Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Refinding Data using Context based Memory Technique

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Kajalekar S. J., Patil B. M., Chandode V. M.
10.5120/ijca2017913410

Kajalekar S J., Patil B M. and Chandode V M.. Refinding Data using Context based Memory Technique. International Journal of Computer Applications 161(12):29-33, March 2017. BibTeX

@article{10.5120/ijca2017913410,
	author = {Kajalekar S. J. and Patil B. M. and Chandode V. M.},
	title = {Refinding Data using Context based Memory Technique},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {12},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {29-33},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume161/number12/27202-2017913410},
	doi = {10.5120/ijca2017913410},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Data retrieval is a major aspect of data mining. Many times users need to access the information they have previously come across, i.e. refinding the information. In this research, ReFinder, which is a context based information refinding system, is used. It uses natural recall characteristics of human memory. By this, users can refind files and web pages according to their previously accessed context. A query by context model is built over a context memory snapshot. These instances are organized in a clustered and associated manner and evolve in life cycles just like the human brain. An eight weeks study was observed and time, place and activity were found to be useful recall clues. Experimental results show that the technique of associative clustering leads to best precision and recall. On average, 16.5 seconds are needed to complete a refinding request against 86.32 seconds with other existing methods. Future challenges like automatic annotation and context degradation are also discussed.

References

  1. R. Capra, M. Pinney, and M.A. Perez-Quinones, “Refinding Is Not Finding Again,” technical report, Aug. 2005.
  2. S.K. Tyler and J. Teevan, “Large Scale Query Log Analysis of ReFinding,” Proc. Third ACM Int’l Conf. Web Search and Data Mining (WSDM), 2010.
  3. J. Teevan, “The Re:Search Engine: Simultaneous Support for Finding and Re-Finding,” Proc. 20th Ann. ACMSymp.User Interface Software and Technology
  4. M. Lamming and M. Flynn, “‘Forget-Me-Not’-Intimate Computing in Support of Human Memory,” Proc. FRIEND21 Int’l Symp. Next Generation Human Interface, 1994.
  5. E. Tulving, “What is Episodic Memory?” Current Directions in Psychological Science, vol. 2, no. 3, pp. 67-70, 1993.
  6. B. MacKay, M. Kellar, and C. Watters, “An Evaluation of Landmarks for Re-Finding Information on the Web,” Proc. Extended Abstracts on Human Factors in Computing Systems (CHI ’05 EA), 2005.
  7. D. Morris, M.R. Morris, and G. Venolia, “Searchbar: A Search- Centric Web History for Task Resumption and Information Re-Finding,” Proc. SIGCHI Conf. Human Factors in Computing Systems (CHI), 2008.
  8. J.P. Dittrich and M.A. Salles, “iDM: A Unified and Versatile Data Model for Personal Dataspace Management,” Proc. 32nd Int’l Conf Very Large Data Bases (VLDB), 2006.
  9. Deng et all, “ReFinder: A Context-Based Information Refinding System”, IEEE Transactions on Knowledge and Data Engineering, vol. 25, no.9, September 2013

Keywords

Information refinding, context memory, association based clustering, decay