NABU - FULL STACK BIG DATA IT ECOSYSTEM

 

FULL STACK BIG DATA IT ECOSYSTEM

We have developed a proprietary IT Ecosystem to support the identification and implementation of innovative business solutions based on the “Big Data Approach”. Ours is a “Full Stack” Ecosystem that addresses with a single integrated solution all features needed to realize advanced Big Data IT solutions.

Our strategic vision is not to be the IT leader in any specific component of a Big Data Platform, but rather have the most advanced Full Stack Big Data IT Platform, which exploits available state of the art technologies. 

Our IT efforts are therefore focused on the development of the full stack and of the missing components necessary to integrate the available ones. Should we discover that a new technology outperforms one of ours, our strategy is to drop ours and use that one. In other words, our scope is to assure that we have the best Full Stack Big Data IT Platform in the market. 

In addition to being a Big Data Full Stack Platform, our key differentiator is the application of a semantic approach to Big Data to obtain information enrichment that differs from most common solutions in the market. As we all know, information is also multilingual, covering many languages and scripts, in all of their complexities and challenges. Our platform can identify over 55 languages and extract entity in over 17 languages including all the major European, Asian and Middle Eastern languages.

 

DATA NORMALIZATION

 

BDT - DATA NORMALIZATION

 

PATTERNS

  • Catch key properties, for example, names, numbers, places, dialects 
    • More than a thousend of different data types supported (text, audio, video, IoT)
    • Native review of files
    • Conceptual search independent from keywords   
    • Sentiment extractions 
  • Automatic correlation of separated entities with information
  • Multi-language text analytics with 55 supported languages
  • Secure and integrated on rights research   
  • High level customization and knowledge management
  • Horizontal research across multiple and different repositories
  • Semantic Analysis
  • Automatic Taxonomy
  • Conceptual Correlation
  • Entity Extraction
  • Machine Learning

 

CORE FEATURES

Information Enrichment - It enlarges information with other pertinent data. It is possible to extract organization names from tweets and make tweets searchable by organization names 

Propelled Enterprise Search - An accurate and refined contextual search within internal and external data that allows to search for a brand and to obtain results including its competitors 

Knowledge Discovery - Reveal patterns, trends and connections without direct inquiries such as identifying original reasons of an attrition between a company and its customers in social media 

Augmented Media Analysis - Perceive and analyze pictures, audio and video files with object recognition (text, logos, etc.) and speech-to-text acquisition from the media itself.

Business Intelligence - It consists of two main blocks: data transformation and their fruition in order to allow people to take strategic decisions. Thanks to NABU Ecosystem we are able to operate with data and obtain added value. For results' presentation in a flexible and dynamic approach, BDT adopted Tableau, a real market leader, whose solution allows to view data in tables, charts, maps and many other formats. Using the Tableau Server features we can build dashboards of analysis that will be included in the business logic of each specific client application. Thanks to the dynamic filters capabilities each presentation realized in Tableau and incorporated in our platform can be customized for every possible user's need. 

 

SCALA - Programming Language

BDT has adopted Scala as main programming language because of its remarkable ductility and especially for its overt superiority in terms of technological evolution. 

Scala is a general purpose programming language. Scala has full support for functional programming and a very strong static type system. Designed to be concise, many of Scala's design decisions were inspired by criticism of the shortcomings of Java.

Scala source code is intended to be compiled to Java bytecode, so that the resulting executable code runs on a Java virtual machine. Java libraries may be used directly in Scala code and vice versa (language interoperability). Like Java, Scala is object-oriented, and uses a curly-brace syntax reminiscent of the C programming language. Unlike Java, Scala has many features of functional programming languages like Scheme, Standard ML and Haskell, including currying, type inference, immutability, lazy evaluation, and pattern matching. It also has an advanced type system supporting algebraic data types, covariance and contravariance, higher-order types (but not higher-rank types), and anonymous types. Other features of Scala not present in Java include operator overloading, optional parameters, named parameters, raw strings, and no checked exceptions.

The name Scala is a portmanteau of "scalable" and "language", signifying that it is designed to grow with the demands of its users.

 

 

Source - https://en.wikipedia.org/wiki/Scala_(programming_language)

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Sentiments are the result of a never-ending clash between the irrational and rational components of our personality. 

Many variables concur to the end result of this clash: the topic and how relevant it is for us, our knowledge and competence on the subject, our current general mood and also the influence of the external environment (social media above all!).

Our sentiments are therefore the result of a very complex process, as it is assessing them...with a software! However, the so-called Artificial Intelligence (AI) does provide us with some understanding of them.

While ours is a very sophisticated and advanced Big Data technology, it’s up to us to make a good use of its output. This application of our Big Data technology, which is only one of those possible, converts sentiments into numbers with a many-digits precision! However, don’t let you be confused by this mathematical precision. Don’t jump to conclusions. Use your rational component to understand those numbers and integrate them with your experience and good judgment. This is the only way our powerful tool can help you gain a better understanding of...what’s going on!

We hope our tool will help you better understand the evolution of this very important campaign and, above all, the complexity of the voters’ behavior. 

We would also appreciate your feedback on our tool.

Thanks for your  attention and...enjoy playing with our “Sentiment Analysis” 

Roberto Fantino

Founder and CEO - Big Data Technologies srl