Desire 3D is a MASA (Mesh App and Service Architecture) Platform to create Complex Enterprise Applications (Web and Smart Devices - Android & iOS) using cutting edge Micro Services architecture and In-Memory Cache / Kafka (for scalability) with RDBMS or Big Data Support.
Design Pipeline is divided into 3 Sections
Code Pipeline is divided into 5 Sections
Event Sourcing Design pattern ensures that all changes to an Application is stored as a Sequence of Events
CQRS is a pattern that segregates the operations that read data (Queries) from the operations that update data (Commands) by using separate interfaces. CQRS should only be used on specific portions of a system in Bounded Context (in DDD).
Domain-Driven Design is an approach to the development of complex software in which we: Focus on the Core Domain. Explore models in a creative collaboration of domain specialists and software practitioners. Speak a ubiquitous language within an explicitly Bounded Context.
The 3 Dimensions to App scaling, ie. Functional Decomposition in the Y-Axis, Cloning of Services in the X-Axis and Database Partitioning in the Z-Axis, which gives the maximum scalability compare to outdated Monolithic App Design..
The core of the Hexagonal Architecture revolves around the Domain Layer (Use case Boundary / Bounded Context) followed by Ports (interfaces) and Adapters (the actual implementations).
Auto creation of Immutable containers based on Docker infrastructure. Same containers will be used in development, to QA to production environment.
It's a protocol for building Immutable historical record of transactions.
Chain Code Services
Blockchain is a Self auditing eco system of Digital Value
Deep Learning is a hierarchical learning model with more than one hidden layer. Deep Learning emulates the learning approach that human beings use to gain knowledge. Each algorithm in the hierarchy applies a non-linear transformation on its input and uses what it learns to create a statistical model as output. This iterative process continue until the output has reached an acceptable level of accuracy. Machine learning is classified into supervised learning and un supervised learning. Deep learning follows the later.
Platform will focus on integrating Deep Learning technologies from Google. Apple, Microsoft and others to easily integrate with your Business Apps.
Following are some of the commercial technologies available (From Google, Apple, Microsoft, Open Source) in the platform to be used / integrated into your Business Apps.
Text Analysis will help you to understand the meaning, context and extract information about people, places, events and much more. This can be also used for sentiment analysis about your product / services on social media or understand the intent from Customer Conversations happening in a call center to a messaging app.
This will enable you to convert audio into text. Commercial Speech API will recognize over 80 languages and it's variants. Using the Text Analysis on the converted text, you can build your business App to interact with the end user using voice.
This service helps the customer to translate any arbitrary string into a any supported language. This can be used to convey the information available in the App (as text) to convert to users preferred language in real time. Or convert an extracted text from an Image or Video into the users preferred language.
Video Analysis allows the user to extract the metadata identifying various objets, landmarks, places & faces etc. You will also able to identify key moments / sentiments and search for similar moments / sentiments across different videos.
Image analysis will classify the image into thousands of categories, detects individual objects and faces within the images as well as read printed words contained in that image.
Open Source Deep Learning libraries will be used to create Business Specific Deep Learning solutions. Deep Learning 4 Java is such an Open Source DL Library.
Requirements to power the User Interface