https://www.qlik.com/us/-/media/files/resource-library/global-us/direct/datasheets/ds-augmented-intelligence-en.pdf
https://www.qlik.com/us/-/media/files/resource-library/global-us/direct/datasheets/ds-augmented-intelligence-en.pdf

In the past few years, Qlik Sense has introduced a solid range of advanced analytics capabilities that compliments the Data Analytics platform. This includes using techniques such as Machine Learning, Natural Language Processing, etc. to help analysts/scientists explore data in a better way, get insights into any hidden patterns & take necessary actions. Bringing these methods together with the Data Analytics platform is often termed ‘Augmented Intelligence’. For a more detailed description of the what’s & why’s, please refer to this link.

Consider the following scenario. An analyst needs to explore geographical data for a variety of neighborhoods in Toronto…


Machine Learning has become ubiquitous in today’s world and our dependency on the decisions made by the algorithms has only increased. Also, as the complexity level of a real-life problem surges, ML practitioners have been on a quest to develop better algorithms to fit the curve. This also leads us to some common questions -

“How do we understand the workings of these complex models?”

“How do we validate the decisions made by the models ?”

With the rise in the application of ML algorithms, the need for transparent and interpretable models becomes essential. This is particularly crucial for decisions…


Welcome to the 2nd part of developing a Visual Text Analytics app using Qlik’s open-sourced solutions and a Word embedding technique(Word2Vec). In our previous tutorial, we designed a simple architecture(seen below) for the application that we will learn to develop today.

Now, let us try to understand the need for each of these components and their role in our app.

  • Front-end : This is the UI of the app that will help the user interact and derive insights.
  • Back-end : Consists of 2 sub-components.

— Client-side — This is where we have the Qlik’s visualization libraries Nebula and Picasso.js.

—…


In my last post, I tried elucidating what Visual Analytics is and highlighted how it can differ from Data Visualization in general with some examples. Today, we are going to talk about one particular research area within Visual Analytics i.e., Visual Text Analytics. This tutorial will focus on the nitty-gritty of this area of research and in my next post, I will do a step-by-step tutorial of how you can actually develop the application.

With the surge in the generation of digital text on the web in the form of product reviews, descriptions, feedback, etc., there has been a demand…


Visual Analytics(VA) formally can be termed as a discipline of analytical reasoning aided by interactive visual interfaces that aim at explaining certain hidden patterns in data and algorithms.

I think in today’s data-driven world we have been fairly familiar with how ‘Data Visualization’ can help users derive insights & make better decisions. However, is Visualization & Visual Analytics the same? This is one of the most frequent questions I have faced during my research work at the university or my job. Let us try to understand how do these disciplines relate and where is the boundary of separation.

Information visualization…


Qlik Sense’s self-service visual analytics platform has been compelling in processing and analyzing complex datasets to derive hidden patterns from the data and helping end-users make faster decisions by presenting interactive visualizations. To add to its charisma, Qlik incorporated Conversational Analytics, the “Insight Advisor” — an AI-powered chatbot platform that provides a faster mechanism for users to ask questions and help them discover insights using Natural language Processing(NLP).

By blending the robustness of Qlik’s Associative and Cognitive Engine, the Insight Advisor assistant instantly generates relevant answers in the form of narrative texts, visualization charts and recommendations to help users with…


In the past few posts, I have discussed the modern, lightweight framework from Qlik, Nebula.js, and its usage in developing various Qlik Sense objects such as — creating a new visualization chart or building Embedded analytics solutions like Mashups. Nebula.js is a collection of product and framework agnostic JavaScript libraries and APIs that helps developers easily integrate out-of-the-box capabilities on top of the Qlik Associative Engine. So, let’s assume you have an already existing extension developed using the Extension API, and you would like to migrate this extension to the Nebula.js framework. How would you do this?

The focus of…


Modern Embedded Analytics solutions using Qlik offers a stack of open-source libraries to build customized analytical platforms backed up with the robustness of Qlik’s Associative Engine. These new mediums facilitates communication with the Engine and provides the flexibility to develop your own client and services or to integrate visualizations and mashups. Historically, Capability APIs have been extensively used to build mashups and perform application related operations. An alternative offering to Capability API-based operations is Nebula.js and Enigma.js.

To clarify the use of each of these libraries, let’s breakdown their functionalities in a simplistic way to help the developer community to…


Parallel Coordinate Plot(PCP) with Iris dataset.

In my last post, I discussed the robust capabilities of Qlik Sense(QS) APIs to build out-of-the-box visual metaphors and ways to integrate them within Qlik’s ecosystem. A natural choice for developers while building QS extensions throughout the years has been the ‘Extension API’ primarily using vanilla JavaScript, jQuery and AngularJS.

The Extension API consists of methods and properties used to create custom visualization extensions.

Enter… Qlik Sense’s Open Source Solution — Nebula.js!


Qlik Sense API’s have been a powerful set of tools for the developer community to build out-of-the-box capabilities and to integrate them seamlessly within the Qlik’s ecosystem. These APIs enable a developer to communicate with the Qlik platform using technology frameworks such as JavaScript, .NET to build custom Visualizations, widgets, or Mashups(integrating Qlik objects with web portal).

Consider a scenario where a customer already uses the benefits of Qlik Sense native charts and objects and their new requirement is to present some data in a chart that perfectly syncs with their use case but is not available within Qlik Sense…

Dipankar Mazumdar

I am an R&D Advocate at Qlik where I experiment with the Engine. Also, a Visual Analytics Researcher at Dalhousie University.

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