Active File Systems for Data Mining and Multimedia

  • S. H. Srinivasan
  • Pranay Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2552)


Data mining and multimedia applications require huge amounts of storage. These applications are also compute-intensive. Active disks make use of the computational power available in the disk to reduce storage traffic. Many of the file system proposals for active disks work at the block level. In this paper we argue for the necessity of filtering at application level. We propose two file systems for active disks: active file system (ACFS) which binds files and filters at the file system level and active network file system (ANFS) which extends ACFS over networks. These file systems preserve the familiar Unix file system semantics to a large extent. We present an implementation of the file systems which makes minimal changes to the existing file system code in Linux.


File System System Call Block Level Read Request Input Queue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Erik Riedel, Garth A. Gibson, and Christos Faloutsos. Active storage for largescale data mining and multimedia. In Proc. 24th Int. Conf. Very Large Data Bases, pages 62–73, 1998. 398, 400Google Scholar
  2. [2]
    Alan F Benner. Fiber channel for SANs. McGraw-Hill, 2001. 398Google Scholar
  3. [3]
    R Hernandez, C K Chal, Geo. Cole, and K Carmichael. NAS and iSCSI Solutions. IBM Redbook, Feb 2002. 398Google Scholar
  4. [4]
    G A Gibson, David F Nagle, K Amiri, F W Chang, E M Feinberg, H Gobio., C Lee, B Ozceri, Erik Reidel, DR ochberg, and J Zelenka. File server scaling with network attached disk. In ACM International Conference on Measurement and Modelling of Computer Systems, June 1997. 398, 399Google Scholar
  5. [5]
    X Ma and A L Narasimha Reddy. MVSS: Multi View Storage System. In Proc. of ICDCS, Apr 2001. 399, 400Google Scholar
  6. [6]
    H Lim, V Kapoor, C Wighe, and David Du. Active disk file system: A distributed, scalable file system. In Proc. of the Eighteenth IEEE Symposium on Mass Storage Systems, pages 101–116, Apr 2001. 399Google Scholar
  7. [7]
    A Acharya, M Uysal, and J Saltz. Active disks programming model, algorithms and evaluation. In International Conference on Architectural Support for Programming Languages and Operating Systems, Oct 1998. 399, 400Google Scholar
  8. [8]
    S Berchtold, C Bohm, and H P Kriegel. Improving the query performance of high-dimensional index structures by bulk load operations. In Proc. of the Int. Conf. on Extending Database Technology, pages 216–230, Mar 1998. 399Google Scholar
  9. [9]
    S Berchtold, C Bohm, B Braunmuller, DA Keim, and H P Kriegel. Fast parallel similarity search in multimedia databases. In Proc ACM SIGMOD Int. Conf. on Management of Data, pages 1–12, 1997. 399Google Scholar
  10. [10]
    Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, 1994. 399Google Scholar
  11. [11]
    Hyeran Lim, Vikram Kapoor, Chirag Wighe, and David H.-C Du. Active disk file system: A distributed scalable file system. In IEEE Symposium on Mass Storage Systems, 2001. 400Google Scholar
  12. [12]
    Uresh Vahalia. Unix internals: The new frontiers. Prentice-Hall, 1996. 403Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • S. H. Srinivasan
    • 1
  • Pranay Singh
    • 1
  1. 1.Satyam Computer Services LtdApplied Research GroupBangaloreINDIA

Personalised recommendations