AIMS: An Immersidata Management System

We introduce a system to address the challenges involved in managing the multidimensional sensor data streams generated within immersive environments. We call this data type, immersidata, which is de ned as the data acquired from a user's interactions with an immersive environment. Management of immersidata is challenging because they are: 1) multidimensional, 2) spatio-temporal, 3) continuous data streams (CDS), 4) large in size and bandwidth requirements, and 5) noisy. By focusing on two speci c applications, Attention De cit Hyperactivity Disorder (ADHD) diagnosis and American Sign Language (ASL) recognition, we propose to study the challenges of two main modes of operations on immersidata: o -line and online query and analysis. In addition, we propose complementary approaches for e cient acquisition and storage of immersidata. The core promising idea behind our proposed approaches is a `database friendly' utilization of linear algebraic transformations on both data sets and queries to e ciently abstract, aggregate, classify and/or approximate multidimensional data streams. This research has been funded in part by NSF grants EEC9529152 (IMSC ERC) and IIS-0082826, NIH-NLM grant nr. R01-LM07061, NASA/JPL contract nr. 961518, DARPA and USAF under agreement nr. F30602-99-1-0524, and unrestricted cash gifts from Microsoft, NCR, and Okawa Foundation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 2003 CIDR Conference