19 10, 2017

Trendalyze available on the Azure platform

2017-10-19T09:22:09+00:00 Analytics, Data Science, Motif Analysis, Time Series Analysis|

Trendalyze is generally available on the Microsoft Azure cloud platform distributed as a Virtual Machine Image  (Ubuntu) and Docker container for Azure Container Services.

Integration has been provided for a number of the Azure services including:

  • Azure BLOB Storage using WASB
  • Azure Data Lake Store
  • Azure HD Insights
  • Azure IoT Hub
  • Azure SQL Data Warehouse
  • Power BI

The support for Azure is in addition to the support for Amazon Web Services (AWS) platform where the implementation supports Ubuntu Amazon Machine Image (AMI) distribution and Docker container for EC2 Container Services (ECS).

8 10, 2017

How patterns analytics can grow your business?

2017-10-08T15:28:14+00:00 Analytics, Time Series Analysis|

This is a nice blog post about “Three Ways Pattern Analytics Will Grow Your Business”. The article explains in a straightforward way what pattern analytics is and why it can be difficult to implement. Because the data is typically big and contains many variables, you will need some tools that automate the pattern discovery. By the way this is what Trendalyze provides.

According to the blog, the first way to grow the business is trough sensor analytics. The analysis of granular data will help you “find anomalies, commonalities and trends that reveal insights that otherwise would remain unnoticed”.  You can use these insights to optimize operations and manage maintenance.  The second way is to use pattern analysis to fortify your company against cyber attacks. Since all intrusion occurs in a sequential manner, pattern analytics allow you to catch any new ways of infiltrating your systems. Lastly, but perhaps most importantly, pattern analytics can help you grow sales. As the authors point out: “Pattern analytics on customer and sales data can therefore indicate market trends, customer interests, latent needs and reveal future sales trends. In addition, pattern analytics can show the top selling items based on geography and interest and news happening around the world.”

We will be interested to hear your stories about how pattern analytics improved your business.

16 03, 2017

Trendalyze and UCL get a C2N award to develop advanced analytic solutions for computer assisted surgery

2017-03-16T14:48:58+00:00 Analytics, Data Science, Deep Learning, eHealth, Time Series Analysis|

MedCity @MedCityHQ is a collaboration between the Mayor of London and London’s three Academic Health Science Centres – Imperial College Academic Health Science Centre, King’s Health Partners, and UCL Partners. Launched in April 2014 to promote and grow the world-leading life sciences cluster of England’s greater south east, it is promoting life sciences investment, entrepreneurship and industry in the region.

Collaborate to Innovate @C2N_ERDF is an exciting MedCity project in the broad life sciences domain which launched in 2016, part-funded by the European Regional Development Fund. The project aims to promote knowledge transfer and commercialisation of innovations. C2N targets London based SMEs that have ambitions to embark on innovative projects in the applied research/clinical domain, or wish to bring new products and services to market.

Trendalyze and UCL have been successful in a C2N award to develop analytics solutions based on advanced data science and deep learning for computer assisted surgery.

Image-guided surgery (IGS) has the potential to enhance the localisation and navigation capabilities of the surgeon during Minimal Access Surgery (MAS). To address the significant challenge of deploying such a system in clinical practice it is important to understand its use in training scenarios with two key research goals:

  • To test the computing algorithms for IGS in a manageable training environment and understand the workflow needs and trend changes in motion patterns for robotic radical prostatectomy;
  • To understand how guidance information influences surgical performance and if it can potentially be used for developing advanced instrument-tissue motion modelling and skill evaluation.

This project will have potential for several potential exploitation opportunities in the IGS and broader computer assisted surgery (CAS) markets:

  • Added value to surgical robotics companies who can use the analytical platform and research to guide their system by Trendalyze;
  • Added value to medical device manufacturers by demonstrating skills changes with new devices in a new analytics product by Trendalyze, designed for bespoke analysis and deep learning on instrument motion data gathered from different sources (robots, optical trackers, sensors);
  • New products can come from IGS work (and the broader CAS) as well as from the time series analysis and deep learning that exploit the workflow information to highlight parameters automatically at the right point of care. Such new analytics products can accelerate clinical innovation time to market and improve quality.
28 06, 2015

Interactive low-latency motif discovery at scale

2016-12-28T00:21:14+00:00 Analytics, Motif Analysis, Time Series Analysis, Uncategorized|

Many algorithms have been developed in the academic world for Motif/time series analysis such as the popular dynamic time warping (DTW). However, most such algorithms have not been implemented to scale and as a result can not provide interactive low-latency response times needed for web applications when dealing with large data sets.

Parallel processing frameworks (such as map reduce) can improve the performance but require large (and expensive) clusters of servers to achieve significant improvements in response times. The expectation of users is often set by using OLAP tools or internet search engines where sub-second response times are the norm.

At Trendalyze Decisions, we have implemented our own patent-pending techniques that can accomplish low latency interactive response times when processing large number of time series for motif search, match and ranking.

This is illustrated below using data from NHS England comparing Trendalyze on a single node with Map Reduce on five nodes:

Scalability Chart

 

3 02, 2015

European Union Future Internet – FIWARE

2016-12-28T00:21:15+00:00 Analytics, eHealth, European Union, FIWARE, IoT Oportyunity, Motif Analysis, Time Series Analysis|

We are pleased to announce that Trendalyze Decisions Ltd is participating in the European Union Future Internet programme known as FIWARE.

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FIWARE is an innovative, open cloud-based infrastructure for cost-effective creation and delivery of Future Internet applications and services, at a scale not seen before. FIWARE API specifications are public and royalty-free, driven by the development of an open source reference implementation which accelerates the availability of commercial products and services based on FIWARE technologies. The FICHe programme selected a number of European small and mid-sized enterprises and startups to develop innovative applications in the eHealth domain using FIWARE technology.

 

Our work leverages and expands the FIWARE generic enablers for Big Data and Internet of Things (IoT). We are also working with FIWARE team to establish London based secure nodes which can conform with UK National Health Service (NHS) information governance and cloud computing standards available to the broader UK public sector through the G-Cloud framework.

The Trendalyze Decisions platform enables IoT search and match based on patterns and trends in the data generated by sensors, wearables and other devices. Within health and social care there are many applications including patient monitoring.

2 02, 2015

Trendalyze Decisions Ltd

2016-06-05T22:28:52+00:00 Analytics, IoT Oportyunity, Motif Analysis, Time Series Analysis|

Trendalyze Decisions is dedicated to developing and providing a new generation of analytic platform for visual discovery, search, and operational monitoring of frequently occurring patterns (motifs) in time series data streams generated by the internet of things (sensors, wearables, mobile apps and networks).  The rapid adoption of IoT has created a tremendous opportunity to capture and unlock knowledge from time series data.  Cisco Consulting Services also estimates that analytics will drive $7.3T cialis with food of the $19T Internet of Everything (IoE) opportunity over the next 10 years. “To capture this opportunity, a new approach is needed to get analytics to the data for instant insights.” (Cisco, 2014)