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The edge AI application landscape is changing very fast. There is a big need for high-performance systems that handle big data in real time. One area that requires advanced development is in the area of time-series data management. With exponential growth in data volume, velocity, and variety coming from edge devices, businesses and organizations are opting for innovative solutions that can process large volumes of data with low latency and high efficiency.
Notable in this space is a high-performance time series object store, like ReductStore, that was specifically targeted at edge applications for AI. This solution combined blob storage with the management of time-series data, offering a viable means of dealing with big volumes of unstructured data, such as images, vibration data, and text, while ensuring that there was low-latency access to critical information.
In this blog, we’ll explore the future of time-series data management for edge AI systems and the key features driving the evolution of this field.
Understanding Time-Series Data in Edge AI
The sequence of data points indexed in chronological order is termed time-series data. These are generated by edge AI applications through a range of devices, including sensors, cameras, and other Internet of Things devices. These systems of Edge AI are reliant on real-time data for decisions, pattern recognition, and insights without the transmission of all information to the cloud. This is very critical in the need for responsive applications, for example, industrial monitoring and self-driving vehicles, among others, predictive maintenance systems.
However, managing and storing time series data is complicated, especially when related to unstructured data. A traditional database or storage product might not be able to handle the volume of the data or the need for access speed to specific data points. It is here that modern solutions for time-series data management come into play, and they can handle huge amounts of data while ensuring low latency and high throughput.
Key Features of Advanced Time-Series Data Storage
Several features of high-performance time-series storage solutions are setting the stage for the future of edge AI systems. These features not only improve the efficiency of data storage and management but also ensure that edge AI applications can scale up as the amount of generated data grows.
Unlimited Blob Size for Flexibility
A characteristic of modern time-series data storage solutions is unlimited blob size. The case with edge AI will be more diverse in forms of appearance since images, videos, sensor readings, and much more will constitute these appearances. It would therefore be essential that these vast blobs are stored and processed efficiently. With this storage system having support for unlimited blob size, all forms of data-intensive inputs are accounted for without performance degradation.
This flexibility is critical in edge environments where devices may generate humongous data sets, such as high-definition video streams or detailed sensor readings. Storing big data blobs without hitting the storage limits helps edge AI systems run smoothly even in very demanding situations.
Data Volume-based Retention Policies
Another major challenge in time-series data management is the retention of data. An edge AI system can produce a massive amount of data, much of which will never be needed after a certain period. Efficient data retention policies manage the costs of storage and improve performance by automatically archiving or deleting old or unnecessary data.
Volume retention policies ensure the right amount of data is retained for a longer time; older data is archived for long-term storage or deleted if it’s not required. In this way, no excess data that would not likely be retrieved again litters the system.
Iterative Data Queries for Real-Time Insights
The Edge AI systems are primarily built around real-time querying over big data. Iterative data querying enables an AI system to keep on querying and processing data as they come up without loading the entire dataset at each step. Hence, it helps in speedy decisions of the edge AI system concerning the latest information available, not held back by the slow times of data retrieval.
Another area where iterative querying would be beneficial is in high-frequency applications, such as sensor monitoring or autonomous driving scenarios where data is streamed continuously and needs to be processed in real time. Edge AI systems respond quickly and accurately to the changing environment through efficient optimization of the query.
Bending the Challenges of Unstructured Data
Advanced time series data management solutions can process unstructured data much better than traditional databases. This kind of data, such as images, videos, or raw sensor readings, is not easily formatted in a traditional database structure and is therefore very difficult to manage and analyze.
These advanced systems enable the handling of unstructured data as well as structured time series data in a single system via an advanced storage solution based on a combination of blob and time-series data management. This makes it possible for edge AI applications that handle both structured and unstructured data to make informed decisions.
To Summarize
The edge AI application becomes more crucial for the need to be scalable in managing time-series data with the advancement of its feature set. Organizations can benefit from using unlimited blob sizes, retention policies, efficient data batching, and label-based replication when developing their edge AI systems to handle massive volumes of data with low latency and high performance.
With vast amounts of data to process and analyze and the constant need for doing so without deviation, the near future in time-series management for edge AI systems promises to advance significantly. Advancements in edge AI systems that can easily process and correctly analyze gigantic amounts of data will ultimately lead to stronger insights and outcomes in myriad industries.
