Model-Based Multi-Modal Information Retrieval from Large Archives

In this paper, we describe a new paradigm for information retrieval in which the retrieval target is based on a model. Three types of models – linear, finite state, and knowledge models are discussed. These information retrieval scenarios often arise from applications such as environmental epidemiology, oil/gas production and exploration, and precision agriculture/forestry. Traditional model-based data and information processing usually requires the processing of each and every data points. The proposed new framework, in contrast, will process the data progressively using a set of progressive models and utilize indexing techniques specialized for the model to facilitate retrieval, thus achieving a dramatic speedup.

• Type: Conference
• Year: 2000
• Where: ICDCSW
• Authors: Chung-Sheng Li, Yuan-Chi Chang, Lawrence D. Bergman, John R. Smith
• Keywords: Computer Networks, ICDCSW 2000, Information Retrieval, Information Retrieval Scenarios, Retrieval Target