MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Pairwise document similarity in large collections with MapReduce
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Frameworks for entity matching: A comparison
Data & Knowledge Engineering
Efficient parallel set-similarity joins using MapReduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
MapDupReducer: detecting near duplicates over massive datasets
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Evaluation of entity resolution approaches on real-world match problems
Proceedings of the VLDB Endowment
Multi-pass sorted neighborhood blocking with MapReduce
Computer Science - Research and Development
LINDA: distributed web-of-data-scale entity matching
Proceedings of the 21st ACM international conference on Information and knowledge management
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The effectiveness and scalability of MapReduce-based implementations of complex data-intensive tasks depend on an even redistribution of data between map and reduce tasks. In the presence of skewed data, sophisticated redistribution approaches thus become necessary to achieve load balancing among all reduce tasks to be executed in parallel. For the complex problem of entity resolution with blocking, we propose BlockSplit, a load balancing approach that supports blocking techniques to reduce the search space of entity resolution. The evaluation on a real cloud infrastructure shows the value and effectiveness of the proposed approach.