This is a lightweight typescripted node.js tool used to track the metadata activity within a folder and it's sub-folders using chokidar. The raw information is then stored in a text file, which we can later be processed and analysed.
The tool was built as part of the EPSRC Language of Collaborative Manufacturing project and used to collect data on the digital activity of a Formula Student project. The aim was to see if the metadata could provide insights into the performance of the project and final product. This could then be used to support Engineering Project Management in real-time.
We want to encourage more Engineering Design researchers to capture and analyse the digital activity of engineering projects as we see it having great potential in providing insights for both practitioners and researchers.
The first thing you need to do to get up and running is to have node installed on your system. Node.js installers can be found here. To confirm that you have node installed and running on your system, you can open a terminal/command prompt window and run the following command:
$ node -v
This should display the version of node that you're running on your system.
Now that you have node installed, you can use the built-in node package manager npm to install the FolderActivityLogger onto your system. Again, in the terminal/command prompt run the following command.
$ npm install -g jamesgopsill/folder-activity-logger
This will install the FolderActivityLogger in your global node environment and enable you to call it anywhere on your system.
To use the logger, create a javascript file (e.g., index.mjs) and add the following.
import { FAL } from "../src"
const config = {
watchGlob: "path/you/wish/to/monitor",
logFilePath: "path/to/log.txt",
debug: false
}
const watcher = new FAL(config)
watcher.watch()
// Set a timeout to stop listening (if you wish)
setTimeout(() => {
watcher.stop()
}, 5000)
Then run the file in the your command line:
$ node ./index.mjs
You will need to keep the terminal window open for it to continue to capture changes to the meta-data.
The output is a simple txt file containing json objects on each line that detail an event that has occurred in the filesystem you're monitoring. The events that are captured are: add/addDir, change, unlink/unlinkDir.
The add, addDir and change events will contain the following information. This comes from node.js fs.stat function.
event: event type that was captured (add, addDir, change)path: path to the object (file/folder/symbolic link)dev: device number that the object is held onmode: information on the type of object and permissions of the objectnlink: hard linksuid: user-id of the owner of the objectgid: group-id of the owner of the objectrdev: device Id (if special file)blksize: block size for filesystem I/Oino: inode numbersize: size in bytesblocks: number of blocks allocatedatimeMs: time when file data was last accessed (milliseconds)mtimeMs: time when file data was last modified (milliseconds)ctimeMs: vtime when file status was last changed (milliseconds)birthtimeMs: time of file creation (only available on some systems, milliseconds)atime: time when file data was last accessedmtime: time when file data was last modifiedctime: time when file status was last changedbirthtime: time of file creation (only available on some systems)_id: unique database document idcreatedAt: date of creation of the documentupdatedAt: most recent update to the documentWhilst unlink/unlinkDir contain the date when the file/folder was removed from the folder.
event: event type that was captured (unlink, unlinkDir)path: file/folder path of interest_id: unique database document idcreatedAt: date of creation of the documentupdatedAt: most recent update to the documentIn addition, the tools produces three fal json objects to help identify key moments in the indexing and monitoring of the filesystem.
{"fal":"watch-started"}
{"fal":"indexing-complete"}
{"fal":"watch-stopped"}
Further documentation on the class and interfaces available in the package can be found here.
Please feel free to download and use this within your research projects. All we ask is that you cite our work in your paper and that you add your paper to the growing list of work that has used the tool in their studies! :)
Gopsill, J., Snider, C., McMahon, C., & Hicks, B. (2016). Automatic generation of design structure matrices through the evolution of product models. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 30(4), 424-445. doi:10.1017/S0890060416000391
@article{gopsill2016,
title={Automatic generation of design structure matrices through the evolution of product models},
volume={30},
DOI={10.1017/S0890060416000391},
number={4},
journal={Artificial Intelligence for Engineering Design, Analysis and Manufacturing},
publisher={Cambridge University Press},
author={Gopsill, James A. and Snider, Chris and McMahon, Chris and Hicks, Ben},
year={2016},
pages={424–445},
url={https://jamesgopsill.github.io/Publications/papers/journal/aiedam2016/aiedam2016.pdf}
}
Also, we would love to hear how you're using the tool and the studies that you're performing with it.
This is a list of papers that have used the tool in their studies.
Generated using TypeDoc