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Top JavaScript TreeGrid libraries in 2020-2021

We will look at the ToP 5 TreeTable web widgets that you can freely download and use in your work. Top JavaScript TreeGrid libraries in 2020-2021.

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TOP JavaScript TreeGrid libraries in 2020-2021

The article will cover one of the most complex interface elements for web applications: ​JavaScript TreeGrid​. It will be of interest to front-end developers and novice JavaScript programmers developing business applications and solutions for working with rich data.

From this material, you will learn about ready-made TreeGrid solutions, their purpose, and application methods. At the end of the article, we will look at the TOP 5 TreeTable web widgets that you can freely download and use in your work.

Using ready-made JavaScript components significantly speeds up the development process and allows you to avoid unwanted errors. We will look at the products of Webix.com, Treegrid.com, jQuery.com, DHTMLX.com, and Sencha.com.

What is it?

Let’s start by defining what a TreeGrid or TreeTable is. It is a way of arranging table data in the form of a list of hierarchical tables. Traditionally, TreeGrid allows developers to create tree-like lists where entire tables are used as “branches.”

The TreeTable component has appeared as a logical response to the complexity of working with the UI trees. On another one hand, TreeTable is a classic DataGrid that allows you to conveniently present information in the form of a table. On the other hand, it performs tree-like hierarchical grouping of table data in the same way as the Tree widget.

Do not confuse the TreeTable component with the grouping of rows and columns used in DataGrid.

Grouping columns

Pic.1 Grouping columns

This mechanism is available in some advanced UI widgets, such as ​SpreadSheet JS​, where rows and columns are grouped in an Excel manner. But TreeTable is a more complex solution, as it groups not only separate data but also entire arrays of information. In addition to that, it displays table data in a hierarchical subordination and control elements to view this structure.

A TreeGrid tree table

Pic.2 A TreeGrid tree table

Application areas of TreeTable

Such interface solutions are in demand in the areas related to:

  • Big-Data;
  • Analytical systems;
  • Financial statistics;

Any industry or business task that contains rich data can use this solution. A key sign that analysts and developers should pay their attention to the TreeTable components is the so-called “data rhythm.”

When data is grouped, for example, by date, in this case, there is a prerequisite for working with three-dimensional arrays of information, which is quite challenging to implement in practice, but it is possible using TreeGrid.

Example:

A good illustration of the demand for TreeTable in applied tasks with big data is the speech of the developer and former Google employee ​Dan Dascalescu​. At a conference in San Francisco, he presented a prototype analytical system, the core of which was TreeGrid.

Dan Dascalescus prototype analytical system

Pic.3 Dan Dascalescu’s prototype analytical system

The development team set an ambitious goal of creating tools for working with complex structured data. The use of the TreeGrid component allowed them to solve the main tasks and complete the project in a shorter period. The full version of the speech can be found at this link​.

Advantages and disadvantages of the TreeTable/TreeGrid UI component

The advantages include:

The possibility to visualize three-dimensional data arrays on a single screen, without having to switch to nested tabs.

Here we note the apparent disadvantages of this approach:

  • The user may get lost in a large number of nested tables. You can avoid this by using navigation and table management elements, such as collapsing/ expanding all data branches.
  • A large amount of data uploaded to the browser loads the client part of the system significantly and can lead to errors due to physical limitations (the maximum size of the web page, the amount of RAM).
  • Complex tables with a large number of rows and columns will be rendered slowly in the browser, so you should choose solutions optimized for rendering speed as much as possible.

The possibility to quickly navigate through large amounts of data without using filters and search queries. TreeTable is a powerful analytical tool in the hands of an experienced user.

Disadvantages that analysts mention:

  • The complexity of working with “end-to-end data,” in cases when TreeTable can combine tables with different structures.
  • It is not possible to build a pivot table.

The opportunity to combine data of different formats in the same table when the nested table has a structure different from the “parent” table.

1. Webix TreeTable Widget

This is one of the most professional and functional widgets in this review. The Webix JavaScript TreeTable​component is based on the Webix DataGrid widget. At the moment of writing this review, it has the highest performance web control and shows the best rendering speed on the market. On the GitHub portal, you can find the ​benchmark conducted by the developers of Bryntum, where the Webix DataGrid leads in all points.

Webix developers have paid great attention to the design. Unlike most component libraries, Webix carefully studies the design of each widget.

Webix TreeTable contains a large number of features: embedded chart lines (SparkLines), clipboard support, advanced data filters, various data grouping methods like rowspan, colspan, and grid grouping. The widget boasts a thorough elaboration of drag-n-drop separately for rows and columns, the ability to change the width of all fields, and create vertical headers.

Price​: this component is distributed free of charge under the GPL license. If you need an MIT license and access to the support service, you can purchase a commercial license, prices for which start from $199.

Webix TreeGrid

Pic.4 Webix TreeGrid

2. TreeGrid

Editable JavaScript Tree Grid​ is a DHTML component that allows you to display and edit data in a grid, table, grid tree, tree view, or histogram on an HTML page. This is a fast AJAX grid with many advanced features such as advanced formulas and cell calculations; multi-level.

Pivot tables; creating Gantt charts; displaying and updating custom JavaScript objects and external objects such as Adobe Flash, Microsoft SilverLight; various editing masks; a calendar component; bulk cell changes; exporting to MS Excel or other spreadsheets that handle XLS, XLSX, CSV, or HTML table files; localization to any languages; and many other features.

The TreeGrid component allows you to load data in XML & JSON formats. The widget is available under the Basic, Standard, Personal, and Grand licenses.

Price​: free and open-source version. Commercial licenses start at $ 79 / a component.

TreeGrid component

Pic.5 TreeGrid component

3. jQuery TreeTable

jQuery Tree Table ​is a plugin for the jQuery JavaScript library. This component unobtrusively displays the tree as an HTML table. The widget allows you to create several columns to display additional information besides the tree. The tree depth can be unlimited.

jQuery TreeTable is produced by Ludo van den Boom and is available under the GPLv2 and MIT licenses. In general, the jQuery JS library is high-speed and light, which significantly simplifies the web development process. It contains easy-to-use APIs that several browsers support.

Price​: free and open-source version.

jQuery TreeGrid 

Pic.6 jQuery TreeGrid

4. The DHTMLX TreeGrid component​

DHTMLX Treegrid ​is an extension of the dhtmlxGrid component that complements the functionality of the powerful data grid with extensible strings, competent XML parsing, elegant rendering, and pagination support. Based on AJAX, the Treegrid JavaScript component with dynamic loading is capable of displaying an unlimited number of rows and processing large amounts of data on the fly.

It provides end-users with convenient built-in editing, advanced dragging of rows and columns, split mode, sorting and coloring of posts, and several mathematical formulas. Also, dhtmlxTreeGrid can be easily styled using CSS and built-in skins. It is available as part of the ​dhtmlxSuite​JavaScript UI framework or as a standalone component within the Professional version.

Price​: free and open-source version. Commercial licenses start at $ 79 / a component.

DHTLMX TreeGrid 

Pic.7 DHTLMX TreeGrid ​

5. Sencha Tree Grid Widget​

Sencha ​has a JavaScript framework that provides several ready-made UI (user interface) components for creating high-load, cross-platform web applications. One of the essential elements of this library is its high-performance Tree Grid widget.

This component has such features as sorting, multiple headers, filtering, keyboard navigation, preloading nodes with a single AJAX request, hiding / displaying / reorganizing / resizing the header, custom icons, and so on. Sencha Ext JS is available under the name of GNU GPLv3 and Commercial licenses.

Price​: free and open-source version. Commercial licenses start at $ 1295 / a package.

Sencha TreeGrid 

Pic.8 Sencha TreeGrid

Let’s compare the key features of the JavaScript mentioned above TreeTable widgets.

Parameters that we have used for comparison:

  • Fully editable – the ability to edit data on the client-side, directly in the browser;
  • Saving current state – saving data and table status when the browser is restarted, or the session is terminated;
  • Selection modes – a variety of methods for selecting data (multi-select, using the mouse and keyboard);
  • Data export – the ability to export data;
  • Checkboxes – built-in Checkboxes support;
  • Filtering – the presence of filters for selecting the necessary data;
  • Clipboard – clipboard support (one of the editing features);
  • Sparklines – built-in graphs for data series.

Comparison table of the components overview

Pic.9 Comparison table of the components overview

As you can see from the table, some developers have gone much further, offering a complete UI solution for working with data.

Conclusion

The main purpose of the TreeGrid JavaScript component is to combine tree and table functions in a single widget. In this article, we have reviewed the TOP 5 JavaScript Tree Grid widgets.

All are their remarkable functions and characteristics. Based on the comparison diagram, the DXTMLX and Webix JS libraries are the most versatile TreeGrid widgets that help integrate the tree into an editable grid, making it easier to work with large amounts of data.

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How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

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How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

6 Reasons to Transcribe Audio to Text

3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output. 

Conclusion

From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.

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