SCAlabUnical

ADAGE

ADAGE (A Data-Aware scheduler based on critical path, work assiGnmEnt, and replication) is a data-aware scheduler designed to efficiently execute data-intensive workflows in a large-scale computer network.

AUDESOME

AUDESOME (AUtomatic Detection of User TrajEctories from SOcial MEdia posts) is an automatic method aimed at discovering user mobility patterns from posts they have shared on social media

BERT-XDD

BERT-XDD is a deep learning methodology for effective and interpretable depression detection from social media posts.

BigDataAnalysisExamples

Scalable Big Data Analysis Examples

G-RoI

G-RoI: Automatic Region-of-Interest detection driven by geotagged social media data

HASHET

HASHET (HAshtag recommendation using Sentence-to-Hashtag Embedding Translation) is a model aimed at suggesting a relevant set of hashtags for a given post.

IOM-NN

IOM-NN (Iterative Opinion Mining using Neural Networks) is a software for discovering the polarization of social media users during election campaigns characterized by the competition of political factions. The methodology uses an automatic incremental procedure based on feed-forward neural networks for analyzing the posts published by social media users. Starting from a limited set of classification rules, created from a small subset of hashtags that are notoriously in favor of specific factions, the methodology iteratively generates new classification rules. Such rules are then used to determine the polarization of people towards a faction.

ParSoDA

ParSoDA (Parallel Social Data Analytics) is a Java library for social media analytics

ParSoDA-Py

Translation of ParSoDA (Parallel Social Data Analytics) in PyCOMPSs

TM-FID

TM-FID (Topic-oriented Multimodal False Information Detection)

SCAlabUnical/HASHET

HASHET (HAshtag recommendation using Sentence-to-Hashtag Embedding Translation) is a model aimed at suggesting a relevant set of hashtags for a given post.

SCAlabUnical/AUDESOME

AUDESOME (AUtomatic Detection of User TrajEctories from SOcial MEdia posts) is an automatic method aimed at discovering user mobility patterns from posts they have shared on social media

SCAlabUnical/ParSoDA

ParSoDA (Parallel Social Data Analytics) is a Java library for social media analytics

SCAlabUnical/BERT-XDD

BERT-XDD is a deep learning methodology for effective and interpretable depression detection from social media posts.

SCAlabUnical/G-RoI

G-RoI: Automatic Region-of-Interest detection driven by geotagged social media data

SCAlabUnical/IOM-NN

IOM-NN (Iterative Opinion Mining using Neural Networks) is a software for discovering the polarization of social media users during election campaigns characterized by the competition of political factions. The methodology uses an automatic incremental procedure based on feed-forward neural networks for analyzing the posts published by social media users. Starting from a limited set of classification rules, created from a small subset of hashtags that are notoriously in favor of specific factions, the methodology iteratively generates new classification rules. Such rules are then used to determine the polarization of people towards a faction.

SCAlabUnical/ADAGE

ADAGE (A Data-Aware scheduler based on critical path, work assiGnmEnt, and replication) is a data-aware scheduler designed to efficiently execute data-intensive workflows in a large-scale computer network.

SCAlabUnical/ParSoDA-Py

Translation of ParSoDA (Parallel Social Data Analytics) in PyCOMPSs

SCAlabUnical/TM-FID

TM-FID (Topic-oriented Multimodal False Information Detection)

SCAlabUnical/BigDataAnalysisExamples

Scalable Big Data Analysis Examples

SCAlabUnical/BLEST-ML

BLEST-ML (BLock size ESTimation through Machine Learning) is a methodology for block size estimation that relies on supervised machine learning techniques.

SCAlabUnical/CompareDimensions

Compare size is a plugin to compare product sizes on e-commerce platforms

SCAlabUnical/Geocon

SCAlabUnical/IIWM

Intelligent In-memory Workflow Manager (IIWM) is a solution for optimizing the in-memory execution of data-intensive workflows on parallel machines. IIWM is based on two complementary strategies: 1) a machine learning strategy for predicting memory occupancy and execution time of workflow tasks; 2) a scheduling strategy that allocates tasks to a computing node taking into account the (predicted) memory occupancy and execution time of each task, and the memory available on that node.

SCAlabUnical/SEDOM-DD

SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters) is a new method for discovering sub-events that take place at different locations after a disaster (e.g, collapsed buildings, broken gas pipes, flooding).

SCAlabUnical/Cryptocurrencies

Market and social data on a set of cryptocurrencies

SCAlabUnical/DiXtill

DiXtill

SCAlabUnical/H-ADAPTS

H-ADAPTS (Hashtag recommendAtion by Detecting and adAPting to Trend Shifts)

SCAlabUnical/JobOffersIT-IT

Job offers in the Italian IT sector

SCAlabUnical/LLM-Bias-Jailbreak

SCAlabUnical/ParTweetHydrator

SCAlabUnical/PronounceableStringGenerator

This Java package allows you to generate pronounceable random strings.

SCAlabUnical/Topic_HEC

SCAlabUnical/USA2020

SCAlabUnical/UserReviewDatasets

SCAlabUnical/WABC