Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a workflow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF reanalysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

In this paper we present SOA based CLAs12 event Reconstruction and Analyses (CLARA) framework used to develop Earth Science multi-sensor data fusion, processing, and analytics applications (NAIADS: NASA JLAB collaboration). CLARA design focus is on two main traits: a) real-time data stream processing, and b) service oriented architecture (SOA) in a flow based programming (FBP) paradigm. Data driven and data centric architecture of CLARA presents an environment for developing agile, elastic, multilingual data processing applications. The CLARA framework presents solutions, capable of processing large volumes of data interactively and substantially faster than batch systems.

English. We describe the creation of HurtLex, a multilingual lexicon of hate words. The starting point is the Italian hate lexicon developed by the linguist Tullio De Mauro, organized in 17 categories. It has been expanded through the link to available synset-based computational lexical resources such as Mul-tiWordNet and BabelNet, and evolved in a multi-lingual perspective by semiautomatic translation and expert annotation. A twofold evaluation of HurtLex as a resource for hate speech detection in social media is provided: a qualitative evaluation against an Italian annotated Twitter corpus of hate against immigrants, and an extrinsic evaluation in the context of the AMI@Ibereval2018 shared task, where the resource was exploited for extracting domain-specific lexicon-based features for the supervised classification of misogyny in English and Spanish tweets.Italiano. L'articolo descrive lo sviluppo di Hurtlex, un lessico multilingue di parole per ferire. Il punto di partenzaè il lessico di parole d'odio italiane sviluppato dal linguista Tullio De Mauro, organizzato in 17 categorie. Il lessicoè stato espanso sfruttando risorse lessicali sviluppate dalla comunità di Linguistica Computazionale come MultiWordNet e Babel-Net e le sue controparti in altre lingue sono state generate semi-automaticamente con traduzione ed annotazione manuale di esperti. Viene presentata sia un'analisi qualitativa della nuova risorsa, mediante l'analisi di corpus di tweet italiani annotati per odio nei confronti dei migranti e una valutazione estrinseca, mediante l'uso della risorsa nell'ambito dello sviluppo di un sistema Automatic Misogyny Identification in tweet in spagnolo ed inglese.