Semantic Data-Modeling and Long-Term Interpretability of Cultural Heritage Data—Three Case StudiesResearch institutions and museums are increasingly initiating projects where large amounts of metadata are coll
Data modelingtechniques: examples and applications There are a few core data modeling techniques or schemas: relational, entity-relationship, hierarchical, network, dimensional, object-oriented database, and object-relational models. They provide frameworks for organizing, storing, and managing data, each...
Data Literacy Examples and Case Studies Essential Data Literacy Resources Conclusion FAQs In an age where 2.5 quintillion bytes of data are produced each day, data literacy has become a critical skill. The rapid rise of data has not only transformed how we do business, but it also demands a ...
We have witnessed database model development from hierarchical and network database models to the relational database model [65]. However, in many non-traditional and emerging applications, modeling and manipulation of complex objects and semantic relationships are essential and the limitations of ...
Database Modeling and Design, Fifth Edition, focuses on techniques for database design in relational database systems. This extensively revised fifth edition features clear explanations, lots of terrific examples and an illustrative case, and practical advice, with design rules that are applicable to ...
Depending upon the modeling use cases, creating high-quality synthetic data sets can help address the availability. Additionally, there might be some advantage in the future as more data sets previously only available to individual organizations are now opened up publicly, even if ...
Different data-driven optimization problems may have completely different data resources and data collection methods. Roughly speaking, data can be classified into two large types: 1) direct and 2) indirect data, consequently resulting in two different types of surrogate modeling and management strategie...
In: 5th European Congress on Intelligent Techniques and Soft Computing, 1997 Aachen, Verlagshaus Mainz, Aachen, pp 1602–1606 Google Scholar Lin TY (2005) Granular computing: Examples, intuitions, and modeling. In: Proceedings of IEEE 2005 conference on granular computing GrC05, Beijing, China....
modeling22,26is the cross-entropy loss27(CEL). It is efficient and robust, and has the desirable property of directing the DNN to learn true conditional probabilities28. Yet, the CEL assumes classes to be mutually exclusive, and thus exactly one species to be present per observation (although...
Understand how to work with cloud platforms (AWS, Google Cloud) and big data frameworks like Apache Spark. 4. Month 5–6: Build real-world projects and a portfolio Apply what you've learned to real datasets. Create dashboards, predictive models, or case studies. Use tools like DataLab to...