A Method of Mining Visualization Rules from Open Online Text for Situation Aware Business Chart Recommendation
Selecting business charts based on the nature of the data and the purpose of the visualization is useful in business analysis. However, current visualization tools lack the ability to help choose the right business chart for the context. Also, soliciting expert help about visualization methods for every analysis is inefficient. Therefore, the purpose of this study is to propose an accessible method to improve business chart productivity by creating rules for selecting business charts from online published documents. To this end, Korean, English, and Chinese unstructured data describing business charts were collected from the Internet, and the relationships between the contexts and the business charts were calculated using TF-IDF. We also used a Galois lattice to create rules for business chart selection. In order to evaluate the adequacy of the rules generated by the proposed method, experiments were conducted on experimental and control groups. The results confirmed that meaningful rules were extracted by the proposed method. To the best of our knowledge, this is the first study to recommend customizing business charts through open unstructured data analysis and to propose a method that enables efficient selection of business charts for office workers without expert assistance. This method should be useful for staff training by recommending business charts based on the document that he/she is working on.
Al-Kassab, J., Ouertani, Z. M., Schiuma, G., and Neely, A., “Information visualization to support management decisions,” International Journal of Information Technology & Decision Making, Vol. 13, No. 2, pp. 407-428, 2014.
Alvarado-Uribe, J., García, A. B., Gonzalez-Mendoza, M., Espinosa, R. L., Martín, J., and Espinosa, M., “Semantic approach for discovery and visualization of academic information structured with OAI-PMH,” Acta Polytechnica Hungarica, Vol. 14, No. 3, pp. 129-148, 2017.
Andor, C., Joó, A., and Mérö, L., “Galois-lattices: A possible representation of knowledge structures,” Evaluation in Education, Vol. 9, No. 2, pp. 207-215, 1985.
Anwar, A., Nagel, T., and Ratti, C., “Traffic origins: A simple visualization technique to support traffic incident analysis,” in IEEE Pac, Vis. Symp., pp. 316-319, 2014.
Blasco, J., Aleixos, N., Cubero, S., Gómez-Sanchís, J., and Moltó, E., “Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features,” Computers and Electronics in Agriculture, Vol. 66, No. 1, pp. 1-8, 2009.
Bowman, R. L., Wang, Q., Carro, A., Verhaak, R. G., and Squatrito, M., “GlioVis data portal for visualization and analysis of brain tumor expression datasets,” Neuro-oncology, Vol. 19, No. 1, pp. 139-141, 2016.
Brown, L. D., Hua, H., and Gao, C., “A widget framework for augmented interaction in SCAPE,” In Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology, pp. 1-10, 2003.
Cassol, I. and Arévalo, G., “A methodology to infer and refactor an object-oriented model from C applications,” Software: Practice and Experience, Vol. 48, No. 3, pp. 550-577, 2018.
Chang, T. W., “A literature review on information visualization of manufacturing industry sector,” The Journal of Society for e-Business Studies, Vol. 21, No. 1, pp. 91-104, 2017.
Chen, W., Guo, F., and Wang, F. Y., “A survey of traffic data visualization,” IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 6, pp. 2970-2984, 2015.
Choe, E. K. and Lee, B., “Characterizing visualization insights from quantified selfers’ personal data presentations,” IEEE Computer Graphics and Applications, Vol. 35, No. 4, pp. 28-37, 2015.
Enzenhofer, M., Bludau, H. B., Komm, N., Wild, B., Mueller, K., Herzog, W., and Hochlehnert, A., “Improvement of the educational process by computer-based visualization of procedures: Randomized controlled trial,” Journal of Medical Internet Research, Vol. 6, No. 2, p. e16, 2004.
Ferreira, N., Poco, J., Vo, H. T., Freire, J., and Silva, C. T., “Visual exploration of big spatio-temporal urban data: A study of New York City taxi trips,” IEEE Trans. Vis. Comput. Graphics, Vol. 19, No. 12, pp. 2149-2158, 2013.
Gmati, H. and Mouakher, A., “Fast and compact cover extraction from big formal contexts,” In 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 209-212, 2018.
Hansen, C. D. and Johnson, C. R., The Visualization Handbook, USA: Academic, San Diego, CA, 2004.
Herman, I., Melançon, G., and Marshall, M. S., “Graph visualization and navigation in information visualization: A survey,” IEEE Transactions on Visualization and Computer Graphics, Vol. 6, No. 1, pp. 24-43, 2000.
Hwangbo, H., Kim, Y. S., and Cha, K. J., “Recommendation system development for fashion retail e-commerce,” Electronic Commerce Research and Applications, Vol. 28, pp. 94-101, 2018.
Ifenthaler, D. and Pirnay-Dummer, P., “Model-based tools for knowledge assessment,” In J. M. Spector, M. D. Merrill, J. Elen, and M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed.), New York, NY: Springer, pp. 289-301, 2014.
Ifenthaler, D., “Learning analytics,” In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology, Vol. 2, pp. 447-451, Thousand Oaks, CA: Sage, 2015.
Ifenthaler, D., “Toward automated computer-based visualization and assessment of team-based performance,” Journal of Educational Psychology, Vol. 106, No. 3, pp. 651-665, 2014.
Keim, D. A., “Information visualization and visual data mining,” IEEE transactions on Visualization and Computer Graphics, Vol. 8, No. 1, pp. 1-8, 2002.
Key, A., Howe, B., Perry, D., and Aragon, C., “Vizdeck: Self-organizing dashboards for visual analytics,” In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 681-684, 2012.
Kim, J. Y. and Kim, D. “A Study on the method for extracting the purpose-specific customized information from online product reviews based on text mining,” Journal of Society for e-Business Studies, Vol. 21, No. 2, pp. 151-161, 2017.
Knaflic, C. N., Storytelling with data: A data visualization guide for business professionals, John Wiley & Sons, 2015.
Kreuseler, M., Lopez, N., and Schumann, H., “A scalable framework for information visualization,” In IEEE Symposium on Information Visualization 2000, pp. 27-36, 2000.
Lee, B., Brehmer, M., Isenberg, P., Choe, E. K., Langner, R., and Dachselt, R., “Data visualization on mobile devices,” In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 7, 2018.
Lee, H. Y. and Ong, K. L., “Visualization support for data mining,” IEEE Expert, Vol. 11, No. 5, pp. 69-75, 1996.
Mehta, A., Makkar, P., Palande, S., and Wankhede, S. B., “Semantic web search engine,” International Journal of Engineering Research and Technology, Vol. 4, No. 4, pp. 687-691, 2015.
Morton, K., Balazinska, M., Grossman, D., and Mackinlay, J., “Support the data enthusiast: Challenges for next-generation data-analysis systems,” Proceedings of the VLDB Endowment, Vol. 7, No. 6, pp. 453-456, 2014.
Mouromtsev, D., Pavlov, D., Emelyanov, Y., Morozov, A., Razdyakonov, D., and Galkin, M., “The simple web-based tool for visualization and sharing of semantic data and ontologies,” In International Semantic Web Conference (Posters & Demos), 2015.
Myers, B. A., Goldstein, J., and Goldberg, M. A., “Creating charts by demonstration,” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 106-111, 1994.
Oh, J., Kim, J., Kim, J., and Kim, D., “Analysis of web traffic change using change ratio visualization,” Proceedings of the Korea IT Service 2014, pp. 89-92.
Perkel, J. M., “Data visualization tools drive interactivity and reproducibility in online publishing,” Nature, Vol. 554, No. 7690, pp. 133-134, 2018.
Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., and Andrienko, G., “Visually driven analysis of movement data by progressive clustering,” Information Visualization, Vol. 7, No. 3/4, pp. 225-239, 2008.
Scheepens, R., Willems, N., van de Wetering, H., and Van Wijk, J. J., “Interactive visualization of multivariate trajectory data with density maps,” In 2011 IEEE Pacific Visualization Symposium, pp. 147-154, 2011.
Shi, C., Hu, B., Zhao, W. X., and Philip, S. Y., “Heterogeneous information network embedding for recommendation,” IEEE Transactions on Knowledge and Data Engineering, Vol. 31, No. 2, pp. 357-370, 2018.
Shin, D. H., “Analysis of online social networks: A cross-national study,” Online Information Review, Vol. 34, No. 3, pp. 473-495, 2010.
Singhal, A., “Introducing the knowledge graph: Things, not strings,” Available: https://googleblog.blogspot.mx/2012/05/introducing-knowledge-graph-things-not.html, 2016.
Streit, A., Pham, B., and Brown, R., “Visualization support for managing large business process specifications,” In International Conference on Business Process Management, pp. 205-219, Springer, Berlin, Heidelberg, 2005.
Symons, D., Konczewski, A., Johnston, L. D., Frensko, B., and Kraemer, K., “Enriching student learning with data visualization,” 2017.
Tang, M., Dai, X., Cao, B., and Liu, J., “Wswalker: A random walk method for QoS-Aware Web service recommendation,” In 2015 IEEE International Conference on Web Services, pp. 591-598, 2015.
Tegarden, D. P., “Business information visualization,” Communications of the Association for Information Systems, Vol. 1, No. 4, pp. 1-38, 1999.
Tummarello, G., Delbru, R., and Oren, E., “Sindice. com: Weaving the open linked data,” In The Semantic Web Springer, Berlin, Heidelberg, pp. 552-565, 2007.
Vartak, M., Madden, S., Parameswaran, A., and Polyzotis, N., “SeeDB: Supporting visual analytics with data-driven recommendations,” Proceedings of the VLDB Endowment, Vol. 8, No. 13, 2015.
Vessey, I., “Cognitive Þt: A theory-based analysis of graphs versus tables literature,” Decision Sciences, Vol. 22, pp. 219-240, 1991.
Voigt, M., Pietschmann, S., Grammel, L., and Meißner, K., “Context-aware recommendation of visualization components,” In The Fourth International Conference on Information, Process, and Knowledge Management (eKNOW), pp. 101-109, 2012.
Wei, J., He, J., Chen, K., Zhou, Y., and Tang, Z. “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Systems with Applications, Vol 69, pp. 29-39, 2017.
Xu, C., Peak, D., and Prybutok, V., “A customer value, satisfaction, and loyalty perspective of mobile application recommendations,” Decision Support Systems, Vol. 79, pp. 171-183, 2015.
Zhang, L., Yan, Q., Lu, J., Chen, Y., and Liu, Y., “Empirical research on the impact of personalized recommendation diversity,” In Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019.
Zhu, D. H., Wang, Y. W., and Chang, Y. P., “The influence of online cross-recommendation on consumers’ instant cross-buying intention: The moderating role of decision-making difficulty,” Internet Research, Vol. 28, No. 3, pp. 604-622, 2018.
- There are currently no refbacks.