As every year , SequoiaAT will be at CES 2020 to be held in las vegas from Jan 7th to Jan 10th.
Sequoia AT is pleased to announce that they are on the list of the CIO bulletin’s 10 Best IoT solution providers for 2019. Speaking on the occasion, COO of the company, KR Gopinath says “I am glad that they recognized what we do here in Sequoia. Our team’s outside the box thinking and persistence for making our customers products better is why we were recognized by the CIO Bulletin.”
SequoiaAT currently has two development centers in Santa Clara (USA) and Trivandrum (India). SequoiaAT is planning on expansion of their development offices in Santa Clara (USA) & setting up new office in Kochi (India).
Working with passion is the internal theme at Sequoia. And this recognition is a proof of what every Sequoiaan believes in. Ram Mohan (Director) says that “At SequoiaAT the quality starts by ensuring that we hire for our culture. We hire only individuals who are extremely passionate about their work. This enables us to go beyond our customer expectations.”
SequoiaAT was named perviously named in the Top 100 Tech companies founded by Indians in Silicon India Magazine
AI is the next big wave which will change we know the world for generations to come. AI has attracted over $17 billion in investments since 2009 and will add over 15 trillion to world economy by 2030 as per estimates.
The term AI was coined in 1956 and even thought of by ancient philosophers, but Some of the early work in this space was done at Stanford University for treating blood infections. Till about early 2000’s most of the work in AI was limited to universities like MIT, Stanford, Rutgers etc.
One of the domains which stands to benefit the most from AI is healthcare. The healthcare industry is advancing in discoveries daily as technology advances in major ways. We have done amazing things in the last few years and currently Artificial Intelligence has been dominating as the main point of interest. AI is being harnessed to increase longevity and health of the human race.
As an example we all know one problem with hospitals is wait times. As a hospital, doctors need to make every second count. With help of AI hospitals can assign beds to patients faster and more effectively. While this may seem like a useless task it prevents having employees do this job, and little by little, it saves a lot of time. In the John Hopkins Hospital, this has been able to see and predict future requests for beds, and even plan for future unavailabilities. As per the recent article in HBR, It decreased wait times, and even allowed them to accept over 50% more new patients from other hospitals. AI can also do the paperwork that takes doctors a significant amount of time, giving them more time to engage with their patients. Every second that AI saves is another second for doctors to save a life.
Besides preparation, AI directly uses Brain Computer Interfaces. This can be used to decode neural activity. Potentially it could be used to help the many people with ALS and strokes, as well as the half a million people yearly that have spinal cord injuries. Neurological problems have been extremely difficult, if not impossible to solve. AI is helping in ways unimaginable 10 years ago. When AI is allowed to look at all the data from patients, it can notice patterns and analyze them in ways that would be humanly unachievable. AI will make sense of data allowing them to predict things that will happen to specific patients with incredible accuracy. AI could take all of the unstructured data and classify them, and this is especially useful as we are expected to double medical data every 73 days from 2020, according to IBM.
Even selfies can be used to find diseases. An algorithm can find the subjects facial features, and predict facial feature abnormalities. Just in a few pictures the AI can analyze things that we would need expensive equipment and preparation to find out. AI with expensive tools such as x-rays and MRI scans, can find out all problems instantaneously. AI is highly useful in predicting patterns. This can be used to predict problems and also patient recovery time. With the right data sets, AI will be able to foresee diseases like seizures and sepsis.
At SequoiaAT we have started taking small steps towards AI in medicine by collaborating with companies in life-sciences and medical domains. We have been working with them on solutions which further this goal.
AI will do everything that humans can do in a fraction of the time, in all helping and curing more people. AI will save unbelievable amounts of money, and even more time, making every second count.
With the advent of fast genome sequencing techniques, biological datasets worldwide have exploded to tremendous sizes today. For instance, a single patient’s sample after sequencing and several stages of data processing and analysis could run into over a Terra byte! Raw sequencing data that comes out of the sequencing machine is at an abstract level of potentially useful information, requiring significant processing to be converted into meaningful form to drive genomics research.
Some of the data conversion steps being highly computation intensive and/or requiring specialized bioinformatics algorithms, a large portion of the bio-informatics data processing pipeline is implemented in the cloud today. However, as the data resident in the “genomics cloud” reaches the hands of the researcher, it is only as good for research as the analytics and visualization capabilities.
Visualization is a graphical representation of data intended to provide the user a qualitative understanding of information. Data visualization techniques greatly enhance the user’s understanding and interpretation of these massive data sets. A visualization-integrated bio-informatics pipeline provides researchers with the ability to explore genomics data and enables them to progressively iterate, backtrack or zero-in on their analysis steps, thereby enabling them to infer high-impact conclusions with an improved degree of confidence within a reasonable time.
The two essential attributes of a successful data visualization framework are:
1) High interactivity
2) Performance at the speed of analysis
Interactivity implies the ability to manipulate graphical entities to derive intuitive data representations. Interactive graphics involves the detection, measurement and comparison between points, lines, shapes and images being represented for the effectiveness of user interpretation, accuracy of quantitative evaluation, aesthetics and adaptability. Enhancing data interpretation by varying the views, labelling to retrieve the original data, zooming in to focus the clarity of data, exploring the neighboring points and a user adjustable mapping can create a good data exploration experience to the user.
Consequently, as the user continuously manipulates data (applies filters, adjusts thresholds, tunes parameters like scale and dynamic range of values) to make “research sense” out of the data, the visualization framework should permit
1) Discrete or continuously variable settings with user-friendly controls like text boxes, selection drop-downs, sliders, knobs etc. and
2) Quick redrawing of the updated graphical representation after every change is made in user settings.
General-purpose and traditional analytics software packages that have been adopted in bio-informatics often come with add-on packages for interactive visualization to a basic level of utility for research. With an easy non-programmer model that appeals very much to researchers, these packages provide interactive graphs and plots. Having an in-built web server eliminates the need to install any client applications, all that the user needs is a browser and an URL to point it to.
However, when it comes to enormous datasets that range millions of data points, these in-built/add-on visualization frameworks are found to be incapable of giving the user an acceptable (sub 1-second?) performance each time a user setting is changed. Therefore, guaranteeing an analysis-continuum to the users remains challenging. Besides the rendering stability of these in-built/add-on packages is often found problematic when large data sets are thrown at them, with statistical methods applied on the data. Rendering inaccuracies including gross misrepresentations of data are frequently encountered that expose the limitations of their scalability.
Here comes the need for evaluating, piloting and implementing visualization frameworks based on customized graphical libraries that leverage fast rendering techniques in a browser environment. As was proven by our experiments with multiple fast-visualization techniques, a customized visualization framework for bio-informatics is the sole solution to match the user’s speed of analysis to provide an enhanced time-to-insights experience.
In conclusion, bio-informatics visualization framework needs to be highly interactive and lightning fast to handle data sets in the millions. Further, from the bioinformatics pipeline provider’s perspective, scalability for a large number of concurrent users and security of data are the other key attributes to be satisfied by the visualization framework, as is applicable to the other modules like data transformation and analytics modules in the pipeline.
Today, 99.6% of all smartphones run on either IOS or Android. Increasingly mobile apps have gained significance as way to not only conduct business but also for raising brand awareness. There are hundreds of new applications being launched on a daily basis. In the last few years, the concept of cross-platform mobile app development has taken off in a big way. It allows the developer to write the code once and employ it across all platforms – Android, IOS or Windows. Some of the advantages of developing Cross Platform apps.
Cross-platform vs Native apps:
Native apps are written in languages that the platform accepts natively. For example, Swift or Objective-C is used to write native IOS apps, Java is used to write native Android apps, and C# for the most part for Windows Phone apps.
Apple and Google offer app developers their own development tools, interface elements and standardized SDK; XCode and Android Studio. This allows any professional developer to develop a native app relatively easily.
- Since native apps work with the device’s built-in features, they are easier to work with and also perform faster on the device.
- Native apps get full support from the concerned app stores and marketplaces. Users can easily find and download apps of their choice from these stores.
- Because these apps have to get the approval of the app store they are intended for, the user can be assured of complete safety and security of the app.
- Native apps work out better for developers, who are provided the SDK and all other tools to create the app with much more ease.
Cross-platform development tools that do not use WebView and communicate with the platform directly aren’t united in any subgroup. Existing under the general term of cross-platform development, they are sometimes called native development tools, which just makes it all even more confusing. For the sake of convenience, we’ll refer to these tools as ‘near-native’ here and will explain why they deserve such a praise.
In ideal scenario, cross-platform apps work on multiple operating systems with a single code base. There are 2 types of cross-platform apps:
- Native Cross-Platform Apps
- Hybrid ‘HTML 5’ Cross-Platform Apps
Native Cross-platform Apps
Native cross-platform apps are created when you use APIs that are provided by the Apple or Android SDK but implement them in other programming languages that aren’t supported by the operating system vendor. Generally, a third-party vendor provides an integrated development environment that handles the process of creating the native application bundle for iOS and Android from a single cross-platform codebase. In this case, the final product is an app that still uses native APIs, and cross-platform native apps can achieve almost native performance without any lag visible to the user. Native Script, Xamarin, and React Native are the most common examples native cross-platform languages.
Hybrid HTML 5 cross-platform apps
Mobile app development tools
Xamarin apps are built with standard, native user interface controls. Built with #C and .NET, Xamarin allows developers to re-use code and simplifies the process of creating dynamic layouts in iOS.Apps not only look the way the end user expects, they behave that way too. Xamarin apps have access to the full spectrum of functionality exposed by the underlying platform and device, including platform-specific capabilities like iBeacons and Android Fragments. Xamarin apps leverage platform-specific hardware acceleration and are compiled for native performance. This can’t be achieved with solutions that interpret code at runtime.
Apache Cordova comes with a set of pre-developed plugins which provide access to the device’s camera, GPS, file system etc. As mobile devices evolve, adding support for additional hardware is simply a matter of developing new plugins.
The React Native framework was created by Facebook, and its development started as a result of a hackathon back in 2013. React is an example of a technology that the developer community created for itself when developers were looking for a tool that would combine the good things about mobile development with the power and agility of the native React environment. React Native’s genesis resulted in a huge enthusiastic community investing into the framework’s development, and there are catalogs of freely available components that go with it.
React Native provides development tools for debugging and application packaging, which saves time.
Which One to Choose
So, if you want to impress users with a lightning fast interface, rich functionality, and overall performance, native apps are what you need. In addition, you get better security and stability. The price for this is that you’ll most likely need to hire two dedicated teams for each platform. Small business may not be able to afford develop an application for both platforms.
Cross platform apps, on the other hand, can be developed for both IOS and Android. Plus, cross platform apps are much easier in terms of maintenance and deployment, so you can spend more time and money on marketing and attracting new customers. However, their biggest disadvantage is lower performance, which may be especially crucial if you’re developing an application with features that require deep hardware integration.
Big data as a technology passed through various stages of evolution during the last few years, which still keeps it hot in the list of tech buzzwords! Starting with handling the 3 V’s of data – Volume (of data to be handled), Velocity (of data generated) and Variety (of data generated), it has spread wings to more V’s – Veracity (to ensure data integrity and reliability), Vulnerability (to address privacy and confidentiality concerns) and Value (of information)!
As Google showed the way, collection and collation of huge volumes of data and applying the right analytics to gain valuable insights into the business and optimization possibilities is the key to extracting the full potential of the data-driven industry. Today Chief Data Officers are building strategies to organize their data and to derive business intelligence from it to drive radical transformation of businesses in many sectors such as industrial, retail, logistics, healthcare etc.
BDaaS (Big Data-as-a-Service) is gaining momentum, enabling external experts to take the company’s customer data to the cloud and to provide analytical insights for decision making. Offered as a managed service, it frees up the customer from substantial initial investment and helps offer RoI-driven spending. This article focusses on BDaaS, describing the potential that enables our customers to conceptualize and launch new business models.
Large corporations with structured and centralized ERP systems wouldn’t benefit as much from BDaaS as compared to unorganized sectors comprising of diverse players each with their own fragmented IT infrastructures. For instance, unorganized retail is a heterogeneous sector with a geographically distributed supply chain that spans across medium and small players, having considerable differences in their levels of process maturity. Stand-alone islands of software application are encountered many times and so are ad-hoc (or legacy) structures of data storage and archival. B2B companies providing services to geographically spread out customers in many traditional supply chains like chemicals/reagents for laboratory use, petrochemical (non-fuel) derivatives and medical drugs could benefit from the transformational potential of BDaaS.
Suppose you are a B2B player in one of these or similar sectors, let us take a closer look at your business and customer data! Could your expertise in the industry be leveraged to identify a new data-driven model by “integrating your customer data” to offer new intelligence gleaned from it? This integration gives you the data in ‘sector level’ rather than ‘individual’ customer level. You will be able to identify sector level intelligence and provide it to all your customers which will be mutually beneficial for all.
In order to accomplish this outcome, you will most often need external expertise in big data to work collaboratively with you (or your domain consultants) in order to build a BDaaS platform to offer your customers. The value of business intelligence that the platform brings helps them win in their businesses and their patronage in turn helps your business model succeed.
So has been our experience working with a world leader in the pharmacy supply chain across North America. Besides supplying medicines and medical equipment to their customers, they also provide inventory and patient management software to their customers. The software installed in each of the numerous hospitals gathers transactional data over time. We worked closely with the customer’s consultants on the feasibility of data integration and created a centralized control center using big data technologies such as Spark and Kafka. Hosted on the cloud, the platform captures streaming data from different hospitals and pushes them to the centralized system that offers a metered BDaaS service to end-customers, the analytics insights helping them to optimize their businesses.
The path to big data implementation, however, was filled with several challenges, a few of which are:
With the regulatory requirements concerning medical information like the HIPPA standards, compliance is mandatory. Only non-sensitive data at a lower level of granularity is collected, that respects privacy concerns of the individual hospitals of exposure of their patients’ sensitive information. This is the key factor to the success of the project both from customer buy-in and regulatory compliance points of view. The collected data is pushed to cloud securely with transport layer security.
Verity of data
The data being heterogeneous and scattered is the foremost challenge while implementing big data solutions. Even though most hospitals use our customer’s software a few others use their own legacy software. Data could be isolated even across the departments in the same organization! We built data collector modules which can be customized easily to collect data from various sources and push it to the cloud. Rationalizing the relevant data fields from these diversified sources and integrating it provides a lot of insight into possibilities of analytics.
Time to market and initial investment
Being a metered service we had to make sure that customer’s cost is kept linear with usage. Databricks big data platform with reliable Open Source Kafka data injector gives us a balanced and scalable framework to meet this objective.
After data was made available from all sources centrally for analysis it was discovered that information on the availability of particular medicines in each hospital along with demand predictability has the potential to reduce the associated transportation costs by around 20%. Data-driven drill down revealed for instance that for a particular area with a prevalence of influenza but with shortage of the corresponding medicine, the system can identify the best possible area (nearest, where there is enough stock but no demand currently) from which this medicine can be arranged. Supply chain demand mitigation by coordinating drug supply between customers can significantly save inventory and transportation cost for customers. More importantly, it saves precious reaction time for their end-users, which would not have been possible without the magic of BDaaS.
In your own strategy to connect your fragmented customer data centrally to provide mutually beneficial information, the role of an experienced big data partner is indeed crucial. Combine the power of your domain expertise with big data specialists to create new data-driven business models which besides increasing your revenues could make you the hub to all customers thereby increasing the bonding of existing ones and attracting new ones.
IoT is changing the world around us. This change is affecting every walk of life including the maintenance industry. Maintenance management used to depend on skills of the maintenance managers to troubleshoot skills and was least data-driven as they have very limited data to fall back on when it came to machine health. However, it is rapidly changing. It is becoming heavily data-driven than skill driven. Advances in wireless communications and data processing enable maintenance managers to gauge the health of the factory in an instant.
We can tell that its no longer a hype but a reality and proof is in the fact that the leading organization- OPC Foundation is spending time in developing the Unified Architecture (UA) Specification for IIoT in the manufacturing environment. The standard is being developed to enable IIOT devices to easily pass information between sensors, machines, monitoring devices and the cloud in a secure and open way. Also OPC, AMT & OMAC have jointly developed Packaging Machine Language (PackML) and MTConnect which combines OPC UA with existing industry standards to lower cost of predictive maintenance.
Low cost of IIOT sensors is making predicting failure or measuring remaining useful life (RUL) of a tool a no-brainer enabling maximum uptime at optimum costs . As an example, a drill over course of its functioning will start to suffer wear. As we continue using them regularly at some point of time they become unusable either because the precision of the job falls below the parameter or the drill bit breaks off. With the combination of Industrial IoT sensors and AI techniques today we can easily predict the remaining useful life of the tool .
Any maintenance professional will agree with me that predictive maintenance is a journey they have to take but IIOT makes the journey easier. Retrofitting existing machine with a sensor to measure machine health becomes very easy. One of the companies where we work with to enable this transitions is OPA By Design. It is a smart device which can be tagged to any existing machine at a very minimal cost to measure 8 different parameters and report it maintenance supervisors via mobile app & cloud. Since the machine is constantly being monitored, any sign in degradation in the health of the machine is alerted instantly
IIOT is also enabling to drive down the inventory holding cost as now maintenance supervisors have better predictability of machine failure and hence they have to stock less spares. It also results in fewer emergency inventory orders and less downtime due to out-of-stock inventory.
IIOT is not changing anything for the maintenance professional except the fact that he can now listen to his assets and make informed decisions based on actual data on the health of the asset. IIOT is not going to fix the problem for him. He will still have to depend on his best technician to fix it reliably