Hypercane

Hypercane is a framework for building algorithms for sampling mementos from a web archive collection. Hypercane is the entry point of the Dark and Stormy Archives (DSA) toolkit. A user can generate samples with Hypercane and then view those samples via the Web Archive Storytelling tool Raintale, thus allowing the user to automatically summarize a web archive collection as a few small samples visualized as a social media story.

The possibilities with Hypercane do not stop there. Users can employ Hypercane actions to explore a web archive collection through different actions. This README will provide an overview of these actions, but more detailed documentation is forthcoming.

Installing Hypercane

Using PIP

1. Install MongoDB
2. Clone this repository
3. Change into the cloned directory
4. Type pip install .

This grants access to the hc command which provides the functionality of Hypercane.

Using Docker

The software is still volatile, so you will need to build your own docker image.

1. Clone this repository
2. Run docker-compose run hypercane hc --help

This may take a while to download and build necessary docker images. When successful, hc CLI help will be printed.

Running Hypercane

Hypercane allows you to perform actions on web archive collections, TimeMaps, or lists of Mementos.

For example, the following sample action executes the random command to randomly sample mementos from the TimeMaps supplied by timemap-file.txt and writes the URI-Ms to random-mementos.txt:

hc sample true-random -i timemaps -a timemap-file.txt -o random-mementos.txt

At the moment, the following actions are supported:

• sample - generate a sample from the collection with various commands, some of the commands may execute various filter, cluster, score, and order actions
• report - generate a report on the collection according to various commands, different commands provide information on collection metadata or provide statistics on the collection
• synthesize - sythesize a web archive collection into the a directory containing files, such as warcs or files
• identify - produce a list of identifiers (URIs) from the collection based on the input, the different commands indicate the type of web resource desired
• filter - filter the given collection according to the criteria specified by the given command
• cluster - group the documents identified from the input into clusters, different commands provide different clustering algorithms
• score - score the mementos from the input based on the command issued
• order - order the mementos from the input based on the command issued

To discover the list of commands associated with an action, use the --help command-line option. For example, to discover the commands associated with the filter action, type hc filter --help.

Running Hypercane with Docker Compose

1. Build the software as specified in the Installing Hypercane - Using Docker subsection above
2. Create a working directory for your project
3. Copy docker-compose.yml into your working directory
4. Type docker-compose run hypercane
5. Run your desired commands, output will appear within your working directory
6. When done, exit from the hypercane container by running exit
7. To stop and remove all the services (such as the cache), run docker-compose down

The Future of Hypercane

We are working on additional sampling algorithms and options for the advanced actions. Please feel free to submit issues and pull requests at https://github.com/oduwsdl/hypercane