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1. |
Record Nr. |
UNINA9910812365703321 |
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Autore |
Broadhead Edwin K. |
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Titolo |
Teaching with authority : miracles and Christology in the Gospel of Mark / / Edwin K. Broadhead |
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Pubbl/distr/stampa |
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Sheffield, England : , : JSOT Press, , [1992] |
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©1992 |
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ISBN |
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1-283-19402-3 |
9786613194022 |
0-567-19342-X |
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Descrizione fisica |
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1 online resource (241 p.) |
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Collana |
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Journal for the study of the New Testament. Supplement series ; ; 74 |
Library of New Testament studies |
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Disciplina |
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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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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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CONTENTS; Preface; Abbreviations; Chapter 1 INTRODUCTION; Chapter 2 A PROPOSAL FOR NARRATIVE ANALYSIS; Chapter 3 MARK 1.1-3.7a; Chapter 4 MARK 3.7-6.6; Chapter 5 MARK 6.6b-8.27a; Chapter 6 MARK 8.27-10.52; Chapter 7 MARK 11.1-13.37; Chapter 8 THE ABSENCE OF MIRACLE STORIES IN MARK 14.1-16.8; Chapter 9 CONCLUSION; Bibliography; Index of References; Index of Authors |
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Sommario/riassunto |
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The foundational inquiries into the relationship of miracles and Christology by Wrede, Dibelius, Bultmann and Marxsen guided a productive half-century of critical research. Their work raised crucial issues concerning the nature of the Gospels and the proper methods of interpretation that have in many ways charted the direction for New Testament studies. A new principle is now to be added to their criteria, however, that strategies of interpretation must be consciously shaped to highlight the features of narrative and its christological focus. The author then employs a consistent narrative stra |
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2. |
Record Nr. |
UNINA9910410052203321 |
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Autore |
Luengo Julián |
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Titolo |
Big Data Preprocessing : Enabling Smart Data / / by Julián Luengo, Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, Francisco Herrera |
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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 (XIII, 186 p. 57 illus., 54 illus. in color.) |
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Disciplina |
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Soggetti |
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Big data |
Machine learning |
Computer networks |
Big Data |
Machine Learning |
Computer Communication Networks |
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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 bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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1. Introduction -- 2. Big Data: Technologies and Tools -- 3. Smart Data -- 4. Dimensionality Reduction for Big Data -- 5. Data Reduction for Big Data -- 6. Imperfect Big Data -- 7. Big Data Discretization -- 8. Imbalanced Data Preprocessing for Big Data -- 9. Big Data Software -- 10. Final Thoughts: From Big Data to Smart Data.-. |
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Sommario/riassunto |
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This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of |
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IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book. |
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