Connectivity resilience
Once you have the graph in your mobile device, your analytics do not depend on the cloud.
Sparksee · graph database management system
The graph engine that ships inside your product.
High-performance, compact, embeddable. Sparksee represents and analyses complex interrelationships between entities, with the security and integrity guarantees enterprise data demands.
Why Sparksee
Six reasons teams choose an embedded graph store over a clustered one.
Increased privacy
Enables the creation of privacy preserving apps, where data is stored in the user's device.
Reduced memory usage
Sparksee has a compact data representation and management that reduces the amount of required resources.
Advanced algorithms
Our built-in algorithms accelerate the creation of applications with out-of-the-box tooling.
Recovery
Sparksee technology is data resilient, so that you don't have to worry about crashes.
Reduced latency
Our graph database is fast at computing on your mobile device, and you do not need to wait for a server's response.
Why are companies using Sparksee?
The technologies provided by Sparksee are compact thanks to the use of bitmaps which provide high performance in the computations of graph algorithms in the mobile or embedded environment.
Sparksee is out of core, providing a persistent data store at the same time. Also, Sparksee provides special features like encryption at the disk level, which allows to preserve the privacy of the data in the case of theft, loss or sale of the device, and recovery of the database in the case of database corruption.
High performance graph computations
Having a GDB in the mobile device allows for preserving the service to the user in the case of bad connectivity or off-line operation, and better latency in the case of saturated networks. Finally, saving cloud operation at the cost of mobile calculations may save a significant amount of money to the provider of the service.
About our patented bitmaps solution
New way to represent graphs to encourage multilevel memories:
- Graph is split into smaller structures to favor the caching of significant parts
- Object identifiers are used to reduce memory requirements (OIDS)
- Specific structures to help in the navigation and traversal of edges
- Attributes are fully indexed to allow queries based on filters
Free trial
Try Sparksee on your own data.
Tell us who you are and what you're building. We send the trial build and a modelling engineer's email address — not a drip campaign.