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Grab’s Trust, Identity and Safety team detects fraud by deploying data science, analytics, and engineering platform to search for anomalous and suspicious transactions, or by identifying high-risk individuals who are likely to commit fraud. The team builds tools for managing data feeds, creates SDK for engineering integration, and builds rules engines and consoles for fraud detection. An example of fraudulent behavior could be that of an individual who pretends to be both the driver and passenger, and makes cashless payments to get promotions. Recently, the team launched GrabDefence as a SaaS service to start helping external clients in Southeast Asia to combat fraud in their business. Billions of fraud and safety detections are performed daily as there are millions of transactions happening every day and thus storing and querying the data of a database in real-time is not feasible. So come listen to us how we use Apache Kafka to detect fraud successfully!

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The Open Landscape of Geospatial Information: Open data, open source, open standards Presented at ASPRS GeoTech 2013 conference: http://www.asprspotomac.org/geotech2013/ Abstract: The many dimensions of "open" provides users with higher quality geospatial information. Open Standards ensures interoperability to information whether its served by proprietary or open source software. Open Source software benefits the development of open standards and leads to a business ecosystem that includes more providers, more partnerships and more customers.[1] In the end the user does not care if the code is open or proprietary. Users care about access to data and the quality of the data. Open Data has advanced with the recent policies from GEOSS Data-CORE [2] and the US Open Government Initiative [3]. Open Earth Observation data from government sources benefits industry and users. Open standards, Open source and Open data can result in higher quality information. The fusion of data from multiple sources results in higher quality. Fusion is possible based on multiple data sources that can be interrelated [4]. Improving Data Quality through knowing the uncertainty and the provenance of derived information is dependent upon an open landscape of geospatial information. [1] http://wiki.osgeo.org/wiki/Open_Source_and_Open_Standards [2] http://www.earthobservations.org/geoss_dsp.shtml [3] http://www.whitehouse.gov/open [4] http://www.opengeospatial.org/projects/initiatives/fusion2

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The Geographic Information System (GIS), industry is booming, especially with the continued reliance on online maps and the rise of location-aware mobile devices. GIS tech can be one of the key players in the mobile internet, big data, and the internet of things, and is an essential tool for the next generation of the global IT industry. Yet, the GIS community is not prepared. With all the data available, GIS experts lack an off-the-shelf solutions to manage the growing volume of spatial data. Relational spatial databases (RSDB) were the leader in this field for decades, but RSDBs have failed to innovate to handle massive volumes of data coming in at high velocity. Fortunately, MongoDB a useful tool for this challenge, but needs some tooling to create a connector to the GIS tech ecosystem. In order to bridge the gap, we built a pipeline to comply with the architecture of the Geospatial Data Abstraction Library (GDAL), so that MongoDB can work with most of popular GIS tools such as OpenLayers, Mapserver, GeoServer, QGIS, ArcGIS and others with ease. In this talk, I'll go through this pipeline tool and showcase some examples of how you can use this in your next application.

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