GIAR

Data Mining and Machine Learning

A service implemented for the workgroup project of the Data Mining and Machine Learning course of the Artificial Intelligence and Data Engineering Master Degree at University of Pisa.

1. Project Documentation
2. Training-set
3. Training-set data distribution
4. Final Test datasets
5. Test datasets confusion matrices
6. Weka test result buffers

Credits

Application designed and developed by Barigliano Lorenzo, Serao Gianluca.

Large-Scale and Multi-Structured Databases

A service for the workgroup tasks of the Large-Scale and Multi-Structured Databases course of the Artificial Intelligence and Data Engineering Master Degree at University of Pisa.

Task 2

1. Design
2. Implementation
3. Indexes Performance Study
4. Tests
5. User Manual

Task 3

1. Design Graph
2. Implementation Graph
3. User Manual (Same of task 2)

Credits

Application designed and developed by Barigliano Lorenzo, Gómez Marsha, Mazzini Matilde, Serao Gianluca.

Application installation

Databases configuration

Neo4j: Install the database in your computer and connect as localhost replacing username and password with yours:

driver = GraphDatabase.driver("bolt://localhost:7687", AuthTokens.basic("username", "password"));

MongoDB: Install the database in your computer and create the giar database with the users and the game collections as described in the Design document.

The connection string is set to localhost:

client = MongoClients.create("mongodb://localhost:27017/");

Twitter API configuration

To run the sentiment analysis a Twitter API token is required. To load the token create a twitter4j.properties file in the resources folder. The file must contain the following lines:

http.useSSL=true
oauth.consumerKey =       Twitter_API_Token
oauth.consumerSecret =    Twitter_API_Token
oauth.accessToken =       Twitter_API_Token
oauth.accessTokenSecret = Twitter_API_Token