Advertisement

From Search Engines to Augmented Search Services: An End-User Development Approach

  • Gabriela BosettiEmail author
  • Sergio Firmenich
  • Alejandro Fernandez
  • Marco Winckler
  • Gustavo Rossi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

The World Wide Web is a vast and continuously changing source of information where searching is a frequent, and sometimes critical, user task. Searching is not always the user’s primary goal but an ancillary task that is performed to find complementary information allowing to complete another task. In this paper, we explore primary and/or ancillary search tasks and propose an approach for simplifying the user interaction during search tasks. Rather than focusing on dedicated search engines, our approach allows the user to abstract search engines already provided by Web applications into pervasive search services that will be available for performing searches from any other Web site. We also propose allowing users to manage the way in which the search results are presented and some possible interactions. In order to illustrate the feasibility of this approach, we have built a support tool based on a plug-in architecture that allows users to integrate new search services (created by themselves by means of visual tools) and execute them in the context of both kinds of searches. A case study illustrates the use of such tool. We also present the results of two evaluations that demonstrate the feasibility of the approach and the benefits in its use.

Keywords

Web search Web augmentation Client-side adaptation 

References

  1. 1.
    Aula, A., Jhaveri, N., Käki, M.: Information search and re-access strategies of experienced web users. In: WWW 2005 Proceedings of the 14th International Conference on World Wide Web, pp. 583–592 (2005)Google Scholar
  2. 2.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284, 34–43 (2001). doi: 10.1038/scientificamerican0501-34 CrossRefGoogle Scholar
  3. 3.
    Broder, A.: A taxonomy of web search. SIGIR Forum 36, 3–10 (2002). doi: 10.1145/792550.792552 CrossRefzbMATHGoogle Scholar
  4. 4.
    Card, S.K., Mackinlay, J.D., Shneiderman, B.: Focus + context. In: Readings in Information Visualization, pp. 306–309 (1999)Google Scholar
  5. 5.
    Card, S.K., Moran, T.P., Newell, A.: The Psychology of Human-Computer Interaction. L. Erlbaum Associates, Hillsdale (1983)Google Scholar
  6. 6.
    Cava, R., Freitas, C.M.D.S., Barboni, E., Palanque, P., Winckler, M.: Inside-in search: an alternative for performing ancillary search tasks on the web. In: 2014 9th Latin America Web Congress IEEE, pp. 91–99 (2014)Google Scholar
  7. 7.
    Ellis, D., Haugan, M.: Modelling the information seeking patterns of engineers and research scientists in an industrial environment. J. Doc. 53, 384–403 (1997). doi: 10.1108/EUM0000000007204 CrossRefGoogle Scholar
  8. 8.
    Firmenich, S., Bosetti, G., Rossi, G., Winckler, M., Barbieri, T.: Abstracting and structuring web contents for supporting personal web experiences. In: Bozzon, A., Cudre-Maroux, P., Pautasso, C. (eds.) ICWE 2016. LNCS, vol. 9671, pp. 77–95. Springer, Cham (2016). doi: 10.1007/978-3-319-38791-8_5 Google Scholar
  9. 9.
    Golovchinsky, G., Qvarfordt, P., Pickens, J.: Collaborative information seeking. Computer (Long Beach Calif.) 42, 47–51 (2009). doi: 10.1109/MC.2009.73 Google Scholar
  10. 10.
    Hearst, M.A.: User interfaces for search. In: Modern Information Retrieval the Concepts and Technology Behind Search Engines, pp. 21–56 (2010)Google Scholar
  11. 11.
    Hearst, M.A.: “Natural” search user interfaces. Commun. ACM 54, 60–67 (2011). doi: 10.1145/2018396.2018414 CrossRefGoogle Scholar
  12. 12.
    Kumar, R., Tomkins, A.: A characterization of online search behavior. IEEE Data Eng. Bull. 32, 1–9 (2009). doi: 10.1145/1772690.1772748 Google Scholar
  13. 13.
    MacLean, A., Carter, K., Lövstrand, L., Moran, T.: User-tailorable systems: pressing the issues with buttons. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Empower people - CHI 1990, pp. 175–182 (1990). doi: 10.1145/97243.97271
  14. 14.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49, 41 (2006). doi: 10.1145/1121949.1121979 CrossRefGoogle Scholar
  15. 15.
    McCallum, A., Nigam, K., Rennie, J., Seymore, K.: A machine learning approach to building domain-specific search engines. In: Proceedings of the Sixth International Joint Conference on Artificial Intelligence, IJCAI 1999, pp. 662–667 (1999)Google Scholar
  16. 16.
    Morris, D., Morris, M.R., Venolia, G.: SearchBar: a search-centric web history for task resumption and information re-finding. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems – CHI 2008, pp. 1207–1216 (2008)Google Scholar
  17. 17.
    Obal, C., Diaz, O.: The augmented web: rationales, opportunities and challenges on browser-side transcoding. ACM Trans. Web 9, 1–30 (2015). doi: 10.1145/2735633 Google Scholar
  18. 18.
    Shneiderman, B.: The Eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of 1996 IEEE Symposium on Visual Language, pp. 336–343 (1996)Google Scholar
  19. 19.
    Wilson, T.D.: Models in information behaviour research. J. Doc. 55, 249–270 (1999). doi: 10.1108/EUM0000000007145 CrossRefGoogle Scholar
  20. 20.
    Winckler, M., Cava, R., Barboni, E., Palanque, P., Freitas, C.: Usability aspects of the inside-in approach for ancillary search tasks on the web. In: Abascal, J., Barbosa, S., Fetter, M., Gross, T., Palanque, P., Winckler, M. (eds.) INTERACT 2015. LNCS, vol. 9297, pp. 211–230. Springer, Cham (2015). doi: 10.1007/978-3-319-22668-2_18 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.LIFIA, CIC, Facultad de InformáticaUniversidad Nacional de La PlataLa PlataArgentina
  2. 2.CONICETLa PlataArgentina
  3. 3.ICS-IRITUniversité Toulouse IIIToulouseFrance

Personalised recommendations