IN RECENT YEARS, THE USE OF SOCIAL NETWORKS HAS INCREASED EXPONENTIALLY, WHICH HAS LED TO A SIGNIFICANT INCREASE IN CYBERBULLYING. CURRENTLY, IN THE FIELD OF COMPUTER SCIENCE, RESEARCH HAS BEEN MADE ON HOW TO DETECT AGGRESSIVENESS IN TEXTS, WHICH IS A PRELUDE TO DETECTING CYBERBULLYING. IN THIS FIELD, THE MAIN WORK HAS BEEN DONE FOR ENGLISH LANGUAGE TEXTS, MAINLY USING MACHINE LEARNING (ML) APPROACHES, LEXICON APPROACHES TO A LESSER EXTENT, AND VERY FEW WORKS USING HYBRID APPROACHES. IN THESE, LEXICONS AND MACHINE LEARNING ALGORITHMS ARE USED, SUCH AS COUNTING THE NUMBER OF BAD WORDS IN A SENTENCE USING A LEXICON OF BAD WORDS, WHICH SERVES AS AN INPUT FEATURE FOR CLASSIFICATION ALGORITHMS. THIS RESEARCH AIMS AT CONTRIBUTING TOWARDS DETECTING AGGRESSIVENESS IN SPANISH LANGUAGE TEXTS BY CREATING DIFFERENT MODELS THAT COMBINE THE LEXICONS AND ML APPROACH. TWENTY-TWO MODELS THAT COMBINE TECHNIQUES AND ALGORITHMS FROM BOTH APPROACHES ARE PROPOSED, AND FOR THEIR APPLICATION, CERTAIN HYPERPARAMETERS ARE ADJUSTED IN THE TRAINING DATASETS OF THE CORPORA, TO OBTAIN THE BEST RESULTS IN THE TEST DATASETS. THREE SPANISH LANGUAGE CORPORA ARE USED IN THE EVALUATION: CHILEAN, MEXICAN, AND CHILEAN-MEXICAN CORPORA. THE RESULTS INDICATE THAT HYBRID MODELS OBTAIN THE BEST RESULTS IN THE 3 CORPORA, OVER IMPLEMENTED MODELS THAT DO NOT USE LEXICONS. THIS SHOWS THAT BY MIXING APPROACHES, AGGRESSIVENESS DETECTION IMPROVES. FINALLY, A WEB APPLICATION IS DEVELOPED THAT GIVES APPLICABILITY TO EACH MODEL BY CLASSIFYING TWEETS, ALLOWING EVALUATING THE PERFORMANCE OF MODELS WITH EXTERNAL CORPUS AND RECEIVING FEEDBACK ON THE PREDICTION OF EACH ONE FOR FUTURE RESEARCH. IN ADDITION, AN API IS AVAILABLE THAT CAN BE INTEGRATED INTO TECHNOLOGICAL TOOLS FOR PARENTAL CONTROL, ONLINE PLUGINS FOR WRITING ANALYSIS IN SOCIAL NETWORKS, AND EDUCATIONAL TOOLS, AMONG OTHERS.

WE PRESENT EFFICIENT ALGORITHMS TO COMPUTE SIMPLE AND COMPLEX MAP ALGEBRA OPERATIONS OVER RASTER DATA STORED IN MAIN MEMORY, USING THE K2-ACC COMPACT DATA STRUCTURE. RASTER DATA CORRESPOND TO NUMERICAL DATA THAT REPRESENT ATTRIBUTES OF SPATIAL OBJECTS, SUCH AS TEMPERATURE OR ELEVATION MEASURES. COMPACT DATA STRUCTURES ALLOW EFFICIENT DATA STORAGE IN MAIN MEMORY AND QUERY THEM IN THEIR COMPRESSED FORM. A K2-ACC IS A SET OF K2-TREES, ONE FOR EVERY DISTINCT NUMERIC VALUE IN THE RASTER MATRIX. WE DEMONSTRATE THAT MAP ALGEBRA OPERATIONS CAN BE COMPUTED EFFICIENTLY USING THIS COMPACT DATA STRUCTURE. IN FACT, SOME MAP ALGEBRA OPERATIONS PERFORM OVER FIVE ORDERS OF MAGNITUDE FASTER COMPARED WITH ALGORITHMS WORKING OVER UNCOMPRESSED DATASETS.