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

[1]  Jeffrey Scott Vitter,et al.  Approximate computation of multidimensional aggregates of sparse data using wavelets , 1999, SIGMOD '99.

[2]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[3]  Divesh Srivastava,et al.  On computing correlated aggregates over continual data streams , 2001, SIGMOD '01.

[4]  S. Muthukrishnan,et al.  Optimal and approximate computation of summary statistics for range aggregates , 2001, PODS '01.

[5]  S. Muthukrishnan,et al.  Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries , 2001, VLDB.

[6]  Cyrus Shahabi,et al.  Immersidata Management: Challenges in Management of Data Generated within an Immersive Environment , 1999, Multimedia Information Systems.

[7]  Cyrus Shahabi,et al.  Modeling Data of Immersive Environments , 2002 .

[8]  M. Victor Wickerhauser,et al.  Adapted wavelet analysis from theory to software , 1994 .

[9]  Shin-Chung Shao.  Multivariate and Multidimensional OLAP , 1998, EDBT.

[10]  Christos Faloutsos,et al.  Efficiently supporting ad hoc queries in large datasets of time sequences , 1997, SIGMOD '97.

[11]  Farnoush Banaei Kashani,et al.  Efficient and Anonymous Web-Usage Mining for Web Personalization , 2003, INFORMS J. Comput..

[12]  Like Gao,et al.  Continually evaluating similarity-based pattern queries on a streaming time series , 2002, SIGMOD '02.

[13]  Sudipto Guha,et al.  Clustering Data Streams , 2000, FOCS.

[14]  Samuel Madden,et al.  Fjording the stream: an architecture for queries over streaming sensor data , 2002, Proceedings 18th International Conference on Data Engineering.

[15]  David J. DeWitt,et al.  NiagaraCQ: a scalable continuous query system for Internet databases , 2000, SIGMOD '00.

[16]  Minos N. Garofalakis,et al.  Wavelet synopses with error guarantees , 2002, SIGMOD '02.

[17]  Paul S. Bradley,et al.  Compressed data cubes for OLAP aggregate query approximation on continuous dimensions , 1999, KDD '99.

[18]  Jeffrey Scott Vitter,et al.  Dynamic Maintenance of Wavelet-Based Histograms , 2000, VLDB.

[19]  Cyrus Shahabi,et al.  How to evaluate multiple range-sum queries progressively , 2002, PODS '02.

[20]  C. Faloutsos.  Eecient Similarity Search in Sequence Databases , 1993 .

[21]  Ada Wai-Chee Fu,et al.  Efficient time series matching by wavelets , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[22]  Cyrus Shahabi,et al.  Alternative representations and abstractions for moving sensors databases , 2001, CIKM '01.

[23]  Cyrus Shahabi,et al.  Virtual Environments for the Assessment of Attention and Memory Processes: The Virtual Classroom and Office , 2002 .

[24]  Jeffrey Scott Vitter,et al.  Data cube approximation and histograms via wavelets , 1998, CIKM '98.

[25]  Alberto O. Mendelzon,et al.  Similarity-based queries , 1995, PODS '95.

[26]  Lingling Zhang,et al.  Alternative techniques for the efficient acquisition of haptic data , 2001, SIGMETRICS '01.

[27]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[28]  Prabhakar Raghavan,et al.  Computing on data streams , 1999, External Memory Algorithms.

[29]  Cyrus Shahabi,et al.  Knowledge discovery from users Web-page navigation , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[30]  Jennifer Widom,et al.  Continuous queries over data streams , 2001, SGMD.

[31]  Antonio Ortega,et al.  A comparison of different haptic compression techniques , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[32]  Kyuseok Shim,et al.  Approximate query processing using wavelets , 2001, The VLDB Journal.

[33]  Divyakant Agrawal,et al.  Using wavelet decomposition to support progressive and approximate range-sum queries over data cubes , 2000, CIKM '00.

[34]  Cyrus Shahabi,et al.  Analysis of haptic data for sign language recognition , 2001, HCI.