| Nome |
# |
|
Analysis of Twitter Data Using a Multiple-level Clustering Strategy, file e384c42e-2bcc-d4b2-e053-9f05fe0a1d67
|
2.713
|
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Generalized association rule mining with constraints, file e384c42e-110c-d4b2-e053-9f05fe0a1d67
|
762
|
|
Energy Signature Analysis: Knowledge at Your Fingertips, file e384c42d-ba62-d4b2-e053-9f05fe0a1d67
|
564
|
|
Energy saving models for wireless sensor networks, file e384c42e-171b-d4b2-e053-9f05fe0a1d67
|
547
|
|
Expressive generalized itemsets, file e384c42e-3120-d4b2-e053-9f05fe0a1d67
|
546
|
|
Twitter data analysis by means of Strong Flipping Generalized Itemsets, file e384c42e-311f-d4b2-e053-9f05fe0a1d67
|
544
|
|
NetCluster: A clustering-based framework to analyze internet passive measurements data, file e384c42e-296a-d4b2-e053-9f05fe0a1d67
|
520
|
|
Modeling correlations among air pollution-related data through generalized association rules, file e384c42f-1713-d4b2-e053-9f05fe0a1d67
|
495
|
|
CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules., file e384c42e-10cf-d4b2-e053-9f05fe0a1d67
|
487
|
|
Misleading Generalized Itemset discovery, file e384c42e-2b2d-d4b2-e053-9f05fe0a1d67
|
472
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-13a8-d4b2-e053-9f05fe0a1d67
|
472
|
|
Digging deep into weighted patient data through multiple-level patterns, file e384c42d-b1f8-d4b2-e053-9f05fe0a1d67
|
468
|
|
NetCluster: a Clustering-Based Framework for Internet Tomography, file e384c42e-02bc-d4b2-e053-9f05fe0a1d67
|
466
|
|
NEMICO: Mining network data through cloud-based data mining techniques, file e384c42e-38c8-d4b2-e053-9f05fe0a1d67
|
425
|
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Early prediction of the highest workload in incremental cardiopulmonary tests, file e384c42e-2de4-d4b2-e053-9f05fe0a1d67
|
412
|
|
Frequent Itemsets Mining for Big Data: A Comparative Analysis, file e384c42f-9940-d4b2-e053-9f05fe0a1d67
|
398
|
|
Fault detection analysis of building energy consumption using Data Mining techniques, file e384c42e-2de5-d4b2-e053-9f05fe0a1d67
|
385
|
|
Analyzing Air Pollution on the Urban Environment, file e384c42f-1ab4-d4b2-e053-9f05fe0a1d67
|
385
|
|
Monitoring the citizens’ perception on urban security
in Smart City environments, file e384c42e-cf96-d4b2-e053-9f05fe0a1d67
|
375
|
|
MeTA: Characterization of medical treatments at different abstraction levels, file e384c42d-b258-d4b2-e053-9f05fe0a1d67
|
359
|
|
Predicting large scale fine grain energy consumption, file e384c42f-6fd3-d4b2-e053-9f05fe0a1d67
|
315
|
|
Network Digest analysis by means of association rules, file e384c42f-41e5-d4b2-e053-9f05fe0a1d67
|
277
|
|
Supporting stock trading in multiple foreign markets: a multilingual news summarization approach, file e384c42f-3fc3-d4b2-e053-9f05fe0a1d67
|
245
|
|
Exploiting clustering algorithms in a multiple-level fashion: A comparative study in the medical care scenario, file e384c42e-8ff7-d4b2-e053-9f05fe0a1d67
|
244
|
|
Discovering air quality patterns in urban environments, file e384c42f-13e2-d4b2-e053-9f05fe0a1d67
|
192
|
|
PaMPa-HD: A Parallel MapReduce-Based Frequent Pattern Miner for High-Dimensional Data, file e384c42e-d384-d4b2-e053-9f05fe0a1d67
|
190
|
|
Diversità è Eccellenza. Bilancio di Genere 2020 - Politecnico di Torino, file e384c432-d9a7-d4b2-e053-9f05fe0a1d67
|
170
|
|
DSLE: A Smart Platform for Designing Data Science Competitions, file e384c432-5eb3-d4b2-e053-9f05fe0a1d67
|
161
|
|
Data mining for better healthcare: A path towards automated data analysis?, file e384c42f-3998-d4b2-e053-9f05fe0a1d67
|
157
|
|
SaFe-NeC: A scalable and flexible system for network data characterization, file e384c42f-45ab-d4b2-e053-9f05fe0a1d67
|
154
|
|
Optimization of Computer Aided Detection systems: an evolutionary approach, file e384c42f-ebfc-d4b2-e053-9f05fe0a1d67
|
152
|
|
Useful ToPIC: Self-tuning strategies to enhance Latent Dirichlet Allocation, file e384c430-3362-d4b2-e053-9f05fe0a1d67
|
120
|
|
E-MIMIC: Empowering Multilingual Inclusive Communication, file e384c434-3cdb-d4b2-e053-9f05fe0a1d67
|
113
|
|
Exploring energy performance certificates through visualization, file e384c431-06f0-d4b2-e053-9f05fe0a1d67
|
106
|
|
Automating concept-drift detection by self-evaluating predictive model degradation, file e384c430-f738-d4b2-e053-9f05fe0a1d67
|
105
|
|
Predicting cardiopulmonary response to incremental exercise test, file e384c42e-9687-d4b2-e053-9f05fe0a1d67
|
86
|
|
A fog computing approach for predictive maintenance, file e384c431-c6da-d4b2-e053-9f05fe0a1d67
|
71
|
|
Data-Driven Estimation of Heavy-Truck Residual Value at the Buy-Back, file e384c432-67b2-d4b2-e053-9f05fe0a1d67
|
68
|
|
Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools, file e384c432-a64a-d4b2-e053-9f05fe0a1d67
|
62
|
|
A cloud-to-edge approach to support predictive analytics in robotics industry, file e384c431-d742-d4b2-e053-9f05fe0a1d67
|
61
|
|
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction, file e384c434-1d09-d4b2-e053-9f05fe0a1d67
|
54
|
|
REDTag: A Predictive Maintenance Framework for Parcel Delivery Services, file e384c431-b91a-d4b2-e053-9f05fe0a1d67
|
52
|
|
Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine, file e384c430-c403-d4b2-e053-9f05fe0a1d67
|
51
|
|
Empowering Commercial Vehicles through Data-Driven Methodologies, file e384c434-43fb-d4b2-e053-9f05fe0a1d67
|
50
|
|
Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case, file e384c433-5ac2-d4b2-e053-9f05fe0a1d67
|
49
|
|
What's in the box? Explaining the black-box model through an evaluation
of its interpretable features, file e384c430-dac7-d4b2-e053-9f05fe0a1d67
|
47
|
|
A data-driven energy platform: from energy performance certificates to human-readable knowledge through dynamic high-resolution geospatial maps, file e384c432-dc2b-d4b2-e053-9f05fe0a1d67
|
46
|
|
Characterizing situations of dock overload in bicycle sharing stations, file e384c430-bc23-d4b2-e053-9f05fe0a1d67
|
45
|
|
Prompting the data transformation activities for cluster analysis on collections of documents, file e384c430-1f1c-d4b2-e053-9f05fe0a1d67
|
42
|
|
Discovering profitable stocks for intraday trading, file e384c433-18e7-d4b2-e053-9f05fe0a1d67
|
42
|
|
Towards automated visualisation of scientic literature, file e384c430-ca90-d4b2-e053-9f05fe0a1d67
|
41
|
|
Robot fault detection and remaining life estimation for predictive maintenance, file e384c431-8102-d4b2-e053-9f05fe0a1d67
|
39
|
|
Modeling Urban Traffic Data Through Graph-Based Neural Networks, file e384c431-9336-d4b2-e053-9f05fe0a1d67
|
37
|
|
Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates, file e384c431-a3e5-d4b2-e053-9f05fe0a1d67
|
36
|
|
Enhancing the friendliness of data analytics tasks: an automated methodology, file e384c434-1106-d4b2-e053-9f05fe0a1d67
|
30
|
|
Enabling predictive analytics for smart manufacturing through an IIoT platform, file e384c433-a620-d4b2-e053-9f05fe0a1d67
|
29
|
|
Twitter data laid almost bare: An insightful exploratory analyser, file e384c433-93d4-d4b2-e053-9f05fe0a1d67
|
27
|
|
K-MDTSC: K-Multi-Dimensional Time-Series Clustering Algorithm, file e384c433-515e-d4b2-e053-9f05fe0a1d67
|
25
|
|
Exploiting scalable machine-learning distributed frameworks to forecast power consumption of buildings, file e384c431-9d46-d4b2-e053-9f05fe0a1d67
|
23
|
|
Simplifying Text Mining Activities: Scalable and Self-Tuning Methodology for Topic Detection and Characterization, file e384c434-61f1-d4b2-e053-9f05fe0a1d67
|
23
|
|
L’analyse du discours et l’intelligence artificielle pour réaliser une écriture inclusive : le projet EMIMIC, file e384c434-ea63-d4b2-e053-9f05fe0a1d67
|
23
|
|
Big data analytics for smart cities, file e384c434-168c-d4b2-e053-9f05fe0a1d67
|
22
|
|
METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models, file e384c430-2a55-d4b2-e053-9f05fe0a1d67
|
21
|
|
Exploiting scalable machine-learning distributed frameworks to forecast power consumption of buildings, file e384c431-abb8-d4b2-e053-9f05fe0a1d67
|
21
|
|
A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data, file e384c433-128d-d4b2-e053-9f05fe0a1d67
|
21
|
|
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery, file 8cd55256-8bf6-4199-8a56-2b82f0bf8569
|
19
|
|
Simplifying Text Mining Activities: Scalable and Self-Tuning Methodology for Topic Detection and Characterization, file e384c434-a8da-d4b2-e053-9f05fe0a1d67
|
19
|
|
Discovering users with similar internet access performance through cluster analysis, file e384c433-7417-d4b2-e053-9f05fe0a1d67
|
16
|
|
Exploring waste-collection fleet data: challenges in a real-world use case from multiple data providers, file e384c434-7e4a-d4b2-e053-9f05fe0a1d67
|
13
|
|
Physical and mental health of university staff during the Covid-19 pandemic, file 09059cd4-8fc3-4738-b13b-aeb70c193729
|
12
|
|
Predicting job execution time on a high-performance computing cluster using a hierarchical data-driven methodology, file e384c434-86a9-d4b2-e053-9f05fe0a1d67
|
12
|
|
Cinematographic Shot Classification with Deep Ensemble Learning, file e384c434-e90b-d4b2-e053-9f05fe0a1d67
|
12
|
|
Advances in Databases and Information Systems - 26th European Conference, ADBIS 2022, Turin, Italy, September 5-8, 2022, Proceedings, file 5555d236-935e-44f8-b850-aec9f32409d2
|
10
|
|
Characterizing unpredictable patterns in Wireless Sensor Network data, file e384c431-80bb-d4b2-e053-9f05fe0a1d67
|
10
|
|
Predictive Maintenance in the Production of Steel Bars: A Data-Driven Approach, file e384c433-b156-d4b2-e053-9f05fe0a1d67
|
10
|
|
A hybrid cloud-to-edge predictive maintenance platform, file e384c433-e15a-d4b2-e053-9f05fe0a1d67
|
8
|
|
New Trends in Database and Information Systems - ADBIS 2022 Short Papers, Doctoral Consortium and Workshops: DOING, K-GALS, MADEISD, MegaData, SWODCH, Turin, Italy, September 5-8, 2022, Proceedings, file 3bc279c7-e0f9-46d2-afd8-550088a0f0aa
|
7
|
|
Explaining deep convolutional models by measuring the influence of interpretable features in image classification, file cc9ea5b1-ee6c-408e-a9fb-d519d701da53
|
7
|
|
SaFe-NeC: A scalable and flexible system for network data characterization, file e384c42f-1b84-d4b2-e053-9f05fe0a1d67
|
7
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-2c56-d4b2-e053-9f05fe0a1d67
|
7
|
|
Real-time Individuation of Global Unsafe Anomalies and Alarm Activation, file e384c42f-3aaf-d4b2-e053-9f05fe0a1d67
|
7
|
|
Frequent Itemsets Mining for Big Data: A Comparative Analysis, file e384c431-7923-d4b2-e053-9f05fe0a1d67
|
7
|
|
Elaborare i dati raccolti negli ambienti di produzione, creare valore e strutturare conoscenza: metodi, sfide e opportunità, file e384c431-c799-d4b2-e053-9f05fe0a1d67
|
7
|
|
DSLE: A Smart Platform for Designing Data Science Competitions, file e384c432-3460-d4b2-e053-9f05fe0a1d67
|
7
|
|
Data-Driven Predictive Maintenance: A Methodology Primer, file e384c433-c931-d4b2-e053-9f05fe0a1d67
|
7
|
|
Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case, file e384c434-5812-d4b2-e053-9f05fe0a1d67
|
7
|
|
Predictive Maintenance in the Production of Steel Bars: A Data-Driven Approach, file e384c434-910c-d4b2-e053-9f05fe0a1d67
|
7
|
|
Trusting deep learning natural-language models via local and global explanations, file e384c434-9a7b-d4b2-e053-9f05fe0a1d67
|
7
|
|
A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data, file e384c42f-d55b-d4b2-e053-9f05fe0a1d67
|
6
|
|
Discovering electricity consumption over time for residential consumers through cluster analysis, file e384c430-316f-d4b2-e053-9f05fe0a1d67
|
6
|
|
Twitter data laid almost bare: An insightful exploratory analyser, file e384c431-7e84-d4b2-e053-9f05fe0a1d67
|
6
|
|
IoT Platforms and Technologies Driving Spatial Planning and Analytics, file e384c431-8c7d-d4b2-e053-9f05fe0a1d67
|
6
|
|
Discovering profitable stocks for intraday trading, file e384c431-bd3a-d4b2-e053-9f05fe0a1d67
|
6
|
|
Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case, file e384c433-5ac1-d4b2-e053-9f05fe0a1d67
|
6
|
|
Estimating Remaining Useful Life: A Data-Driven Methodology for the White Goods Industry, file e384c433-db64-d4b2-e053-9f05fe0a1d67
|
6
|
|
Trusting deep learning natural-language models via local and global explanations, file 377303a3-27ff-4cb9-82be-2c54f1ebe2f3
|
5
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-0dbd-d4b2-e053-9f05fe0a1d67
|
5
|
|
Discovering users with similar internet access performance through cluster analysis, file e384c42f-23a6-d4b2-e053-9f05fe0a1d67
|
5
|
|
Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case, file e384c433-664a-d4b2-e053-9f05fe0a1d67
|
5
|
|
DS4ALL: All you need for democratizing data exploration and analysis, file e384c434-9109-d4b2-e053-9f05fe0a1d67
|
5
|
| Totale |
16.032 |
Archivio Istituzionale della Ricerca