Streaming of High-Velocity Information using Dynamic Spatio-Temporal Query Processing

In the event that the status of the query is modified in any way, a restart is required. This reduces the responsiveness of the system and, in the worst-case situation, produces results that are either wrong or out of date. In this work, we recommend using techniques from stream database, spatial information management, and computing systems to improve the processing of dynamic spatio-temporal queries across high-velocity large data streams. We provide strategies for streaming spatio-temporal querying and suggest spatial having earlier on geohashes for efficient parallel computing. Both of these are shown below. The in-memory batch processing that Apache Flink provides is compatible with our spatially ideas and performs very well with them. The viability of our strategy is proved by a comparative examination of our prototype, which maintains high consistent response times for both static and shifting query over high velocity spatiotemporal big data streams. This result demonstrates that our technique is successful.