Time series data is crucial for the efficient operation of IoT devices such as smart cars and medical equipment, as it involves collecting data points timestamped over a period.

To explore the vital significance of time series data in the modern interconnected landscape, we had a discussion with Evan Kaplan, CEO of InfluxData, on our podcast.

Below is a summarized version of our conversation:

What is time series data?

Understanding time-based data collection is quite straightforward. Essentially, it involves gathering data through measurements over specific time intervals. Consider sensor analytics as an example. Sensors might monitor various parameters like pressure, temperature, or light levels, and data is typically logged with timestamps at regular intervals—be it every 30 seconds, each minute, or even every nanosecond. This process aims at instrumenting systems comprehensively to monitor their performance, detect any anomalies, and to further train AI models for future applications.

Conventionally, such data was stored in general databases, but over time, with the surge in data volume and the need for immediate processing, specialized time series databases have been developed. These specialized databases significantly enhance the capabilities of system architects to design advanced, real-time systems.

Take the example of a sensor embedded within a medical device, rapidly transmitting data. The question is whether it captures all data or primarily flags anomalies. The answer lies in the handling of both data in motion and data at rest. The system not only records extensive data points from multiple sensors every fraction of a second but also allows this data to be instantly queryable. While it’s a database designed to store and maintain records, it simultaneously supports comprehensive, long-term data analysis.

So storage, is that a big issue? If all this data is being thrown off, and if there are no anomalies, you could be collecting hours of data that nothing has changed?

If you’re dealing with a constant stream of data, particularly in regulated industries where long-term retention is required, it becomes crucial to master data compression techniques. Furthermore, managing an object storage format in performance-sensitive systems can pose challenges. Moreover, the ability to downsample data effectively is essential. For example, while data might be recorded every 10 milliseconds, summarizing it every 20 minutes helps in focusing on significant signals within those intervals. Efficient downsampling and eviction of redundant data, while retaining summary insights, are skills not all databases are equipped for, making them highly specialized and valuable not only for us but for our competitors too.

We were talking about edge devices and now artificial intelligence coming into the picture. So how does time series data augment those systems? Benefit from those advances? Or how can they help move things along even further?

Time series data is incredibly fundamental to technological advancements. If you consider systems developed 30 years ago, they were likely built on platforms like Oracle, Informatics, or IBM Db2, taking inspiration from real-time financial data on Wall Street. The new aspect today is the amplification in the sensification of the physical world. Everywhere, from medical devices to smart cities, public transport, cars, homes, and industrial settings, the integration of sensors is accelerating. Though “sensification” might not be a standard term, it aptly describes the ongoing transformation.

Sensors continuously capture data as a series of measurements; they convey information such as pressure, volume, humidity, and temperature over time. To develop an intelligent system, it’s paramount to begin with advanced instrumentation. If I aspire to create an exceptional self-driving car, I need a comprehensive, high-resolution understanding of both the car’s actions and its surrounding environment at all times. This detailed monitoring allows me to train a model to achieve awareness equivalent to or surpassing a human driver in future scenarios. This involves a cycle of instrumentation, observation, adjustment, and re-instrumentation, repeated numerous times.

Considering this, what future applications could we anticipate? You’ve mentioned a few scenarios involving cities and vehicles. What other fields might benefit from this technology?

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