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Promoting Understanding of Logical Structures of File Systems

Explore the effectiveness of visualizing logical structures of file systems using algorithms. Investigate challenges, contributions, and the impact on learning and teaching methods. Discover new approaches for teaching file systems.

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Promoting Understanding of Logical Structures of File Systems

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  1. Promoting Understanding of Logical Structures of File Systems Dr Ian Mitchell

  2. Motivation and Objectives • Current Research/Tools • Visualisation decomposed • Is Visualisation helpful • Experiment • Contribution • Challenges • Problem • Linked to motivation • How to visualise the logical structure of a file system • Motivation • “Learning is motivated by interest and understanding by visualisation” A.B. Garrett • “picture worth a 1000 words” • Difficulty in understanding Logical structure of initial file system • Use existing ideas to teach File Systems? • Or do we invent new ones?

  3. Visualisation Algorithms Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  4. Visualisation Algorithms Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  5. Visualisation Algorithms – Linear Structures Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  6. Visualisation Algorithms – Search Structures Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  7. Visualisation Algorithms – Other Data Structures Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  8. Visualisation Algorithms –Sorting Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  9. Visualisation Algorithms –Search Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  10. Visualisation Algorithms –Graphs Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  11. Visualisation Algorithms –Compression Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  12. Visualisation Algorithms –Memory Management Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  13. Visualisation Algorithms –Other Algorithms Source: Shaffer et al “Algorithm Visualisation: A report on the State of the Field“,SIGCSE'07.

  14. File Systems • Dead Analysis • Important • Confirming location of file • Content of the file • The provenance of the file • Verification of the file • Discovery of Evidence • Discovery of Hidden Data • Recovery • Problem • Scalability • Complex Algorithms • Hexidecimal/Little Endianness • Abstraction tools • Programme Design • Digital Investigation • File Systems Analysis • 24 week modules in parallel?

  15. Experiment – find Cluster2 (C2) • Cohort : 21 u/g • FAT16? • FAT32 is idiosyncratic • 32 is a misnomer • 28bit cluster chain - see MS White paper on FAT • Ubiquitous – 2GB • Cluster Analysis is 16 bit • FAT8 is obsolete • FAT12 is possible but floppy drives are slow • FAT 16 has 16 bit cluster chain analysis • C2 = R + F + F + R • C2  Size – Capacity • Four Stages • Acquisition • Locating and Identifying Parameters • Extracting values • Calculating Offset

  16. Conceptual - FAT16 and 2Gbyte Memory Stick Boot FAT1 FAT2 Root Content

  17. Factual - 0-35 Bytes of FAT12/16/32 Boot Sector

  18. Example – Boot Sector Bytes per Sector: 0x200 Size of Reserved Area: 0x02 EB 3C 90 4D 53 44 4F 53 – 35 2E 30 00 02 40 02 00 02 00 02 00 00 F8 EF 00 – 3F 00 FF 00 80 1F 00 00 Size of RD Size of FAT Number of FATs: 0x02

  19. Example – Boot Sector Bytes per Sector: 0x200 Size of Reserved Area: 0x02 EB 3C 90 4D 53 44 4F 53 – 35 2E 30 00 02 40 02 00 02 00 02 00 00 F8 EF 00 – 3F 00 FF 00 80 1F 00 00 Size of RD 00x0200 Size of FAT 00x00EF Number of FATs: 0x02

  20. Boot FAT1 FAT2 Root Content 2 239 239 32 241 Sector Cluster 0 2 480 512 3,907,711 2 61,051

  21. Confirmation - TSK FILE SYSTEM INFORMATION -------------------------------------------- File System Type: FAT16 OEM Name: MSDOS5.0 Volume ID: 0x3ed4054c Volume Label (Boot Sector): NO NAME Volume Label (Root Directory): 11 FAT File System Type Label: FAT16 Sectors before file system: 8064 File System Layout (in sectors) Total Range: 0 - 3907711 * Reserved: 0 - 1 ** Boot Sector: 0 * FAT 0: 2 - 240 * FAT 1: 241 - 479 * Data Area: 480 - 3907711 ** Root Directory: 480 - 511 ** Cluster Area: 512 - 3907711 METADATA INFORMATION -------------------------------------------- Range: 2 - 62515718 Root Directory: 2 CONTENT INFORMATION -------------------------------------------- Sector Size: 512 Cluster Size: 32768 Total Cluster Range: 2 - 61051 FAT CONTENTS (in sectors) -------------------------------------------- 512-575 (64) -> EOF

  22. Confirmation – FTK Imager

  23. Is PowerPoint Pointless? • Narayanan & Hegarty • Decomposition • Static Mental Model • Referential Connections • Causal Chain of Events • Dynamic Mental Model • Nap et al • “Effective visualisation requires learner engagement” Nap et al • Graphical Representation • Adaptation • Multiple Views • Performance Information • Execution History • Flexible Control • Learner Built • Data Sets • Support Dynamic Questions • Support Dynamic Feedback • Complement Visualisations with Explanations.

  24. Results • Reflector • Weblog • Non-assessed • Open agenda, although I start with a question • Post-it notes • Completion of sheets • Coursework • 6 weeks • No generic tool; combined tools; • ALVIS was confusing • Some Students like Java Code; others like the formula; some invented their own • Reassurance

  25. Contribution & Challenges • Developing Subject with under-developed tools • Reliance on commercial or traditional CS software • Dead Analysis & New File Systems to keep up with Hardware (disk size development is double Moore’s Law). • New Subject Domain • Commercial Software Requirements • Scalability • Open Source Software Requirements • Abstraction • Hexiphobic • Complexity

  26. Bibliography Alvis. http://eecs.wsu.edu/~ veupl/soft/alvis/index.htm. Exploring the Role of Visualization and Engagement in Computer Science Education, 2003. S. Edwards C. Shaer, M. Cooper. Algorithm visualisation: A report onthe state of the eld. In SIGCSE'07, March 2007. B. Carrier. The sleuth kit. http://www.thesleuthkit.org. A. B. Garrett. Visualization: A step to understanding. J. Chem. Educ.,25 (10), 1948. N. H. Narayanan and M. Hegarty. On designing comprehensible interactive hypermedia manuals. Int. Journal of Human-Computer Studies. N. Presmeg. Research on visualization in learning and teaching mathematics.1988. D. Schrimpsher S. Hansen, N. Narayanan and M. Hegarty. Empirical studies of animation-embedded hypermedia algorithm visualisations. Technical report, Auburn University, 1998. Breakpoint Software. Hexworkshop. http://www.hexworkshop.com. FAT: General Overview of On-Disk Format. Microsoft Corporation 2000.

  27. Boot FAT1 FAT2 Root Content

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