kienpt / ache

ACHE focused crawler

ACHE focused crawler

Introduction

ACHE is an implementation of a focused crawler. A focused crawler is a web crawler that collects Web pages that satisfy some specific property, by carefully prioritizing the crawl frontier and managing the hyperlink exploration process 1.

Installation

Download with Conda

You can download ache from Binstar 2 with Conda 3 by running:

  conda install -c memex ache

Build from source with Gradle

To build ache from source, you can run the following commands in your terminal:

  git clone https://github.com/chdoig/ache.git
  cd ache
  ./gradlew clean installApp

which will generate an installation package under /build/install/.

Alternatively, you can build a zip archive:

  git clone https://github.com/chdoig/ache.git
  cd ache
  ./gradlew clean distZip

which will generate a zip file of your project under /build/distributions/.

Learn more about Gradle: http://www.gradle.org/documentation.

Running

Building a model

To run the ache crawler, you'll first need to build a model.

$ ache buildModel <target storage config path> <training data path> <output path>

<target storage config path> is the path to the configuration of the target storage.

<training_data> is the path to the directory containing positive and negative examples.

<output path> is the new directory where you want to save the generated model files: pageclassifier.model and pageclassifier.features.

For example:

ache buildModel conf/sample_crawl/target_storage.cfg training_data models/sample_model/

Running a crawl

To start a crawl, run:

ache startCrawl <data output path> <config path> <seed path> <model path> <lang detect profile path>

<data output path> is the path to the directory you want to store your output.

<config path> is the path to the config directory.

<seed path> is the path to the seed list file.

<model path> is the path to the model directory (containing pageclassifier.model and pageclassifier.features).

<lang detect profile path> is the path to the language detection profile. Note: We are currently refactoring the code. You'll be able to find it under resources in the near future. You can currently download here: https://code.google.com/p/language-detection/wiki/Downloads.

For example,

$ ache startCrawl sample_crawl conf/sample_config seeds/sample_crawl.seeds models/sample_model/ libs/profiles/

Requirements

To use ache, you'll need the following:

• JDK 1.6+