimates obtained by comparing current signal readings to a reference map. This talk will discuss how we create and maintain these maps in our big data machine learning system which leverages crowd data through Kafka and Spark to run SLAM and context aware algorithms to create high quality trajectories. In addition to use in reference maps, these trajectories provide an additional input for our interactive analytics.

Speakers
Thomas Burgess

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