There are many types of data in the Internet of Vehicles scenario, some of which are time-related and have a very large amount of data, such as motion track records, battery data, monitoring data…

Question

Options

• ATime series database
• BCloud database
• CDistributed database middleware
• DDocument database

• A
  72% (23)
• B
  9% (3)
• C
  16% (5)
• D
  3% (1)

Explanation

Time series databases are purpose-built for exactly this scenario: they excel at storing and querying large volumes of sequentially timestamped data, offering optimized write throughput, efficient time-range queries, and automatic data compression - all critical when continuously ingesting motion tracks, battery readings, and sensor streams from thousands of vehicles.

Option B (cloud database) describes a deployment model, not a data model, so it says nothing about how well the underlying engine handles time-stamped, high-frequency data. Option C (distributed database middleware) is a routing and sharding layer that sits in front of relational databases to scale them horizontally - useful for general scaling but not optimized for time-series ingestion patterns. Option D (document database) is designed for flexible, semi-structured records like JSON objects, and while it can store timestamped data, it lacks the specialized indexing and compression that make time-series workloads efficient.

Memory tip: Match the data shape to the database name. If the data is a continuous stream of measurements stamped with a time - speed at 10:00:01, battery at 10:00:02, location at 10:00:03 - then "time series database" names exactly what you need.

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