Presenter: Dr. Haruna Isah, Queen’s University
Topic: A Multilevel Streaming Data Analytics Infrastructure for Predictive Analytics
There has been a considerable interest in developing systems for processing continuous data streams with the increasing need for real-time analytics for decision support in business, healthcare, manufacturing, security, and internet of things. Some of the data processed through streaming data processing systems need further processing for which most systems currently store the data on the disk and re-load the data in memory for the next level of processing. Storing re-loading of large streaming data incurs compute and storage overhead. We are developing a real-time multilevel streaming data analytics infrastructure using cutting edge streaming data processing engines, in-memory data structure and analytics tools which will preprocess, filter and store necessary data in the memory for processing at the next level. The challenges include selection of the right tools, in-memory storage structures, and data analytics engines and building seamless data analytics pipelines. So far we have implemented an initial infrastructure that combines state of the art scalable and distributed frameworks for data stream processing. We have also been exploring the most popular stream processing engines, in-memory graph structures and data analytics engines in terms of scalability, functionality and efficiency in processing large streaming data. Currently we are working on implementing use case analytics pipelines for our industry partner Gnowit. This presentation is aimed at showcasing our preliminary framework with open source data and is targeted for individuals and organizations that are planning to optimize their current infrastructure to embrace multilevel streaming data analytics. We will also highlight how the SOSCIP Cloud platform and the technical support staff have been supporting us in achieving the project objectives.
About Dr. Isah
A Postdoctoral Fellow at Queen’s University working on streaming data analytics. Received a PhD from Bradford University, UK. Currently with the Big-Data Analytics and Management Laboratory (BAM Lab) of the School of Computing at Queen’s University. My project is titled “A Multilevel Streaming Data Analytics Infrastructure for Predictive Analytics” and Dr Farhana H. Zulkernine is the project supervising PI.
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