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Haim Koshchitzky of Sys-Con Media recently wrote, “Enterprise applications can ‘live’ in many places and their logs might be scattered and unstandardized. First generation log analysis tools made some of the log data searchable, but the onus was on the developer to know what to look for. That process could take many hours, potentially leading to unacceptable downtime for critical applications. Proprietary log formats also confuse and confound conventional keyword search. That’s why semantic search can be so helpful. It uses machine intelligence to understand the context of words, so it becomes possible for a Google user to type ‘cheap flights to Tel Aviv on February 10th’ rather than just ‘cheap flights’ and receive a listing of actual flights rather than links to airline discounters. Bing Facebook, Google and some vertical search engines include semantic technology to better understand natural language. It saves time and creates a better experience.”

 

He continues, “The time saving is even more dramatic for developers who are using log analysis tools with this capability. The challenge of reading proprietary logs is immediately solved by virtue of the semantic ontology, so it’s no longer necessary to examine each console separately. Machine intelligence also augments the ability of the developer to find answers within minutes instead of hours. It’s called intelligence amplification. U.S. military researchers first conceived of intelligence amplification during the ’50s and ’60s. It’s now being put into practice by applying semantic search principles to apps.”

 

Read more here.

 

Image: Courtesy IntelFreePress