Modern telecommunications are alphabet soups that produce massive amounts of diagnostic data. One particular difficulty in telecom analytics is that the data to be analyzed is produced by mobile entities and reported via ever-changing data transports. The resulting mess can be hard to understand from any single viewpoint, but looking at all of the data together isn’t all that hard if you aren’t afraid of data at scale.
Ted Dunning offers an overview of a real-time, low-fidelity simulation of the edge protocols of such a system to help illustrate how modern big data tools can be used for telecom analytics. Ted demos the system and shows how to analyze the data produced by the simulator using a variety of big data tools including Spark.
Ted Dunning is the chief technology officer at MapR, an HPE company. He’s also a board member for the Apache Software Foundation, a PMC member, and committer on a number of projects. Ted has years of experience with machine learning and other big data solutions across a range of sectors. He’s contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library and designed the t-digest algorithm used in several open source projects and by a variety of companies. Previously, Ted was chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics (LifeLock). Ted has coauthored a number of books on big data topics, including several published by O’Reilly related to machine learning, and has 24 issued patents to date plus a dozen pending. He holds a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.
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