Scalable high-dimensional indexing and searching with Hadoop
This addresses the 'big data' challenge for researchers and practitioners dealing with explosively growing multimedia collections, though it is incremental as it adapts existing methods to distributed infrastructures.
The paper tackled the problem of scaling high-dimensional search-by-similarity for very large multimedia collections by proposing a method using Hadoop on grid environments, achieving a stable throughput of around 210ms per image when searching a 100M image collection.
While high-dimensional search-by-similarity techniques reached their maturity and in overall provide good performance, most of them are unable to cope with very large multimedia collections. The 'big data' challenge however has to be addressed as multimedia collections have been explosively growing and will grow even faster than ever within the next few years. Luckily, computational processing power has become more available to researchers due to easier access to distributed grid infrastructures. In this paper, we show how high-dimensional indexing and searching methods can be used on scientific grid environments and present a scalable workflow for indexing and searching over 30 billion SIFT descriptors using a cluster running Hadoop. Besides its scalability, the proposed scheme not only provides good search quality, but also achieves a stable throughput of around 210ms per image when searching a 100M image collection. Our findings could help other researchers and practitioners to cope with huge multimedia collections.