Nicholas Kofi Akortia Hagan Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Nicholas Kofi Akortia Hagan's research focuses on developing scalable and efficient methods for data processing and analysis. His work includes the design of unsupervised systems for data clustering, cleaning, and entity resolution. Hagan has developed systems like the Data Washing Machine (DWM) and its Spark-based implementation, SparkDWM, aimed at improving the quality and usability of large datasets. He has also investigated graph-based approaches for entity resolution, utilizing techniques such as modularity optimization and locality-sensitive hashing to enhance accuracy and scalability. His publications demonstrate a commitment to advancing data management techniques, particularly in areas requiring unsupervised learning and distributed computing frameworks.
Metrics
- h-index: 2
- Publications: 4
- Citations: 9
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