Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
- Authors
- Paul ZikopoulosChris Eaton
In The Last Decade
doi.org/w78685414 →Countries where authors are citing Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
This map shows the geographic impact of Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. 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 Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data more than expected).
Fields of papers citing Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
This network shows the impact of Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data.
About Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
This paper, published in 2011, received 873 indexed citations . Written by Paul Zikopoulos and Chris Eaton covering the research area of Management Information Systems and Information Systems and Management. It is primarily cited by scholars working on Information Systems (329 citations), Management Information Systems (273 citations) and Computer Networks and Communications (254 citations).
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/w78685414.