First, the EMBERS system architecture was designed to support collaboration from the outset. Three key considerations motivated the design of EMBERS. “TO ADDRESS THESE NEEDS, THE SYSTEM IS COMPOSED OF MANY SIMPLE INDEPENDENT COMPONENTS STRUNG TOGETHER IN A PIPES-AND-FILTERS ARCHITECTURE.” Much of the system is designed to look for precursor signals in social media streams and use these indicators to drive statistical and machine learning algorithms that generate the predictions. The system processes a range of data, from high-volume, high-velocity, noisy open-source media such as Twitter to lower-volume, higher-quality sources, such as economic indicators. It has been operational and delivering predictions (and continues to) since November 2012. For civil unrest, EMBERS produces detailed forecasts about future events, including the date, location (to within a city resolution), type of event (e.g., whether it is a protest for wages or a protest for safety), and protesting population (e.g., educators, factory workers, doctors), along with uncertainties involved in the forecasts. The classes of events EMBERS is designed to forecast include influenza-like illness case counts, rare disease outbreaks, elections, domestic political crises, and civil unrest (we focus in this article primarily on civil unrest). EMBERS is supported by the Intelligence Advanced Research Project Activity (IARPA) Open Source Indicators (OSI) program. Early Model Based Event Recognition using Surrogates (EMBERS) 1 is an anticipatory intelligence system for forecasting socially significant population-level events, such as civil unrest incidents, disease outbreaks, and election outcomes, on the basis of publicly available data. One of the promising themes in this space is the idea of harnessing open-source datasets to identify threats and support decision making for national security, law enforcement, and intelligence missions. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years.Ī nticipatory intelligence is considered to be one of the next frontiers of “big data” research, wherein myriad data streams are fused together to generate predictions of critical societal events. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. It is deployed on Amazon Web Services using an entirely automated deployment process. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. We have successfully refactored existing projects from async to sync without much trouble.Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. Mixing sync and async can surprise developers in exactly the same ways we were trying avoid by using only sync relationships in the first place. We've found that sync relationships work best when you're able to use them for every relationship in your project (Storefront actually has an option to enforce this).
There are some more goodies in there like run-time development assertions, so be sure to check back on it regularly as we continue to develop it. With sync relationships, this CP always works. In this post I'll outline why we think using relationships we would've needed to be aware of calls to this.get('comments') and comment.get('author') returning promise proxies.
If you've spent any time around Sam and me, you've probably heard us talk about our frustrations with Ember Data's async relationships.
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