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Telecom network switches, network servers and other equipment generate and store large amounts of data every day. The data is mainly used for billing and network operations, If utilized fully, this data can have an enormous impact on network operations and overall profitability.
Many communications service providers (CSP) do not have the tools to mine this data quickly and deeply enough to realize its value. Tools are being used that are usually home grown and not scalable. Valuable information is being lost—information that could be used to predict network issues rather than respond to them after the fact. The alternative of a full analytic database can be cost-prohibitive.
By applying big data tools and predictive analytics upstream of the database, CSPs can move from reactive to proactive use of the data. Network quality problems can be identified in minutes rather than days. By analyzing all the data, analytics tools can pinpoint root cause and suggest corrective actions. Finding and fixing these issues more quickly leads to higher call quality, more profitable service and increased customer satisfaction.
This session is sponsored by Pervasive
As Chief Technologist for Pervasive DataRush, Jim is responsible for the architectural design of Pervasive DataRush™, a software development platform that lets you rapidly build parallel applications that deliver high throughput on big data, fully leveraging the parallel processing capabilities of multicore processors in a multicore server, SMP machine, cluster or Hadoop cluster. (seewww.pervasivedatarush.com).
Jim has 20 years of large-scale software development experience in roles including development manager, software architect, and principal engineer. Prior to joining Pervasive, Jim was Software Development Manager for NexQL, as Director of Software Architecture for Voyence, as Software Development Principal for Net Perceptions/KD1, and as Senior Software Engineer for Convex Computer. Jim also held senior technical positions with Sequel Systems and E-Systems.
Jim is an active public speaker, including presentations on scalable, parallel architectures for big data challenges at Big Data Camp, JavaOne, QCon, and the AMD Fusion Developer Summit. Jim is a dataflow innovator and has authored articles including “Dataflow Programming: Handling Huge Data Loads Without Adding Complexity” in Dr. Dobb’s Journal, “How to Enhance Existing Applications with Embedded Analytics” in eWeek, and “Dataflow Programming: A Scalable Data-Centric Approach to Parallelism,” “Crunching Big Data with Java” and “Let the Data Flow” in Java Developers Journal. He holds a B.Sc. (Cum Laude) in Computer Science from Nicholls State University.
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