Ming-Ling Lo
Impact in
- Signal Processing top 1%
- Data Management and Algorithms
- Time Series Analysis and Forecasting
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- Advanced Database Systems and Queries
- Distributed and Parallel Computing Systems
Papers in
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- Data Management and Algorithms 8
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- Advanced Database Systems and Queries 6
- Co-authors
- Chinya V. Ravishankar (9 shared papers)Yuan‐Chi Chang (3 shared papers)John R. Smith (3 shared papers)Lawrence D. Bergman (2 shared papers)Vittorio Castelli (2 shared papers)Chung‐Sheng Li (2 shared papers)Philip S. Yu (4 shared papers)Ming-Syan Chen⋆ (4 shared papers)
- Journals
- ACM SIGMOD Record (4 papers)IEEE Transactions on Knowledge and Data Engineering (2 papers)Lecture notes in computer science (2 papers)IEEE International Conference on Distributed Computing Systems (ICDCS) (1 paper)Proceedings - ACM-SIGMOD International Conference on Management of Data (4 papers)
- Partner nations
- United States
In The Last Decade
Ming-Ling Lo
16 papers receiving 786 citations
Peers
Comparison fields: 5 of 13
- Signal Processing 548
- Computer Networks and Communications 171
- Conservation 2
- Computer Graphics and Computer-Aided Design 2
- Information Systems 8
Countries citing papers authored by Ming-Ling Lo
This map shows the geographic impact of Ming-Ling Lo's research. 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 Ming-Ling Lo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming-Ling Lo more than expected).
Fields of papers citing papers by Ming-Ling Lo
This network shows the impact of papers produced by Ming-Ling Lo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming-Ling Lo. The network helps show where Ming-Ling Lo may publish in the future.
Co-authors
The 13 scholars most cited alongside Ming-Ling Lo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 1996 | 177 | |
| 2 | 2000 | 155 | |
| 3 | 1994 | 138 | |
| 4 | 2000 | 98 | |
| 5 | Using Segmented Right-Deep Trees for the Execution of Pipelined Hash Joins | 1992 | 65 |
| 6 | 1996 | 56 | |
| 7 | 1993 | 32 | |
| 8 | 1994 | 30 | |
| 9 | 1995 | 25 | |
| 10 | 1998 | 15 | |
| 11 | 1995 | 13 | |
| 12 | 2001 | 13 | |
| 13 | 2002 | 6 | |
| 14 | 2000 | 4 | |
| 15 | 1993 | 3 | |
| 16 | 2002 | 1 |
About Ming-Ling Lo
Ming-Ling Lo is a scholar working on Signal Processing, Computer Networks and Communications, Computer Vision and Pattern Recognition, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 16 papers that have together received 831 indexed citations. Recurring topics across this work include Data Management and Algorithms (8 papers), Advanced Database Systems and Queries (6 papers) and Data Visualization and Analytics (1 paper). The work is most often cited by research in Signal Processing (548 citations), Computer Networks and Communications (171 citations), Conservation (2 citations), Computer Graphics and Computer-Aided Design (2 citations) and Information Systems (8 citations). Ming-Ling Lo has collaborated with scholars based in United States. Frequent co-authors include Chinya V. Ravishankar, Yuan‐Chi Chang, John R. Smith, Lawrence D. Bergman, Vittorio Castelli, Chung‐Sheng Li, Philip S. Yu, Ming-Syan Chen⋆, Honesty C. Young and Hong Su. Their work appears in journals such as ACM SIGMOD Record, IEEE Transactions on Knowledge and Data Engineering, Lecture notes in computer science, IEEE International Conference on Distributed Computing Systems (ICDCS) and Proceedings - ACM-SIGMOD International Conference on Management of Data.
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.