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What is ZingChart?

Value Boxes are an essential feature for smaller datasets, providing clear visibility of each node's exact values. When enabled, these boxes prominently showcase the value associated with each node, making it easy for users to comprehend the data. However, in situations involving slightly larger datasets, displaying value boxes for every single node may create visual confusion, prompting the use of tooltips that allow users to view a node's value on demand. For instances where tooltips fall short in delivering sufficient information, crosshairs present a simple yet effective way to analyze a spectrum of values. In the case of very large datasets, where users need to zoom in significantly to identify node values, the ability to magnify specific points helps reduce the difficulty of focusing on the data. Furthermore, the inclusion of scroll bars significantly improves the navigation experience, enabling users to move through the dataset more efficiently. The preview window plays a dual role by assisting users in monitoring their current zoom level while also facilitating quick adjustments to the scroll position. Collectively, these features empower users to engage with the data efficiently, regardless of the size of the dataset, ensuring that they have the tools necessary to derive meaningful insights. This comprehensive approach to data interaction enhances user experience across various dataset scales.

What is Sparksee?

Sparksee, formerly known as DEX, enhances both spatial efficiency and performance while keeping a streamlined design that allows for quick analysis of large-scale networks. It accommodates a variety of programming languages, such as .Net, C++, Python, Objective-C, and Java, which provides flexibility across numerous operating systems. The graph data is systematically organized using bitmap data structures, allowing for impressive compression ratios. These bitmaps are segmented into chunks that are aligned with disk pages, which improves input/output locality and leads to superior performance. By utilizing bitmaps, calculations are performed using binary logic instructions that support efficient processing in pipelined architectures. Additionally, the system includes comprehensive native indexing, ensuring swift access to all graph data structures. Connections between nodes are also represented as bitmaps, which further decreases their storage requirements. To optimize resource utilization, advanced I/O techniques are employed to limit the number of data pages loaded into memory. Each distinct value within the database is stored singularly, effectively reducing redundancy and enhancing overall efficiency. Consequently, this amalgamation of characteristics positions Sparksee as an exceptionally capable tool for conducting extensive graph data analyses, making it invaluable for researchers and data scientists alike.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

$249 one-time payment
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

ZingChart

Company Website

www.zingchart.com

Company Facts

Organization Name

Sparsity Technologies

Date Founded

2010

Company Location

Spain

Company Website

sparsity-technologies.com

Categories and Features

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

Categories and Features

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