Browse Source

Document PRIVA'MOV changes

master
Markus Becker 2 years ago
parent
commit
623fff13f9
  1. 4
      README.md

4
README.md

@ -15,7 +15,7 @@ After installing this tool using pip run:
loclib-ingest -p /path/to/privamov/folder -g /path/to/geolife/folder -d traces.db
```
This operation will take a while (fastest I've seen is around 30 minutes for PRIVA'MOV alone, around 10 for GeoLife), but the resulting database is both smaller, a lot easier to query and most importantly unifies the two very different datasets.
This operation will take a while (fastest I've seen is around 10 minutes for PRIVA'MOV alone, around 5 for GeoLife), but the resulting database is both smaller, a lot easier to query and most importantly unifies the two very different datasets.
The resulting SQLite database is kept very simple, only two tables are created:
@ -31,7 +31,7 @@ The resulting SQLite database is kept very simple, only two tables are created:
This means it is easy to query for all samples of a trace, or all samples within an area. It even allows to ignore the underlying datasets concept of traces entirely by querying for all samples belonging to a user within a certain timeframe.
It being a SQLite database doesn't bring the highest performance, you can expect around 5s for each trace query. I'll consider adding other database backends in the future.
It being a SQLite database doesn't bring the highest performance, you can expect around 5s for each trace query. I'll consider adding other database backends in the future. The ingest of PRIVA'MOV is parallelized across a number of processes (--cpu) to speed it up and chunked (--chunk) to reduce memory usage. If your ingest takes very long you can try to optimize: reduce the number of chunks as far as your RAM allows you to and increase the number of processes (up to the number of cpu threads), the major bottleneck is the saving into the SQLite database (which only one thread can do at a time).
## Method for rating traces

Loading…
Cancel
Save