Joining real-time structured and unstructured data feeds for better accuracy and reliability from your operational intelligence, and the Text Analytics Summit, 2012, London.
Three IT trends have emerged over the past year – Big Data, real-time and the importance of unstructured data. Taking the latter first, there is an increasing awareness that much of the data we have available to us today is unstructured (Cloudera amongst the many claiming 80% of all data is unstructured). Unstructured data includes text messages, documents, tweets emails and video content. There’s also a growing industry for tools and software that perform unstructured data analytics – primarily text analytics using semantic modeling, tagging and subsequent analysis.
The past year has also seen Big Data and Hadoop emerge from the rarefied atmosphere of California’s Silicon Valley into mainstream IT. Driven by statistics such as 90% of all data available today has been generated in the past two years, Big Data as a functional area for primarily unstructured data is here to stay, and is effectively supercomputing lite for the masses.
The need for real-time streaming data management
However, the real-time trend is less well served today by either Hadoop or by the currently available tools and software for unstructured data analytics. Real-time is about the need for immediate detection and response – turning data sources into live data feeds, and processing the data on the fly, then loading batch based distributed platforms such as Hadoop as an output data stream.
I’ve also seen the term ‘stream reasoning’ used to describe the real-time processing of unstructured data, although this is still an area that is less well developed and understood than the more mainstream text analytics from stored data. ‘Streaming Reasoning’ is the ability to process and respond to semantic knowledge about tweets, messages and other social media interaction in real-time, on the fly. The diagram below illustrates how a semantic modeling library has been plugged into a real-time streaming pipeline in SQLstream – the example is based on SQLstream’s GATE UDX but any library with reasonable performance and a query response API can be plugged in.
Unstructured data feeds, such as text messages and tweets, are streamed through the semantic tagging UDX and library, with the output of this stage being real-time streams of semantic tagged data. The data can then be analyzed and frequency charted in real-time.
Text Analytics Summit, 2012, London
I’ll be speaking on this topic at the Text Analytics Summit, 2012, London. I’ll be discussing how to combine streaming reasoning (admittedly, mostly Twitter messages) with structured data, with the objective of improving the overall accuracy and reliability of the resulting operational intelligence. I’ll be using a couple of examples – customer experience management for IP content services such as VoIP and VoD, and also improving the accuracy and reliability of traffic congestion information and travel time information – how can text analysis of tweets and messages help to pinpoint the severity of road network traffic problems.
Look forward to seeing you there, or if you can’t make, I’ll be blogging on the highlights next week.