ACM Home Page
Please provide us with feedback. Feedback
Searching large indexes on tiny devices: optimizing binary search with character pinning
Full text PdfPdf (425 KB)
Source
International Conference on Intelligent User Interfaces archive
Proceedings of the 13th international conference on Intelligent user interfaces table of contents
Sanibel Island, Florida, USA
SESSION: Mobile interaction table of contents
Pages 257-266  
Year of Publication: 2009
ISBN:978-1-60558-168-2
Authors
Guy Shani  Microsoft Research, Redmond, WA, USA
Christopher Meek  Microsoft Research, Redmond, WA, USA
Tim Paek  Microsoft Research, Redmond, WA, USA
Bo Thiesson  Microsoft Research, Redmond, WA, USA
Gina Danielle Venolia  Microsoft Research, Redmond, WA, USA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 7,   Downloads (12 Months): 96,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1502650.1502687
What is a DOI?

ABSTRACT

The small physical size of mobile devices imposes dramatic restrictions on the user interface (UI). With the ever increasing capacity of these devices as well as access to large online stores it becomes increasingly important to help the user select a particular item efficiently. Thus, we propose binary search with character pinning, where users can constrain their search to match selected prefix characters while making simple binary decisions about the position of their intended item in the lexicographic order. The underlying index for our method is based on a ternary search tree that is optimal under certain user-oriented constraints. To better scale to larger indexes, we analyze several heuristics that rapidly construct good trees. A user study demonstrates that our method helps users conduct rapid searches, using less keystrokes, compared to other methods.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
 
3
M. Breene. US portable music device forecast, 2007 to 2012. Technical report, Jupiter Research, 2007.
 
4
D. Card. US portable music device forecast, 2006 to 2011. Technical report, Jupiter Research, 2006.
 
5
L. Chittaro and L. D. Marco. Evaluating the effectiveness of "effective view navigation" for very long ordered lists on mobile devices. In Interact 2005: 10th IFIP International Conference on Human-Computer Interaction, pages 482--495, 2005.
 
6
D. E. Knuth. Optimum binary search trees. Acta Informatica, 1(1):14--25, 1971.
 
7
 
8
A. Lopez. Hierarchical phrase-based translation with suffix arrays. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, page 976985, 2007.
 
9
T. Matsumoto, D. M. W. Powers, and G. Jarrad. Application of search algorithms to natural language processi. In Australasian Language Technology Workshop, 2003.
10
 
11

Collaborative Colleagues:
Guy Shani: colleagues
Christopher Meek: colleagues
Tim Paek: colleagues
Bo Thiesson: colleagues
Gina Danielle Venolia: colleagues