Nicholas Kofi Akortia Hagan
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Researcher
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Biography and Research Information
OverviewAI-generated summary
Nicholas Kofi Akortia Hagan's research focuses on developing and scaling data processing techniques, particularly for unsupervised learning and entity resolution. His work has resulted in publications detailing methods for data cleaning and washing, including the development of SparkDWM, a scalable design for a data washing machine utilizing Apache Spark, and ModER, a graph-based unsupervised entity resolution system. Hagan also investigates optimal starting parameters for unsupervised data clustering and cleaning within data washing machine frameworks.
His recent publications include "SparkDWM: a scalable design of a Data Washing Machine using Apache Spark" (2024) and "A scalable MapReduce-based design of an unsupervised entity resolution system" (2024). Hagan has collaborated with researchers such as John R. Talburt and Kris E. Anderson at the University of Arkansas at Little Rock on multiple shared publications. His scholarly output is characterized by an h-index of 2 across 4 total publications.
Metrics
- h-index: 2
- Publications: 4
- Citations: 10
Selected Publications
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SparkDWM: a scalable design of a Data Washing Machine using Apache Spark (2024)
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A scalable MapReduce-based design of an unsupervised entity resolution system (2024)
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Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine (2023)
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ModER: Graph-based Unsupervised Entity Resolution using Composite Modularity Optimization and Locality Sensitive Hashing (2022)
Collaboration Network
Top Collaborators
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine
- ModER: Graph-based Unsupervised Entity Resolution using Composite Modularity Optimization and Locality Sensitive Hashing
- A scalable MapReduce-based design of an unsupervised entity resolution system
- SparkDWM: a scalable design of a Data Washing Machine using Apache Spark
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine
- A scalable MapReduce-based design of an unsupervised entity resolution system
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine
- A scalable MapReduce-based design of an unsupervised entity resolution system
- ModER: Graph-based Unsupervised Entity Resolution using Composite Modularity Optimization and Locality Sensitive Hashing
- ModER: Graph-based Unsupervised Entity Resolution using Composite Modularity Optimization and Locality Sensitive Hashing
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine