PAASMER Edge Software Releases Version 2.1

New Updates Include: Machine Learning Developer Kit, Easier Device Management and New Ways to Configure Your Data

Paasmer has been consistently upgrading their software stack over the past couple of years based on feedback from their IoT developers and clients. Last year, Paasmer extended its reach and support beyond IoT into drone, robotics, and smart city applications. Earlier this year Paasmer made major updates to the  Edge Analytics; Edge Machine Learning & Edge Artificial Intelligence software packages with the Paasmer 2.0 release.


Today, Paasmer 2.1 launched with an upgraded Machine Learning Developer Kit and enhanced Paasmer Cloud GUI. THese new enhancements make it easier for developers to build machine-learning applications on the Edge. Typically,  historical data sets and related parameters are used to train machine-learning applications; however, the new trained module will be deployed on the device itself which allows for continuous training and improved accuracy.

Data visualization has also been updated in Paasmer Cloud GUI the new update allows developers to choose a different data configuration and presentation for each feed. The following presentation types have been added with the Paasmer 2.1  release: line chart, bar chart, circular gauge, vertical gauge, button and slider bar button.

The biggest enhancement made to last years Paasmer 2.0 version was the addition of dockerized architecture on their Edge Software. To accompany the dockerized architecture, Paasmer 2.1 features a new section in the left side panel of the Cloud GUI called, “Docker Device”. This new section makes it easier for developers to replace a broken or malfunctioning device without having to buy a new license for faster prototyping and more tests on multiple devices at a time.

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bi & A

Key Drivers of Business Intelligence & Analytics market

ManagementTeamMouli1

Chandramouli Srinivasan

The overall BI and analytics market segment continues to expand and is expected to sustain its 2014 growth rate of 5.8% (adjusted for constant currency) through 2019 — as reflected in Gartner’s current estimate of the compound annual growth rate for the sector. However, this lower rate of growth reflects a market in transition, with changing buying patterns and requirements. Purchasing decisions continue to move from IT leaders to line-of-business executives and users who want more agility and more flexible personalized options — making the land-and-expand model the new norm. This is in stark contrast to the large, enterprise-scale deals that fueled double-digit growth at a time when IT had larger budgets and wielded much more influence in buying decisions.

The primary drivers of new growth in this rapidly evolving market are being influenced by the following dynamics:

  • New vendors continue to emerge, offering innovative products to the market for buyers to consider. During the next several years, buyers will benefit from the attention that vendors are giving to the BI and analytics market and will have ample opportunity to invest in the innovative product offerings that are brought to market. The downside of having a plethora of innovative products to pilot and vendors to engage in POCs with, is the tendency for organizations to incur technical debt over time — as multiple stand-alone solutions that demonstrate business value quickly (and hastily) turn into production deployments without adequate attention being paid to design, implementation and support.
  • The increased need for governance will serve as the catalyst for renewed IT engagement as business-user-led deployments expand. When the market shift first began, business users felt empowered to circumvent IT and autonomously purchase and begin using any product that addressed the gaps in their enterprise BI program.
  • Market awareness and adoption of smart data discovery will extend data discovery to a wider range of users, increasing the reach and impact of analytics. These emerging capabilities facilitate discovery of hidden patterns in large, complex and increasingly multi-structured datasets, without building models or writing algorithms or queries.
  • The need for organizations to integrate and derive insight from a growing number of multi-structured data sources will drive innovation in smart self-service data preparation and smart data discovery. Organizations will require sophisticated software capabilities that automate the ingestion, inference, enrichment and creation of search indexes when accessing new data sources.
  • Search-based data discovery enabled by natural-language query will extend the reach of analytics to more users. As BI and analytics platforms increasingly support natural-language query, allowing nontechnical users to analyze data by asking questions in a conversational way, new users are more likely to engage with and leverage analytics.
  • Marketplaces will expand and mature, creating new opportunities for organizations to buy and sell analytic capabilities. The availability of an active marketplace where buyers and sellers converge to exchange analytic applications, aggregated data sources, custom visualizations and algorithms is likely to generate increased interest in the BI and analytics space and to fuel its future growth.
  • Organizations will need to support real-time events and streaming data capture in support of IoT use cases. In order for organizations to prepare for the volume of data that is generated by devices, sensors and people in a connected world, organizations will have to make new investments in products that are designed to capture and process this type of data.

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Cloud Services Opportunity

Cloud Services Opportunity – Prime Time for Cloud Service Players to different and win the market place

ManagementTeamMouli1

Chandramouli Srinivasan

I was talking to an analyst about cloud services business opportunity for SMB Players like MoboDexter. A few key points emerged that were really interesting and concurred with our understanding as MoboDexter’s Cloud Services Offering is already into this space and working with players like Harman Connected Services. Sharing the key points from this discussion here.
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JAR:Load

JAR:Load – Cloud Based Web Load Testing Software

ManagementTeamMouli1

Chandramouli Srinivasan

I am Happy to announce that MoboDexter Inc. has signed a partnership agreement with JAR Technologies, U.K. to sell their Cloud Based Loading Testing solution called JAR:Load. As part of this agreement, MoboDexter has secured an exclusive pricing deal that can be leveraged by any prospect who wants to purchase JAR: Load through MoboDexter. I want to talk about the reason why I felt this is a compelling tool in the market in comparison to its competitor products.

JAR:Load – Stand Out Features

  • JAR: Load is the only enterprise load testing product, delivered from the cloud, that uses real web browsers to simulate load. If you run ANY type of JavaScript (jQuery, GWT, AngularJS, Ember, etc.) on your web-site then you can’t rely on “other” load testers who just record HTTP transactions – they will miss all your dynamic AJAX operations!
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Healthcare Transformation through IOT

Healthcare Transformation through IOT

ManagementTeamMouli1

Kavitha Gopalan

IOT is revolutionizing various industries and we cannot ignore the huge transformation it can bring to the health care industry. IOT combined with the analytics and cloud can provide unprecedented capabilities in healthcare – virtual healthcare, predictive diagnostics, preventive care and targeted care to name a few. It can help in improving effectiveness and quality of patient care at the same time help the healthcare organizations to improve efficiency and improve their financial and administrative performance.

IDC predicts that virtual care, made possible by the Internet of Things and big data, will become routine by 2018, improving healthcare quality and efficiency. Some of the ways in which IOT is changing healthcare industry are covered here.
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