Machine data is all around us: logs, sensors, GPS devices and meters to name a few. The enormous growth of machine data has become a major driver of big data solutions and a challenge for many organizations. The complex and diverse nature of machine data leaves many organizations unable to leverage
Cities are temporal rhythms of patterned activity. Just as each community has a distinctive spatial footprint when viewed from space, each has an ambient footprint.
You can sense a city's ambient footprint at a coarse or a fine grain. At coarse granularity, you can sense it at night when the
In today’s increasingly connected world, machine data analysis is becoming a business imperative. While managing it may be challenging, opportunities abound across multiple industries for those who can tackle this complex data.
"Anyone who makes assertions and is unwilling to engage in a discussion or provide evidence for what they say, is probably someone who doesn't really know what they're talking about. Be very skeptical."
That's the advice from Tom Deutsch, program director of big data and analytics at IBM. In this
This week’s Friday Data Flick gives you insight into “operations analysis,” which is one of the top five uses (also called “use cases”) for big data. Operations analysis is about analyzing a variety of machine data to get improved business results. The key is combining machine and business data,
Join Vijay Ramaiah, product manager for IBM big data, as he discusses the new class of big data applications that are delivering new operational insights by analyzing huge volumes of machine data. This is the third in our series examining popular use cases for big data.
For more examples of
Dirk de Roos, IBM's technical sales lead for Big Data discusses the Machine Data Accelerator, a set of building blocks designed to help customers extract value from an important source of Big Data - machine generated data including sensor data and log data.
Quality-of-service (QoS) is one of the most paradoxical metrics in the telecommunications industry. “Quality” of the customer experience is normally measured through surveys and logged feedback, but plenty of data can lead to good quantitative measures.