ADAM — A System for Jointly Providing IR and Database Queries in Large-Scale Multimedia Retrieval
The tremendous increase of multimedia data in recent years has heightened the need for systems that not only allow to search with keywords, but that also support content-based retrieval in order to effectively and efficiently query large collections. In this demo, we present ADAM, a system that is able to store and retrieve multimedia objects by seamlessly combining aspects from databases and information retrieval. ADAM is able to work with both structured and unstructured data and to jointly provide Boolean retrieval and similarity search. To efficiently handle large volumes of data it makes use of a signature-based indexing and the distribution of the collection to multiple shards that are queried in a MapReduce style. We present ADAM in the setting of a sketch-based image retrieval application using the ImageNet collection containing 14 million images.