Streaming analytics is giving developers the ability to accelerate the time to value provided by applications designed for the Internet of Things. Discover how IBM Streams 4.2 is connecting developers with the capabilities of advanced data analytics solutions, helping them keep pace with the ever-
Spark just seems to be getting big play everywhere in the technology arena. What is Spark? And do you need it? Get a good glimpse into its in-memory execution capabilities, some of its key components, its integrations and its availability as a service.
University of Montana researchers maximized analytics to improve outcomes through highly effective and prompt brain trauma treatment and accelerated experimental research. Analytics also enabled quick identification of patients likely to develop post-traumatic epilepsy.
When customers or other key stakeholders expect to be able to connect with an organization instantaneously, extremely low latency, high throughput data and analytics flows and execution are absolutely essential. The advent of the Internet of Things is among several key drivers of the emergence of
Streaming analytics is becoming ubiquitous as data streams from a range of sources, including the Internet of Things, are now mainstream. Although streaming analytics is not a new technology, it is well suited for today’s real-time, low-latency business and consumer applications. And today’s data
Time series data can contain highly valuable insights—if organizations can detect and classify the events within it. An approach that combines stream processing and machine learning holds the key to analyzing large, fast data streams.
Even when learning a new language, becoming fluent within certain contexts can be easier than other contexts. When analyzing textual data, context is imperative to understand that data. And like corpora developed for linguistics research, a simple and straightforward conversion of textual data