Daniel P. Miranker

2.9k citations
117 papers · 1.9k · h-index 25

Impact in

Papers in

Daniel P. Miranker

111 papers receiving 1.8k citations

Peers

Daniel P. Miranker
Comparison fields: 5 of 84
  • Artificial Intelligence 1.1k
  • Computer Networks and Communications 519
  • Hardware and Architecture 176
  • Signal Processing 89
  • Management Science and Operations Research 141
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Countries citing papers authored by Daniel P. Miranker

Since Specialization
Citations

This map shows the geographic impact of Daniel P. Miranker'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 Daniel P. Miranker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel P. Miranker more than expected).

Fields of papers citing papers by Daniel P. Miranker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel P. Miranker. 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 Daniel P. Miranker. The network helps show where Daniel P. Miranker may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel P. Miranker, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel P. Miranker Line = papers co-authored together Daniel P. Miranker links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 117 papers — load more, or switch the sort, to bring in the rest.

#Work
1
TREAT: a better match algorithm for AI production systems
1987181
2 2013107
3 2012106
4
A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem
199683
5 201375
6 199161
7
On the performance of lazy matching in production systems
199056
8 200856
9 200954
10 201154
11 201350
12 198446
13 201444
14 200233
15 201133
16
Effects of Database Size on Rule System Performance: Five Case Studies
199132
17 200432
18 200629
19 200929
20
TREAT: A Better Match Algorithm for AI Production System Matching.
198728

About Daniel P. Miranker

Daniel P. Miranker is a scholar working on Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Signal Processing and Management Science and Operations Research, having authored 117 papers that have together received 1.9k indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (32 papers), Semantic Web and Ontologies (28 papers), AI-based Problem Solving and Planning (21 papers), Parallel Computing and Optimization Techniques (20 papers), Data Management and Algorithms (13 papers), Logic, programming, and type systems (13 papers), Data Quality and Management (12 papers) and Algorithms and Data Compression (12 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Computer Networks and Communications (519 citations), Hardware and Architecture (176 citations), Signal Processing (89 citations) and Management Science and Operations Research (141 citations). Daniel P. Miranker has collaborated with scholars based in United States, China and Chile. Frequent co-authors include Juan Sequeda, Roberto J. Bayardo, Mayank Kejriwal, Salvatore J. Stolfo, Marcelo Arenas, Weijia Xu, Rui Mao, James C. Browne, Vasilis Samoladas and Smriti R. Ramakrishnan. Their work appears in journals such as Bioinformatics, Journal of Web Semantics, IEEE Transactions on Knowledge and Data Engineering, BMC Bioinformatics and Journal of Parallel and Distributed Computing.

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.

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