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IBM > Case Studies > Darwin Ecosystem: Accelerating discovery and insight through cutting-edge big data and cognitive technologies
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Darwin Ecosystem: Accelerating discovery and insight through cutting-edge big data and cognitive technologies

Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
  • Software
Applicable Functions
  • Business Operation
Use Cases
  • Edge Computing & Edge Intelligence
  • Predictive Maintenance
  • Real-Time Location System (RTLS)
Services
  • Cloud Planning, Design & Implementation Services
  • Data Science Services
The Challenge
Darwin Ecosystem was founded with a unique vision of harnessing chaos theory mathematics to uncover previously hidden connections in unstructured data. The company’s algorithms can look at all the data generated by any source (such as news, RSS feeds and Twitter), and analyze how a specific set of concepts within that data are evolving over time. This is particularly valuable in situations such as business and competitive intelligence, social research, brand monitoring, legal discovery, risk mitigation and even law enforcement. A common problem in these areas is that a regular web search will only turn up the all-time most popular answers to a given question – but what the expert researcher is actually interested in is the moment-tomoment evolution of the data available on that topic. Darwin’s algorithm is computationally intensive, and the sources of data it correlates can be vast. To bring its benefits to a larger commercial audience, Darwin needed to find a way to make it scale.
About The Customer
Founded in 2007, Darwin Ecosystem creates innovative data exploration, discovery and research tools. Its temporal organic curation algorithms help people, businesses and governments gain awareness and insight into constantly evolving topics across multiple sources of unstructured data. The company was founded with a unique vision of harnessing chaos theory mathematics to uncover previously hidden connections in unstructured data. In layman’s terms, the company’s algorithms can look at all the data generated by any source (such as news, RSS feeds and Twitter), and analyze how a specific set of concepts within that data are evolving over time. This is particularly valuable in situations such as business and competitive intelligence, social research, brand monitoring, legal discovery, risk mitigation and even law enforcement.
The Solution
The Darwin team was invited to IBM’s Thomas Watson Research Center in New York to see how IBM® InfoSphere® Streams and SoftLayer® technologies could transform its business. IBM InfoSphere Streams is specifically designed to distribute large volumes of incoming data efficiently across the available computing resources, and process it in real time. As a result, while testing the solution, Darwin saw a 10:1 performance increase in the time spent fetching, correlating and delivering data, compared to its existing open source streaming tools. Darwin moved its existing clients’ environments and other applications from another cloud provider’s platform to virtual machines running on a dedicated SoftLayer platform, and also set up a bare-metal SoftLayer environment to support its new API. The SoftLayer environments are hosted at a data center in the US, and provide high-performance, low-latency access to all of Darwin’s services from anywhere in the world.
Operational Impact
  • A correlation task that used to take ten minutes using legacy open source streaming tools can be completed by IBM InfoSphere Streams in 3.2 seconds – even though the volume and velocity of data have tripled.
  • With performance like this, Darwin can deal with thousands of requests and process gigabytes of data every day – enabling it to grow its user-base by several orders of magnitude without significantly increasing its costs.
Quantitative Benefit
  • 6-8 seconds to summarize new information from hundreds of articles, blogs and tweets
  • 10 times faster results with IBM technology, compared to open source streaming tools
  • Hours saved by replacing manual research with near-real-time analysis of information

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