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1. |
Record Nr. |
UNINA990008553580403321 |
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Autore |
De Blasiis, Giuseppe <1832-1914> |
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Titolo |
Processo contro Cesare Carrafa inquisito di fellonia / Giuseppe De Blasiis |
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Pubbl/distr/stampa |
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[Rist. anast] |
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Descrizione fisica |
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Lingua di pubblicazione |
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Livello bibliografico |
Monografia |
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Note generali |
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Paginato anche 757-851 |
Estratto da Archivio storico per le provincie napoletane |
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2. |
Record Nr. |
UNINA9910337565703321 |
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Autore |
Wang Wei |
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Titolo |
Integrating Business Process Models and Rules : Empirical Evidence and Decision Framework / / by Wei Wang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XII, 127 p. 63 illus., 8 illus. in color.) |
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Collana |
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Lecture Notes in Business Information Processing, , 1865-1356 ; ; 343 |
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Disciplina |
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Soggetti |
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Software engineering |
Application software |
Information technology - Management |
Software Engineering |
Computer and Information Systems Applications |
Business Process Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1 INTRODUCTION -- 1.1 BACKGROUND -- 1.2 AIM AND OBJECTIVES OF THE RESEARCH -- 1.3 THESIS STRUCTURE -- 2 LITERATURE REVIEW -- 2.1 OVERVIEW -- 2.2 BUSINESS PROCESS MODELS -- 2.3 BUSINESS PROCESS UNDERSTANDING -- 2.4 BUSINESS RULES -- 2.5 BUSINESS PROCESS MODEL AND BUSINESS RULE INTEGRATION -- 2.6 INTEGRATION APPROACHES -- 2.7 CHAPTER SUMMARY -- 3 METHODOLOGY -- 3.1 RESEARCH METHOD OF STUDY 1 – EXPERIMENT -- 3.2 RESEARCH METHOD OF STUDY 2 – SYSTEMATIC LITERATURE REVIEW AND SURVEY -- 3.3 RESEARCH METHOD OF STUDY 3 – DESIGN SCIENCE -- 3.4 CHAPTER SUMMARY -- 4 RULE INTEGRATION AND MODEL UNDERSTANDING: A THEORETICAL UNDERPINNING -- 4.1 OVERVIEW -- 4.2 RELATED THEORIES -- 4.3 PROCESS MODELS AND RULES UNDERSTANDING -- 4.4 CHAPTER SUMMARY -- 5 THE EFFECT OF RULE LINKING ON BUSINESS PROCESS MODEL UNDERSTANDING -- 5.1 OVERVIEW -- 5.2 HYPOTHESES DEVELOPMENT -- 5.3 APPROACH -- 5.4 RESULT ANALYSIS -- 5.5 CHAPTER SUMMARY -- 6 IDENTIFICATION |
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OF FACTORS AFFECTING BUSINESS PROCESS AND BUSINESS RULE INTEGRATION -- 6.1 OVERVIEW -- 6.2 APPROACH -- 6.3 BUSINESS RULE MODELLING FACTORS -- 6.4 EMPIRICAL VALIDATION OF FACTORS -- 6.5 BUSINESS RULE EMBEDDING GUIDELINES -- 6.6 CHAPTER SUMMARY -- 7 A BUSINESS RULE MODELLING DECISION FRAMEWORK -- 7.1 OVERVIEW -- 7.2 PROBLEM IDENTIFICATION AND DEFINITION OF OBJECTIVES -- 7.3 THE DESIGN AND DEVELOPMENT OF THE DECISION FRAMEWORK -- 7.4 THE DECISION FRAMEWORK DEMONSTRATION -- 7.5 THE DECISION FRAMEWORK EVALUATION -- 7.6 CHAPTER SUMMARY -- 8 CONCLUSION -- 8.1 OVERVIEW -- 8.2 SUMMARY OF CONTRIBUTIONS -- 8.3 RESEARCH LIMITATIONS AND FUTURE WORK -- REFERENCES -- APPENDIX A ONLINE SURVEY -- APPENDIX B EXPERIMENT MATERIALS. |
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Sommario/riassunto |
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This book combines multiple research methods, experiment, survey, and design science, as well as traditional measurements and neurophysiological techniques that can capture a variety of cognitive behaviors in human information processing, providing more solid and comprehended research findings. While the focus of the book is the modelling of process models and rules, the methods and techniques used in this book can also be adopted and applied to broader conceptual modelling research incorporating a variety of notations (e.g. UML, ER diagrams) or ontologies. It is a revised version of the PhD dissertation written by the author at the School of Information Technology and Electrical Engineering of the University of Queensland, Australia. In 2018, the PhD dissertation won the “CAiSE PhD Award,” granted to outstanding PhD theses in the field of information systems engineering. |
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3. |
Record Nr. |
UNINA9910373928603321 |
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Titolo |
Advanced Analytics and Learning on Temporal Data : 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers / / edited by Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Alexis Bondu, Thomas Guyet, Romain Tavenard |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (X, 229 p. 109 illus., 90 illus. in color.) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 11986 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Computer networks |
Computer engineering |
Application software |
Image processing - Digital techniques |
Computer vision |
Artificial Intelligence |
Computer Communication Networks |
Computer Engineering and Networks |
Computer and Information Systems Applications |
Computer Imaging, Vision, Pattern Recognition and Graphics |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Robust Functional Regression for Outlier Detection -- Transform Learning Based Function Approximation for Regression and Forecasting -- Proactive Fiber Break Detection based on Quaternion Time Series and Automatic Variable Selection from Relational Data -- A fully automated periodicity detection in time series -- Conditional Forecasting of Water Level Time Series with RNNs -- Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories -- Localized Random Shapelets -- Feature-Based Gait Pattern Classification for a Robotic Walking Frame -- How to detect novelty in |
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textual data streams? A comparative study of existing methods -- Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model -- Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets -- Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems -- Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets using Deep Learning -- Learning Stochastic Dynamical Systems via Bridge Sampling -- Quantifying Quality of Actions Using Wearable Sensor -- An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data. . |
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