How LeanXcale can solve the major challenges of data management for XR applications

In the XR (Extended Reality) domain, effective data management is crucial but challenging. The top challenges faced in this area are mostly related with the data volume and variety, the real-time data processing and the data integration and interoperability.


Regarding data volume and variety, XR applications often involve high-resolution video, 3D models and detailed textures, which require substantial storage space. Moreover, XR devices use various sensors (e.g., cameras, gyroscopes, accelerometers) to capture real-time environmental data, while detailed logs of user interactions, such as gestures, eye movements, and voice commands, contribute to the data volume. In fact, efficient storage solutions are needed to manage this data. Traditional storage systems might not be sufficient, requiring scalable cloud-based solutions or specialized databases designed to handle large volumes of diverse data types. To make things worse, XR applications necessitate advanced processing techniques, combining visual data with sensor data to create a coherent and interactive XR experience that is always computationally intensive.


Regarding real-time data processing, XR applications require minimal latency to maintain immersion and prevent user discomfort. Delays in data processing can lead to lag, which can be particularly problematic in VR and AR experiences.  Real-time data streaming from sensors to the processing units and back to the display devices is also crucial. This involves not only capturing the data quickly but also transmitting it efficiently. Analyzing data in real-time to provide immediate feedback is critical. For example, an AR application that provides real-time navigation assistance needs to process GPS and visual data instantaneously to offer accurate directions.


For what concerns data integration and interoperability, XR applications often need to integrate data from multiple sources, having different data formats and protocols. The lack of standardized data formats and communication protocols can hinder integration efforts. Developing and adopting industry-wide standards is essential for seamless interoperability, while using APIs and middleware solutions can help in bridging the gap between different systems and devices. These tools facilitate data exchange and integration, but they must be designed to handle the specific requirements of XR data.


LeanXcale can play a crucial role in dealing with all these challenges. The LeanXcale database is a novel distributed database, which can be cloud based, ultra-scalable with elasticity capabilities. It can handle the vast volume of data that XR applications necessitate and scale out its data nodes horizontally as the size of the data grows. Its novel indexing mechanism, which includes global, local and covered indexes, allows for efficient query processing with minimum latency. LeanXcale also allows for data ingestion at very high rates, making it ideal for XR devices that generates data with high velocity and requires the transmission and capture of this data effectively with no delays. It is an SQL compliant database, thus supports the rich expressity of the relational query language to be used for complex query processing. This can be combined with one of its most important innovations, the online aggregates, which calculate aggregation operations incrementally, thus enabling real-time processing and analytics, providing immediate results, a critical challenge in the XR domain. Last but not least, the LeanXcale database is compliant with industry standards and popular analytical frameworks, facilitating the integration and interoperability.