Abstract
The amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.
Highlights
The amount of data produced by sensors, Internet of Things (IoTs), social and digital media, are rapidly increasing each day [1]
The exemplar is not meant to compete with crowd sourcing Global Positioning System (GPS) apps, but rather serve as a generic exemplar that can be extended to other Big Data Actionable Intelligence problems
In conclusion, our big data architecture provides a framework for machine-learning algorithms to learn and analyze streaming data from heterogenous data sources to turn them into actionable information for decision makers
Summary
The amount of data produced by sensors, Internet of Things (IoTs), social and digital media, are rapidly increasing each day [1]. Actionable intelligence is the level of data analysis where data are analyzed in near-real-time to create insights that support decision making [1]. The traffic prediction problem is extremely complex, which makes it hard to accurately predict traffic condition based on off-line data (patterns, trends, road networks, etc.) or crowdsourcing applications such as Waze [10] due to the dynamic changes of real-time environment (i.e. accidents, sport events, weather changes, etc.). This exemplar highlights the importance of Actionable Intelligence. Time measured from data creation to the time the data has arrived and indexed into our system
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