How many ways to use CiteSpace? A study of user interactive events over 14 months

Journal of the Association for Information Science and Technology

Volumn 68, Issue 5, 2017, Pages 1234-1256

  Ping, Qing ^a   He, Jiangen ^a   Chen, Chaomei ^a  

^a Drexel University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC TOOLS; BEHAVIORAL PATTERNS; CITESPACE; LEVEL-1; NOCV2; PRIOR KNOWLEDGE; SCIENTIFIC LITERATURE; STATE TRANSITIONS; STEEP LEARNING CURVE;

VISUALIZATION;

CONFORMATIONAL TRANSITION; LEARNING; SCIENTIFIC LITERATURE;

EID: 85010918172     PISSN: 23301635     EISSN: 23301643     Source Type: Journal    
DOI: 10.1002/asi.23770     Document Type: Article

References (78)

1. Allendoerfer, K., Aluker, S., Panjwani, G., Proctor, J., Sturtz, D., Vukovic, M., & Chen, C. (2005). Adapting the cognitive walkthrough method to assess the usability of a knowledge domain visualization. In IEEE Symposium on Information Visualization (INFOVIS 2005). Washington, DC: IEEE Computer Society.
2. Arhippainen, L., & Tähti, M. (2003). Empirical evaluation of user experience in two adaptive mobile application prototypes. In Proceedings of the Second International Conference on Mobile and Ubiquitous Multimedia. New York: ACM.
3. Banerjee, A., & Ghosh, J. (2001). Clickstream clustering using weighted longest common subsequences. In Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining. Society for Industrial and Applied Mathematics
4. Bavoil, L., Callahan, S.P., Crossno, P.J., Freire, J., Scheidegger, C.E., Silva, C.T., & Vo, H.T. (2005). Vistrails: Enabling interactive multiple-view visualizations. In Visualization, 2005. VIS 05. IEEE. Washington, DC: IEEE Computer Society.
5. Bayir, M.A., Toroslu, I.H., Cosar, A., & Fidan, G. (2009). Smart miner: A new framework for mining large scale web usage data. In Proceedings of the 18th International Conference on World Wide Web. New York: ACM.
6. Bostock, M., & Heer, J. (2009). Protovis: A graphical toolkit for visualization. IEEE Transactions on Visualization and Computer Graphics, 15, 1121–1128.
7. Burnham, K.P., & Anderson, D.R. (2002). Model selection and multimodel inference: A practical information-theoretic approach. Springer Science & Business Media.
8. Callahan, S.P., Freire, J., Santos, E., Scheidegger, C.E., Silva, C.T., & Vo, H.T. (2006). VisTrails: visualization meets data management. In Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data. New York: ACM.
9. Cao, H., Jiang, D., Pei, J., Chen, E., & Li, H. (2009). Towards context-aware search by learning a very large variable length hidden markov model from search logs. In Proceedings of the 18th International Conference on World Wide Web. New York: ACM.
10. Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101, 5303–5310.
11. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for information Science and Technology, 57, 359–377.
12. Chen, L., Bhowmick, S.S., & Nejdl, W. (2009). COWES: Web user clustering based on evolutionary web sessions. Data & Knowledge Engineering, 68, 867–885.
13. Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E. & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology, 62, 1382–1402.
14. Cockburn, A., Gutwin, C., Scarr, J., & Malacria, S. (2015). Supporting novice to expert transitions in user interfaces. ACM Computing Surveys (CSUR), 47, 31.
15. Cook, K., Grinstein, G., & Whiting, M. (2014). The VAST Challenge: History, scope, and outcomes: an introduction to the Special Issue. Information Visualization, 13, 301–312.
16. Dimopoulos, C., Makris, C., Panagis, Y., Theodoridis, E., & Tsakalidis, A. (2010). A web page usage prediction scheme using sequence indexing and clustering techniques. Data & Knowledge Engineering, 69, 371–382.
17. Dogan, R.I., Murray, G.C., Névéol, A., & Lu, Z. (2009). Understanding PubMed® user search behavior through log analysis. Database, 2009, bap018.
18. Eccles, R., Kapler, T., Harper, R., & Wright, W. (2008). Stories in geotime. Information Visualization, 7, 3–17.
19. Fischer, G. (2001). User modeling in human–computer interaction. User Modeling and User-Adapted Interaction, 11, 65–86.
20. Fuxman, A., Tsaparas, P., Achan, K., & Agrawal, R. (2008). Using the wisdom of the crowds for keyword generation. In Proceedings of the 17th International Conference on World Wide Web. New York: ACM.
21. Gallego, M.D., Luna, P., & Bueno, S. (2008). User acceptance model of open source software. Computers in Human Behavior, 24, 2199–2216.
22. Gandy, L., Rahimi, S., & Gupta, B. (2005). A modified competitive agglomeration for relational data algorithm. In Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American. Washington, DC: IEEE Computer Society.
23. Girvan, M., & Newman, M.E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of United States of America, 99, 7821–7826.
24. Godoy, D., & Amandi, A. (2005). User profiling in personal information agents: A survey. The Knowledge Engineering Review, 20, 329–361.
25. Görg, C., Liu, Z., & Stasko, J. (2013). Reflections on the evolution of the Jigsaw visual analytics system. Information journalTitle, 0:(0), 1–11.
26. Gotz, D., & Zhou, M.X. (2009). Characterizing users' visual analytic activity for insight provenance. Information Visualization, 8, 42–55.
27. Gove, R. P. (2011). Understanding scientific literature networks: Case study evaluations of integrating visualizations and statistics (Doctoral dissertation).
28. Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Quarterly, 82, 581–629.
29. Guo, C., Liu, Y., Shen, W., Wang, H.J., Yu, Q., & Zhang, Y. (2009). Mining the web and the internet for accurate ip address geolocations. In INFOCOM 2009, IEEE. Washington, DC: IEEE Computer Society.
30. Harris, M., & Butterworth, G. (2012). Developmental psychology: A student's handbook. Psychology Press.
31. He, D., Göker, A., & Harper, D.J. (2002). Combining evidence for automatic web session identification. Information Processing & Management, 38, 727–742.
32. Heer, J., Card, S.K., & Landay, J.A. (2005). Prefuse: A toolkit for interactive information visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM.
33. Hindus, D., Ackerman, M.S., Mainwaring, S., & Starr, B. (1996). Thunderwire: A field study of an audio-only media space. In Proceedings of the 1996 ACM Conference on Computer Supported Cooperative Work. New York: ACM.
34. Hu, B., Zhang, Y., Chen, W., Wang, G., & Yang, Q. (2011). Characterizing search intent diversity into click models. In Proceedings of the 20th International Conference on World Wide Web. New York: ACM.
35. Huang, Z., Ng, J., Cheung, D.W., Ng, M.K., & Ching, W.K. (2001). A cube model for web access sessions and cluster analysis. In Proceedings of WEBKDD. Heidelberg:Springer-Verlag Berlin.
36. Kamvar, M., Kellar, M., Patel, R., & Xu, Y. (2009). Computers and iphones and mobile phones, oh my!: A logs-based comparison of search users on different devices. In Proceedings of the 18th International Conference on World Wide Web. New York: ACM.
37. Kang, Y.A., Gorg, C., & Stasko, J. (2011). How can visual analytics assist investigative analysis? Design implications from an evaluation. IEEE Transactions on Visualization and Computer Graphics, 17, 570–583.
38. Karrer, B., & Newman, M.E. (2011). Stochastic blockmodels and community structure in networks. Physical review E, 83, 016107.
39. Kullback, S., & Leibler, R.A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 79–86.
40. Kumar, R., & Tomkins, A. (2010). A characterization of online browsing behavior. In Proceedings of the 19th International Conference on World Wide Web. New York: ACM.
41. Lee, U., Liu, Z., & Cho, J. (2005). Automatic identification of user goals in web search. In Proceedings of the 14th International Conference on World Wide Web. New York: ACM.
42. Lin, J., & Wilbur, W.J. (2009). Modeling actions of PubMed users with n-gram language models. Information Retrieval, 12, 4, 487–503.
43. Linton, F., Joy, D., & Schaefer, H.P. (1999). Building user and expert models by long-term observation of application usage. Proceedings of the Seventh International Conference on User modeling, 129–138, Springer-Verlag New York.
44. Liu, N., Liu, Y., & Wang, X. (2010). Data logging plus e-diary: Towards an online evaluation approach of mobile service field trial. In Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services. New York: ACM.
45. Lu, Z., Wilbur, W.J., McEntyre, J.R., Iskhakov, A., & Szilagyi, L. (2009). Finding query suggestions for PubMed. In American Medical Informatics Association.
46. Mackinlay, J., Hanrahan, P., & Stolte, C. (2007). Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics, 13, 1137–1144.
47. Manning, C.D., & Schütze, H. (1999). Foundations of statistical natural language processing. (Vol. 999). Cambridge: MIT press.
48. Mobasher, B., Cooley, R., & Srivastava, J. (1999). Creating adaptive web sites through usage-based clustering of URLs. In Proceedings, 1999 Workshop on Knowledge and Data Engineering Exchange(KDEX'99). Washington, DC: IEEE Computer Society.
49. Nasraoui, O., Frigui, H., Joshi, A., & Krishnapuram, R. (1999). Mining web access logs using relational competitive fuzzy clustering. In Proceedings of the Eight International Fuzzy Systems Association World Congress. Washington, DC: IEEE Computer Society.
50. Newman, M.E. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69(6), 066133.
51. Norris, J.R. (1998). Markov chains (Vol. 2). New York: Cambridge University Press.
52. Osiński, S., Stefanowski, J., & Weiss, D. (2004). Lingo: Search results clustering algorithm based on singular value decomposition Intelligent information processing and web mining (pp. 359–368). Berlin, Heidelberg: Springer.
53. Poese, I., Uhlig, S., Kaafar, M.A., Donnet, B., & Gueye, B. (2011). IP geolocation databases: Unreliable? ACM SIGCOMM Computer Communication Review, 41, 53–56.
54. Rieman, J. (1993). The diary study: A workplace-oriented research tool to guide laboratory efforts. In Proceedings of the INTERACT'93 and CHI'93 Conference on Human Factors in Computing Systems. New York: ACM.
55. Maimon, O., & Rokach, L. (Eds.). (2005). Clustering methods. Data mining and knowledge discovery handbook (Vol. 2, pp. 321–352). New York: Springer.
56. Rousseeuw, P.J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
57. Schmid, B. (2012). An investigation of activity logging methods in user studies. Diploma thesis.
58. Shahabi, C., & Banaei-Kashani, F. (2003). Efficient and anonymous web-usage mining for web personalization. INFORMS Journal on Computing, 15, 123–147.
59. Shahabi, C., Zarkesh, A.M., Adibi, J., & Shah, V. (1997). Knowledge discovery from users web-page navigation. In Proceedings. Seventh International Workshop on Research Issues in Data Engineering, 1997. Washington, DC: IEEE Computer Society.
60. Silva, C.T., Anderson, E., Santos, E., & Freire, J. (2011). Using vistrails and provenance for teaching scientific visualization. Computer Graphics Forum (Vol. 30, No. 1, pp. 75–84).
61. Song, Q., & Shepperd, M. (2006). Mining web browsing patterns for E-commerce. Computers in Industry, 57, 622–630.
62. Song, Y., Ma, H., Wang, H., & Wang, K. (2013). Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance. In Proceedings of the 22nd International Conference on World Wide Web. New York: ACM.
63. Srivastava, J., Cooley, R., Deshpande, M., & Tan, P.N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter, 1, 12–23.
64. Stasko, J., Görg, C., & Liu, Z. (2008). Jigsaw: supporting investigative analysis through interactive visualization. Information Visualization, 7, 118–132.
65. Stephens, M. (1982). A question of generalizability. Theory & Research in Social Education, 9, 75–89.
66. Stolte, C., Tang, D., & Hanrahan, P. (2002). Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Transactions on Visualization and Computer Graphics, 8, 52–65.
67. Suryavanshi, B.S., Shiri, N., & Mudur, S.P. (2005). An efficient technique for mining usage profiles using relational fuzzy subtractive clustering. In Proceedings. International Workshop on Challenges in Web Information Retrieval and Integration, 2005. Washington, DC: IEEE Computer Society.
68. Synnestvedt, M.B., & Chen, C. (2005). Design and evaluation of the tightly coupled perceptual-cognitive tasks in knowledge domain visualization. In Proceedings of the 11th International Conference on Human-Computer Interaction (HCI International 2005). New York: ACM.
69. Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538.
70. Viegas, F.B., Wattenberg, M., Van Ham, F., Kriss, J., & McKeon, M. (2007). Manyeyes: A site for visualization at internet scale. IEEE Transactions on Visualization and Computer Graphics, 13, 1121–1128.
71. Wan, M., Jönsson, A., Wang, C., Li, L., & Yang, Y. (2012). Web user clustering and web prefetching using random indexing with weight functions. Knowledge and Information Systems, 33, 89–115.
72. Ward, Jr., J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.
73. Xie, Y., & Phoha, V.V. (2001). Web user clustering from access log using belief function. In Proceedings of the 1st International Conference on Knowledge Capture. New York: ACM.
74. Xu, J., & Liu, H. (2010). Web user clustering analysis based on KMeans algorithm. In 2010 International Conference on Information Networking and Automation (ICINA). Washington, DC: IEEE Computer Society.
75. Yan, T.W., Jacobsen, M., Garcia-Molina, H., & Dayal, U. (1996). From user access patterns to dynamic hypertext linking. Computer Networks and ISDN Systems, 28, 1007–1014.
76. Yuan, X., Chen, C., Zhang, X., Avery, J., & Xu, T. (2013). Effects of domain knowledge on user performance and perception in a knowledge domain visualization system Design, User Experience, and Usability. Web, Mobile, and Product Design (pp. 601–610). Springer Berlin Heidelberg.
77. Zhang, Z., & Nasraoui, O. (2006). Mining search engine query logs for query recommendation. In Proceedings of the 15th International Conference on World Wide Web. New York: ACM.
78. Zhao, Q., Hoi, S.C., Liu, T.Y., Bhowmick, S.S., Lyu, M.R., & Ma, W.Y. (2006). Time-dependent semantic similarity measure of queries using historical click-through data. In Proceedings of the 15th International Conference on World Wide Web. New York: ACM.