Redefining Art of Analyzing Medical Machine Logs: Glassbeam Q2 Milestones and Momentum

Puneet Pandit
Jul 17, 2018

Most would consider analytics a science. The Glassbeam team considers analytics an art of combining impermeable truth from machine logs with deep healthcare domain expertise.  As we expand our penetration of the healthcare market after spending years in the data center world, where the gold standard for machine uptime was 99.999 percent, we have recognized a huge opportunity since the acceptable machine uptime for medical equipment ranged from 90 to 97 percent.

Data Doesn’t Lie: 5 Ways Hospitals Can Use Machine Log Data

Pawan Jheeta
Jul 04, 2018

Maximizing uptime of diagnostic equipment is vital to both patients and healthcare organizations. As medical imaging equipment becomes more sophisticated and the need for healthcare organizations to improve their availability becomes more acute, so does the value of machine log data and advanced analytics. Here, we’ve listed several important ways that hospitals can use machine log data and predictive/prescriptive analytics to optimize operational efficiency and revenues.

7 Must Have Items on Your Checklist Before You Consider Machine Data Analysis

Vijay Vasudevan
Jun 05, 2016

These are the 7 key factors that highlight whether performing machine data analysis realizes credible value for you.

1. Prepare the raw data —

Most of machine data are in the form of logs. Industrial machines are constantly producing valuable operational data (call-home data) on configuration, performance, usage, and other important parameters that define the very life of the device in the field.

Geting the most out of akka clusters

Aug 14, 2015

Anyone serious about distributed systems or building one, commonly encounters issues such as replication, consistency, availability and partition tolerance (CAP) [1]. In a real life scenario, partition tolerance is inevitable. So the system must be able to handle partition tolerance when there are network outages. Therefore, ‘P’ in the CAP is a must for any distributed system. This has been backed by Peter Deutsch in his (EIGHT FALLACIES OF DISTRIBUTED COMPUTING).

What the spark!

Feb 13, 2015

No, we don’t hate Spark! We like it a lot – regular blog readers know that we integrated Apache Spark’s MlLib library into our SCALAR product last Fall. If you missed this news, here’s the PRESS RELEASE and BLOG POST.

Coverage by techrepublic and the 451 group

Dec 30, 2014

A couple of cool media mentions is probably the ideal way to round out what has been a tremendous year at Glassbeam. It sets the stage for what we (along with most media pundits) believe will be a banner year for the IOT industry in 2015.

it’s a new era at glassbeam!

Nov 18, 2014

At Glassbeam we are thrilled to ANNOUNCE our foray into the exciting space of machine learning and real time analytics. By integrating our market-leading Glassbeam SCALAR platform with the powerful capabilities of Apache Spark framework, we are adding significant differentiators to our IOT Analytics platform. This announcement received tons of media attention – here’s a nice BLOG POST by the Taneja Group.

Introducing glassbeam 4.2

Sep 12, 2014

We are thrilled to announce the release of Glassbeam 4.2. – the latest version of our IOT platform.

For those new to GLASSBEAM – our Hyper scale platform, SCALAR, along with breakthrough Semiotic Parsing Language (SPL) is used by manufacturers of technology products to quickly extract strategic intelligence from operational data that gets called home from devices in the field.

In this release we have added some important capabilities: