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Last pushed: 8 months ago
Short Description
Anomaly detection based on environmnental data and correlation to events based on tweets information
Full Description

The image do not contains any tweets or sensing data, which should be collected before running the images. The process is as follows (note: information in <> is defined by user):
1) Run docker image with tag copyfiles:
$ docker run --name cf anomaly-detection:copyfiles
2) Copy all the files from the running container, i.e. cf, into a volume in the host:
$ docker cp cd:/app <volume_path_in_host>
3) Copy pre-collected sensing data and tweets into <volume_path_in_host>
4) Run ad_run.sh:
Method 1: run in terminal (require MCR 2017a installed in <mcr_directory>): $ ./ad_run.sh <mcr_directory> <sensing_data_filename>
Method 2: run in docker container: $ docker run -v <volume_path_in_host>:/app anomaly-detection:ad <sensing_data_filename>
Note: if <sensing_data_filename> is not provided, the program will use a default name: data.csv
5) Run TwiEvent.jar:
Method 1: run in terminal: $ java -jar TwiEvent.jar <filename_list_tweets_file_in_each_line>
Method 2: run in docker container: $ docker run -v volume_path_in_host:/app anomaly-detection:tx <filename_list_tweets_file_in_each_line>
Note: if <filename_list_tweets_file_in_each_line> is not provided, the program will use a default name: tweetslist.txt
6) Run TwitterCorr.jar:
Method 1: run in terminal: $ java -jar TwitterCorr.jar
Method 2: run in docker container: $ docker run -v volume_path_in_host:/app anomaly-detection:tc

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Owner
ikaas