Abstract Model for Multi-model Data
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 12683 LNCS, Page: 647-651
2021
- 1Citations
- 2Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In recent years, many so-called multi-model database management systems have emerged, mainly as extensions of the existing single-model systems, regardless they used to be relational or NoSQL. These new database systems make new demands on their users. From the point of view of the conceptual and logical representation, the so far widely used approaches, especially ER and UML, prove not to be sufficient enough in many aspects due to the specific properties of multi-model data. In addition, it is also difficult to query data that is represented in various and often overlapping data models at the logical level.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85104802862&origin=inward; http://dx.doi.org/10.1007/978-3-030-73200-4_53; http://link.springer.com/10.1007/978-3-030-73200-4_53; http://link.springer.com/content/pdf/10.1007/978-3-030-73200-4_53; https://dx.doi.org/10.1007/978-3-030-73200-4_53; https://link.springer.com/chapter/10.1007/978-3-030-73200-4_53
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know