Big Data: Principles and best practices of scalable realtime data systems
- Authors
- Jim Warren
- Journal
- CERN Document Server (European Organization for Nuclear Research)
In The Last Decade
doi.org/w84691842 →Countries where authors are citing Big Data: Principles and best practices of scalable realtime data systems
This map shows the geographic impact of Big Data: Principles and best practices of scalable realtime data systems. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Big Data: Principles and best practices of scalable realtime data systems with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Big Data: Principles and best practices of scalable realtime data systems more than expected).
Fields of papers citing Big Data: Principles and best practices of scalable realtime data systems
This network shows the impact of Big Data: Principles and best practices of scalable realtime data systems. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Big Data: Principles and best practices of scalable realtime data systems.
About Big Data: Principles and best practices of scalable realtime data systems
This paper, published in 2015, received 553 indexed citations . Written by Jim Warren covering the research area of Management Information Systems and Information Systems and Management. It is primarily cited by scholars working on Computer Networks and Communications (258 citations), Information Systems (203 citations) and Artificial Intelligence (135 citations). Published in CERN Document Server (European Organization for Nuclear Research).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w84691842.