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Semantic Computing for Analytics – Lessons from the Automotive Industry
One of the huge oncoming challenges for information technology is the critical need for analytics that can make sense of “big data”. The diversity, heterogeneity and sheer volume of data along with inherent errors and gaps require new solutions that utilize intelligence and context to help solve this problem. Semantic computing and its related technologies is one such solution. In this talk I will discuss how the application of semantic technologies is making a significant difference in how analytics can be used to make sense of big data. Technologies such as natural language processing, ontologies, text analytics, machine translation, machine learning and others are being utilized to help understand data and use that knowledge to solve real-world problems. The automotive industry has a wealth of data that cannot be understood easily and analyzed without adding intelligence and context to the process. I will discuss how semantic computing is currently being used in a variety of different automotive domains including manufacturing, vehicle assembly and quality. More importantly, we will look at the current trends in semantic computing and try to see where they will lead to in the future.
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