Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs
โ Scribed by Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness (editors)
- Publisher
- Springer Nature
- Year
- 2021
- Tongue
- English
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language Key Features โข Transform relational data models into graph data model while learning key applications along the way โข Discover common challenges in graph modeling and analy
<p><span>Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language</span></p><p><span>Purchase of the print or Kindle book includes a free PDF eBook</span></p><h4><span>Key Features</span></h4><ul><li><span><span>Transform relational d
<p><span>Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language</span></p><p><span>Purchase of the print or Kindle book includes a free PDF eBook</span></p><h4><span>Key Features</span></h4><ul><li><span><span>Transform relational d
The breadth of problems that can be solved with data science is astonishing, and this book provides the required tools and skills fot a broad audience. The reader takes a journey into the forms, uses, and abuses of data and models, and learns how to critically examine each step. Python coding and da
<p>Complex databases can be understood well with visual representation. A graph is a very intuitive and rational structure to visually represent such databases. Graph Data Model (GDM) proposed by the author formalizes data representation and operations on the data in terms of the graph concept. The