Event Correlation and Changing Detection Algorithm
Project description
pyspark-event-correlation
Event Correlation and Changing Detection Algorithm
Theory
Correlating events in complex and dynamic IoT environments is a challenging task not only because of the amount of available data that needs to be processed but also due to the call for time efficient data processing. We propose the adoption of a univariate change detection algorithm for real-time event detection and we implement a stepwise event correlation scheme based on a first-order Markov model.
Requirements
- Python 3.6 to 3.10 supported.
- pyspark 3.2.1 supported.
Installation
- Install with pip:
python -m pip install pyspark-event-correlation
Example Project
See the example project in the example/ directory of the GitHub repository.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pyspark-event-correlation-1.0.2.tar.gz.
File metadata
- Download URL: pyspark-event-correlation-1.0.2.tar.gz
- Upload date:
- Size: 3.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
bde407e9e31ac115eebbb3a3df9173a3a925bc7940cbdcad1edca5b9671ee152
|
|
| MD5 |
dbbfe5e56c6db5474fa589310cbbc8b1
|
|
| BLAKE2b-256 |
dd71d9c534c25148918c218e790d28cc7e37dbb0d8fd81c9b9972d21f8da3dea
|
File details
Details for the file pyspark_event_correlation-1.0.2-py3-none-any.whl.
File metadata
- Download URL: pyspark_event_correlation-1.0.2-py3-none-any.whl
- Upload date:
- Size: 4.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
604ef3e7e219b84075149033ded7ec4646f95debd16b4d62678335f99029638d
|
|
| MD5 |
cc50477c6039071bf3005ef87737a996
|
|
| BLAKE2b-256 |
7c4c58ca2f45ae8794e35d3dedfcd5e0424c862f467b371438614317379f2b08
|