MACHINE DATA ANALYTICS

Semiotic Parsing Language (SPL) - Breakthrough DSL for IIoT Analytics - Part Two

Ashok Agarwal
Jun 13, 2018

In this section we will investigate how Glassbeam’s DSL called SPL (Semiotic Parsing Language) helps in parsing multi-structured machine logs.

SPL Terminology:

Namespace:

SPL allows a log file to be treated as a hierarchical document consisting of multiple segments (or sections). Each hierarchical segment is called namespace. This allows for zeroing in on the exact section to parse specific elements from, thus localizing the scope of extracts.

New FDA Report Highlights the Importance of Servicing Medical Devices

Puneet Pandit
Jun 05, 2018

This month, the FDA issued a report that focused on the quality, safety, and effectiveness of servicing of medical devices.

Semiotic Parsing Language (SPL) - Breakthrough DSL for IIoT Analytics - Part One of Two

Ashok Agarwal
May 30, 2018

Introduction

Glassbeam’s business revolves around providing business intelligence on machine data. Intelligence comes from structured data. Machine data is not always structured. So, there is a gap between what is needed and what is produced. As Glassbeam’s head of engineering, I am going to write a two series blog about how Glassbeam bridges this gap.

Starting 2018 Off Right – A Look at Glassbeam’s Q1 Highlights

Puneet Pandit
Apr 27, 2018

It has been a productive and exciting start to the year here at Glassbeam, with a number of Q1 new wins and renewals from existing customers.

Integrating Machine Log Data with CMMS Solutions. An Exciting Opportunity for Glassbeam!

Pawan Jheeta
Apr 12, 2018

Glassbeam loves its customers. Customer development is an integral part of Glassbeam. Our healthcare/clinical customers are telling us how the newly launched Glassbeam CLinical Engineering ANalytics (CLEAN) Blueprint is creating immense value by analyzing machine log data and presenting deep insights about machines in their clinical environment.

Glassbeam to Demo Its Rx for Healthcare Equipment and Systems at MD Expo Next Week in Nashville

David Sawatzke
Mar 30, 2018

We are exhibiting at MD Expo April 4-6, a leading high tech medicine (HTM) trade show and conference. It will be held at the Renaissance Nashville Hotel, 611 Commerce Street. We hope you will stop and visit us at booth #119.

Glassbeam Analytics Changes the Game for Clinical Engineering and Imaging Equipment Service Teams

Rick Gaylord
Mar 16, 2018

In my role as the Healthcare Solutions Specialist for Glassbeam I've had the opportunity to meet with a wide range of Glassbeam customers and partners. I've spoken to members of the C-Suite, Directors of Clinical Engineering, and Field Service Engineers/Imaging Service Engineers (FSEs/ISEs) on the frontlines of imaging system service.

How much does medical equipment downtime cost hospitals?

Puneet Pandit
Aug 31, 2017

The increasing demand for better diagnostics is putting pressure on hospitals to invest in high-end medical imaging equipment such as ultrasound and X-Ray devices, computerized tomography (CT) scanners, magnetic resonance imaging (MRI) scanners, and positron emission tomography (PET) scanners. These machines can range from several hundred thousand to a few million dollars each.

Glassbeam for Glassbeam (Part 5) – Data Driven Product Management

Pramod Sridharamurthy
Aug 14, 2017
Glassbeam for Glassbeam - Product Management
 
Product Management is not an easy thing to do. Bulk of the company’s resources and direction is driven by the decisions made on the roadmap of the product. Bad decisions lead to wasted effort internally and in the best case lead to unhappy customers and in the worst case, lead to losing customers. 
 

Glassbeam for Glassbeam (Part 4) - Helping Smart Engineering Teams be Smarter

Pramod Sridharamurthy
Aug 07, 2017

In this post, let me explain how Glassbeam’s engineering team uses the Glassbeam Analytics solution to be an effective and responsive team to every bug and the not-so-nice user experiences that our customers could potentially face.

Here are some guidelines that our Engineering team uses to seek answers from log data-driven Glassbeam Analytics:

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