The Internet of Things

The Internet of Things (IoT) is using billions of sensors and devices to collect highly specific time series data, and for that, you need a time series database.

Time series data requires massive ingestion rates and the ability to query data across time and geographical location to understand trends and insights from the data.

Mediaflux is designed specifically to ingest, store and run statistical analysis for large sets of timestamped data – simply, efficiently and at scale – from right inside the platform.

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As a unification system for all time series data, Mediaflux makes it simple to extract important or useful information from petabytes of data collected through a single unified view.

Assets and metadata make it exponentially faster to measure changes, analyse past changes, monitor current changes, and predict future changes from billions of points in time.

Diagram showing how time series data is ingested by Mediaflux: Sensors (1) Mediaflux (2) Storage (3) Mediaflux Desktop (4) Metadata and time series data (5) Insight
  1. Sensor data sends time‐stamped readings from a sensor in a constant stream from the device to Mediaflux where it is automatically.
  2. Mediaflux configuration sends data to different storage types and vendors based on maximum cost‐benefit. At any time, data can be transparently migrated from one storage to another without any change to the end‐users’ pathway to find it.
  3. Mediaflux automatically filters and groups time series data into the correct place categorised by assets.
  4. In addition to using assets to manage time series more easily, Mediaflux gives that asset metadata to manage the assets more easily.
  5. Using metadata and assets, it is simple to run complex queries through Mediaflux Desktop for statistical analysis to unlock insights.

A Feature rich database with immense speed and scale.

Mediaflux really comes into its own when there are thousands and thousands of “things” recording information that require complex statistical analysis. Mediaflux also supports subsampling, aggregate analysis, and spatio‐temporal aggregate computing – for millions of data points in seconds.


Fragments of metadata make it unlocking the valuable sensor data to run processes such as the average value, sum, maximum value, and minimum value quickly on large data sets.

For example

A fridge manufacturer needs to collect the electricity consumption data of all the fridges of a particular model, to assess how to save and control energy. To check the energy consumption of the past 12 months, the administrator can directly obtain data from Mediaflux. The administrator can calculate data at a coarser time granularity such as by day, week, or month to view consumption trends.

Aggregate computing

Time series data aggregation provides powerful and flexible capabilities to analyse different query dimensions in real time, without the need to create any index information.

For example

To check the stopping time of vehicles for a specific traffic condition, the administrator only needs to send a request containing the specific traffic condition. Then, the administrator can obtain the stopping time of all the vehicles that have been exposed to that event in real time. To check the stopping time of a particular manufacturer, the administrator only needs to submit the manufacturer value to Mediaflux. Vehicle stopping time can also be checked by any other defined value, such as weather condition or travelling speed above X.

Time and space analysis

With more intelligent transport systems, automated vehicles being tested and others already emerging in the market, data storage and time series analysis scenarios of geographical location are able to unlock even richer insights.

For example

In testing autonomous vehicles, an engineer might be interested in assessing all the lidar imagery of a certain location for all vehicles operating on iteration “Z” of the software. What Mediaflux can do is build a gate around those coordinates and then filter each run within the geo fence to show only the runs where the vehicle was using iteration “Z”. Improving the safety and operations of automated vehicles depends on spatio‐temporal analysis capability.

And that's just scratching the surface.

Learn more about how Time Series on Mediaflux can be used by IoT data scientists to make sense of this flood of data, speed up discovery, and discuss some of the current and future applications in geospatial mapping and autonomous vehicles.