Big data: an art of decision making
"Manipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook - already well known in the social sciences and the humanities - to convey intelligible results from data analysis algorithms and create new knowledge....
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London, UK
ISTE Ltd.
2020
London ; Hoboken, NJ, USA Wiley 2020 |
| Schriftenreihe: | Information systems, web and pervasive computing series. Intellectual technologies set
volume 7 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Manipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook - already well known in the social sciences and the humanities - to convey intelligible results from data analysis algorithms and create new knowledge. Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions."--Page 4 of cover, |
| Beschreibung: | xiii, 259 Seiten Illustrationen, Diagramme (schwarz-weiß) 24 cm |
| ISBN: | 9781786305558 |
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Datensatz im Suchindex
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| adam_text | Contents Introduction.................................................................................................... vii Chapter 1. From Trace to Web Data: An Ontology of the Digital Footprint.......................................................................... 1 1.1. The epistemology of the cultural sciences............................................. 1.2. The footprint in evidential sciences....................................................... 1.3. The log or activity history.................................................................... 1.4. The digital footprint as a web log......................................................... 1.5. The intentionality of digital footprints.................................................. 1.6. Data as theoretically-loaded footprints.................................................. 7 9 14 18 20 24 Chapter 2. Toward an Epistemic Continuity Anchored in the Cultural Sciences.............................................................................. 29 2.1. Digital technology in the cultural sciences.......................................... 2.2. Field and corpus: two modes of access to reality.................................. 2.3. Virtual methods, a reconstruction of access to the field........................ 2.4. The challenges of the technical revolution of the text........................... 2.5. From the web as an object to the web as a corpus................................ 2.6. Conclusion............................................................................................ 31 34 38 48 59 69 Chapter 3.
The Status of Computation in Data Sciences................. 71 3.1. Making data computable....................................................................... 3.2. The field of computability.................................................................... 3.3. Computational thinking......................................................................... 3.4. Computation in the natural sciences..................................................... 3.5. From exploratory analysis to data mining............................................. 3.6. The institutional and theoretical melting pot of data science................ 73 77 81 87 98 107
vi Big Data 3.7. The contribution of artificial intelligence............................................ 3.8. Conclusion............................................................................................ 115 122 Chapter 4. A Practical Big Data Use Case............................................. 125 4.1. Presentation of the case study............................................................... 4.2. Customer experience and coding of feedback....................................... 4.3. From the representative approach to the “big data” project.................. 4.4. Data preparation.................................................................................... 4.5. Design of the coding plan.................................................................... 4.6. The constitution of linguistic resources............................................... 4.7. Constituting the coding plan.................................................................. 4.8. Visibility of the lánguage activity.......................................................... 4.9. Storytelling and interpretation of the data............................................. 4.10. Conclusion.......................................................................................... 126 131 134 137 140 143 148 153 155 161 Chapter 5. From Narratives to Systems: How to Shape and Share Data Analysis............................................................... 165 5.1. Two epistemic configurations............................................................... 5.2. The genesis of
systems.......................................................................... 5.3. Conclusion............................................................................................ 166 172 183 Chapter 6. The Art of Data Visualization............................................... 187 6.1. 6.2. 6.3. 6.4. 6.5. Graphic semiology............................................................................... Data cartography.................................................................................... Representation as evidence.................................................................. The visual language of design in system configuration........................ Materialization and interpretation of recommendations........................ 187 198 203 207 214 Chapter 7. Knowledge and Decision....................................................... 219 7.1. Big data, a pragmatic epistemology? .................................................. 7.2. Toward gradual validity of knowledge.................................................. 7.3. Deciding, knowing and measuring....................................................... 220 227 233 Conclusion....................................................................................................... 239 References....................................................................................................... 243 Index 257
|
| adam_txt |
Contents Introduction. vii Chapter 1. From Trace to Web Data: An Ontology of the Digital Footprint. 1 1.1. The epistemology of the cultural sciences. 1.2. The footprint in evidential sciences. 1.3. The log or activity history. 1.4. The digital footprint as a web log. 1.5. The intentionality of digital footprints. 1.6. Data as theoretically-loaded footprints. 7 9 14 18 20 24 Chapter 2. Toward an Epistemic Continuity Anchored in the Cultural Sciences. 29 2.1. Digital technology in the cultural sciences. 2.2. Field and corpus: two modes of access to reality. 2.3. Virtual methods, a reconstruction of access to the field. 2.4. The challenges of the technical revolution of the text. 2.5. From the web as an object to the web as a corpus. 2.6. Conclusion. 31 34 38 48 59 69 Chapter 3.
The Status of Computation in Data Sciences. 71 3.1. Making data computable. 3.2. The field of computability. 3.3. Computational thinking. 3.4. Computation in the natural sciences. 3.5. From exploratory analysis to data mining. 3.6. The institutional and theoretical melting pot of data science. 73 77 81 87 98 107
vi Big Data 3.7. The contribution of artificial intelligence. 3.8. Conclusion. 115 122 Chapter 4. A Practical Big Data Use Case. 125 4.1. Presentation of the case study. 4.2. Customer experience and coding of feedback. 4.3. From the representative approach to the “big data” project. 4.4. Data preparation. 4.5. Design of the coding plan. 4.6. The constitution of linguistic resources. 4.7. Constituting the coding plan. 4.8. Visibility of the lánguage activity. 4.9. Storytelling and interpretation of the data. 4.10. Conclusion. 126 131 134 137 140 143 148 153 155 161 Chapter 5. From Narratives to Systems: How to Shape and Share Data Analysis. 165 5.1. Two epistemic configurations. 5.2. The genesis of
systems. 5.3. Conclusion. 166 172 183 Chapter 6. The Art of Data Visualization. 187 6.1. 6.2. 6.3. 6.4. 6.5. Graphic semiology. Data cartography. Representation as evidence. The visual language of design in system configuration. Materialization and interpretation of recommendations. 187 198 203 207 214 Chapter 7. Knowledge and Decision. 219 7.1. Big data, a pragmatic epistemology? . 7.2. Toward gradual validity of knowledge. 7.3. Deciding, knowing and measuring. 220 227 233 Conclusion. 239 References. 243 Index 257 |
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| spelling | Schmitt, Églantine Verfasser (DE-588)1241252912 aut Big data an art of decision making Églantine Schmitt London, UK ISTE Ltd. 2020 London ; Hoboken, NJ, USA Wiley 2020 xiii, 259 Seiten Illustrationen, Diagramme (schwarz-weiß) 24 cm txt rdacontent n rdamedia nc rdacarrier Information systems, web and pervasive computing series. Intellectual technologies set volume 7 From trace to web data : an ontology of the digital footprint -- Toward an epistemic continuity anchored in the cultural sciences -- The status of computation in data sciences -- A practical big data use case -- From narratives to systems : how to shape and share data analysis -- The art of data visualization -- Knowledge and decision "Manipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook - already well known in the social sciences and the humanities - to convey intelligible results from data analysis algorithms and create new knowledge. Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions."--Page 4 of cover, Big Data (DE-588)4802620-7 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Erkenntnistheorie (DE-588)4070914-0 gnd rswk-swf Big data Data mining Big Data (DE-588)4802620-7 s Data Mining (DE-588)4428654-5 s DE-604 Data Science (DE-588)1140936166 s Erkenntnistheorie (DE-588)4070914-0 s Erscheint auch als Online-Ausgabe 978-1-119-77700-7 (DE-604)BV047442061 Information systems, web and pervasive computing series. Intellectual technologies set volume 7 (DE-604)BV045123022 7 Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032836358&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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