Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2016, Article ID 3083450, 22 pages
http://dx.doi.org/10.1155/2016/3083450
Research Article

Wise Mobile Icons Organization: Apps Taxonomy Classification Using Functionality Mining to Ease Apps Finding

Information Systems Department, The University of Haifa, Mount Carmel, 31905 Haifa, Israel

Received 12 August 2015; Accepted 24 November 2015

Academic Editor: Salil Kanhere

Copyright © 2016 David Lavid Ben Lulu and Tsvi Kuflik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Wikipedia, “Mobile app,” 2012, http://en.wikipedia.org/wiki/Mobile_app.
  2. S. Costello, How Many Apps Are in the iPhone App Store, About.com Guide, 2013, http://ipod.about.com/od/iphonesoftwareterms/qt/apps-in-app-store.htm.
  3. D. Horace, “App developers receive $12 for each iOS device sold,” Asymco, 2012, http://www.asymco.com/2012/02/19/app-developers-get-12-for-each-ios-device-sold/.
  4. S. Paul, Android users have an average of 95 apps installed on their phones, according to Yahoo Aviate data, TheNextWeb, TWN blog, 2014.
  5. Charles Newark-French, Mobile App Usage Further Dominates Web. Spurred by Facebook, 2012.
  6. Appsfire 2012, http://blog.appsfire.com/infographic-ios-apps-vs-web-apps/.
  7. S. Perez, App-ocalypse, TechCrunch, 2011, http://techcrunch.com/2011/12/18/app-ocalypse/.
  8. A. Karatzoglou, L. Baltrunas, K. Church, and M. Böhmer, “Climbing the app wall: enabling mobile app discovery through context-aware recommendations,” in Proceedings of the 21st ACM International Conference On Information And Knowledge Management (CIKM '12), pp. 2527–2530, ACM, November 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Yan, D. Chu, D. Ganesan, A. Kansal, and J. Liu, “Fast app launching for mobile devices using predictive user context,” in Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys '12), pp. 113–126, ACM, Lake District, UK, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Pash, “Organize your Apps by action instead of category for a more intuitive find-and-launch system,” Lifehacker, 2012, http://lifehacker.com/5882749/organize-your-apps-by-action-instead-of-category-for-a-more-intuitive-find+and+launch-system.
  11. D. L. B. Lulu and T. Kuflik, “Functionality-based clustering using short textual description: Helping users to find apps installed on their mobile device,” in Proceedings of the International Conference on Intelligent User Interfaces (IUI '13), pp. 297–305, ACM, New York, NY, USA, March 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Böhmer and A. Krüger, “A study on icon arrangement by smartphone users,” in Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI '13), pp. 2137–2146, ACM, Paris, France, April-May 2013. View at Publisher · View at Google Scholar
  13. J. G. Shanahan and E. J. Glover, “Bridging the app gap: from web search to app search via functional search,” in Proceedings of the 1st Workshop on Appification of the Web, Lyon, France, April 2012.
  14. M. Rudolf, “In pursuit of a perfect App search engine,” Workshop on the Appification of the Web, 2012.
  15. E. Gabrilovich, “From information needs to action needs: towards contextual app search and recommendation,” in Proceedings of the Workshop on the Appification of the Web (AppWeb '12), Lyon, France, April 2012.
  16. D. Barreau and B. A. Nardi, “Finding and reminding: file organization from the desktop,” ACM SIGCHI Bulletin, vol. 27, no. 3, pp. 39–43, 1995. View at Publisher · View at Google Scholar
  17. O. Bergman, S. Whittaker, M. Sanderson, R. Nachmias, and A. Ramamoorthy, “How do we find personal files? The effect of OS, presentation & depth on file navigation,” in Proceedings of the Conference on Human Factors in Computing Systems (CHI '12), Austin, Tex, USA, May 2012.
  18. S. Voida, E. D. Mynatt, and W. K. Edwards, “Re-framing the desktop interface around the activities of knowledge work,” in Proceedings of the 21st ACM Symposium on User Interface Software and Technology (UIST '08), pp. 211–220, ACM, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Bruce, A. Wenning, E. Jones, J. Vinson, and W. Jones, “Seeking an ideal solution to the management of personal information collections,” in Proceedings of the Information Seeking in Context Conference (ISIC '10), Murcia, Spain, 2010.
  20. Stardock, Fences, 2012, http://www.stardock.com/products/fences/.
  21. J. Karlin, “Launchy,” 2010, http://launchy.net/.
  22. Punk Labs, RocketDock, 2008, http://rocketdock.com/.
  23. Cogeco, RK Launcher, 2005, http://www.askvg.com/rk-launcher-one-of-the-best-dock-utility-for-windows/.
  24. Ecocardio, Orbit, October 2012, http://www.ghacks.net/2008/06/02/orbit-provides-a-circle-based-menu-for-windows/.
  25. E. X. Launcher, GO Launcher Dev Team, 2013, https://play.google.com/store/apps/details?id=com.gau.go.launcherex.
  26. ZeroTouchSystems, “Auto App Organizer,” 2012, https://play.google.com/store/apps/details?id=com.utility.autoapporganizer&feature=related_apps#?t=W251bGwsMSwxLDEwOSwiY29tLnV0aWxpdHkuYXV0b2FwcG9yZ2FuaXplciJd.
  27. GinLemon, Smart launcher, 2011, https://play.google.com/store/apps/details?id=ginlemon.flowerfree.
  28. EverythingMe Launcher, March 2014, http://www.androidauthority.com/everythingme-launcher-what-you-need-to-know-342218/.
  29. Aviate Beta, Yahoo, March 2014, https://play.google.com/store/apps/details?id=com.tul.aviate&hl=en_GB.
  30. Androidrank, October 2012, http://www.androidrank.org.
  31. SharpNLP—open source natural language processing tools 2006, version 1.0.2529 Beta, April 2013, http://sharpnlp.codeplex.com/.
  32. K. K. Schuler, VerbNet: a broad-coverage, comprehensive verb lexicon [Ph.D. thesis], University of Pennsylvania, Philadelphia, Pa, USA, 2005.
  33. Bing Synonyms API, October 2012, http://datamarket.azure.com/dataset/bing/synonyms.
  34. S. Banerjee and T. Pedersen, “Extended gloss overlaps as a measure of semantic relatedness,” in Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI '03), vol. 3, pp. 805–810, APA, Acapulco, Mexico, August 2003.
  35. G. A. Miller, “WordNet: a lexical database for English,” Communications of the ACM, vol. 38, no. 11, pp. 39–41, 1995. View at Publisher · View at Google Scholar
  36. T. Dao and T. Simpson, “Measuring similarity between sentences,” April 2013, http://wordnetdotnet.googlecode.com/svn/trunk/Projects/Thanh/Paper/.
  37. C. D. Manning, R. Prabhakar, and H. Schütze, Introduction to Information Retrieval, vol. 1, Cambridge University Press, Cambridge, UK, 2008.
  38. E. Gabrilovich and S. Markovitch, “Computing semantic relatedness using wikipedia-based explicit semantic analysis,” in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI '07), pp. 1606–1611, Hyderabad, India, January 2007.
  39. G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing & Management, vol. 24, no. 5, pp. 513–523, 1988. View at Publisher · View at Google Scholar · View at Scopus
  40. H.-F. Yu, C.-H. Ho, Y.-C. Juan, and C.-J. Lin, “LibShortText: a library for short-text classification and analysis,” Tech. Rep., 2013, http://www.csie.ntu.edu.tw/~cjlin/papers/libshorttext.pdf. View at Google Scholar
  41. C.-J. Lin, R. C. Weng, and S. S. Keerthi, “Trust region Newton method for logistic regression,” Journal of Machine Learning Research, vol. 9, pp. 627–650, 2008. View at Google Scholar
  42. K. Crammer and Y. Singer, “On the algorithmic implementation of multiclass kernel-based vector machines,” Journal of Machine Learning Research, vol. 2, pp. 265–292, 2002. View at Google Scholar
  43. B. E. Boser, I. M. Guyon, and V. N. Vapnik, “Training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory (COLT '92), pp. 144–152, July 1992. View at Scopus
  44. Z. Kozareva and E. Hovy, “A semi-supervised method to learn and construct taxonomies using the web,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '10), pp. 1110–1118, Cambridge, Mass, USA, October 2010.
  45. W. Wu, H. Li, H. Wang, and K. Q. Zhu, “Probase: a probabilistic taxonomy for text understanding,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 481–492, ACM, Scottsdale, Ariz, USA, May 2012.
  46. Ozgur Ozcitak, ImageListView, 2010, http://www.codeproject.com/Articles/43265/ImageListView.
  47. SAS, Statistical software, version 9.4, SAS Institute Inc., Cary, NC, USA.
  48. J. Brooke, “SUS: a ‘quick and dirty’ usability scale,” in Usability Evaluation in Industry, P. W. Jordan, B. Thomas, B. A. Weerdmeester, and I. L. McClelland, Eds., pp. 189–194, Taylor and Francis, London, UK, 1996. View at Google Scholar
  49. A. Bangor, P. Kortum, and J. Miller, “Determining what individual SUS scores mean: adding an adjective rating scale,” Journal of Usability Studies, vol. 4, pp. 114–123, 2009. View at Google Scholar
  50. A. Gorla, I. Tavecchia, F. Gross, and A. Zeller, “Checking app behavior against app descriptions,” in Proceedings of the 36th International Conference on Software Engineering (ICSE '14), pp. 1025–1035, ACM, Hyderabad, India, May 2014. View at Publisher · View at Google Scholar