The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
권호 표지
DOI QR코드

DOI QR Code

A Method of Mining Visualization Rules from Open Online Text for Situation Aware Business Chart Recommendation

상황인식형 비즈니스 차트 추천기 개발을 위한 개방형 온라인 텍스트로부터의 시각화 규칙 추출 방법 연구

  • Received : 2019.11.19
  • Accepted : 2019.12.24
  • Published : 2020.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

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.

데이터의 성격과 시각화의 목적에 따라 비즈니스 차트를 선택하는 것은 비즈니스 분석에 유용한 지식이다. 그러나 현재 시각화 도구에는 상황에 맞는 비즈니스 차트를 선택할 수 있는 기능이 부족하다. 또한 매번마다 시각화 방법에 대해 전문가의 도움을 요청하는 것은 비용과 시간이 소요된다. 따라서 본 연구의 목적은 온라인으로 게시된 문서로부터 비즈니스 차트 선정 규칙에 대한 지식을 추출하여 비즈니스 차트 생산성을 향상시키는 방법을 제안하는 것이다. 이를 위해 인터넷에서 비즈니스 차트를 묘사하는 한국어, 영어 및 중국어 비정형 데이터를 수집하고 TF-IDF를 사용하여 컨텍스트와 비즈니스 차트 간의 관계를 계산했다. 또한 Galois 래티스를 사용하여 비즈니스 차트 선택 규칙을 생성했다. 제안된 방법으로 생성된 규칙의 품질을 평가하기 위해 실험군과 대조군에 대해 실험을 수행했다. 그 결과 제안된 방법으로 의미 있는 규칙이 추출되었음을 확인했다. 본 연구의 결과물로 시각화 전문가의 도움 없이도 사무직 직원들이 비즈니스 차트를 효율적으로 선택할 수 있을 것으로 기대된다. 또한 작업 중인 문서를 기반으로 비즈니스 차트를 추천함으로 직원 교육에 유용할 것이다.

Keywords

References

  1. 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. https://doi.org/10.1142/S0219622014500497
  2. Alvarado-Uribe, J., Garcia, A. B., Gonzalez-Mendoza, M., Espinosa, R. L., Martin, 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.
  3. Andor, C., Joo, A., and Mero, L., "Galoislattices: A possible representation of knowledge structures," Evaluation in Education, Vol. 9, No. 2, pp. 207-215, 1985. https://doi.org/10.1016/0191-765X(85)90015-1
  4. 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.
  5. Blasco, J., Aleixos, N., Cubero, S., Gomez-Sanchis, J., and Molto, 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. https://doi.org/10.1016/j.compag.2008.11.006
  6. 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. https://doi.org/10.1093/neuonc/now247
  7. 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.
  8. Cassol, I. and Arevalo, 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. https://doi.org/10.1002/spe.2549
  9. 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. https://doi.org/10.7838/jsebs.2016.21.1.091
  10. 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. https://doi.org/10.1109/TITS.2015.2436897
  11. 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. https://doi.org/10.1109/MCG.2015.51
  12. 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. https://doi.org/10.2196/jmir.6.2.e16
  13. 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. https://doi.org/10.1109/TVCG.2013.226
  14. 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.
  15. Hansen, C. D. and Johnson, C. R., The Visualization Handbook, USA: Academic, San Diego, CA, 2004.
  16. 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. https://doi.org/10.1109/2945.841119
  17. 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. https://doi.org/10.1016/j.elerap.2018.01.012
  18. 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.
  19. 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.
  20. 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. https://doi.org/10.1037/a0035505
  21. Keim, D. A., “Information visualization and visual data mining,” IEEE transactions on Visualization and Computer Graphics, Vol. 8, No. 1, pp. 1-8, 2002. https://doi.org/10.1109/2945.981847
  22. 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.
  23. 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. https://doi.org/10.7838/jsebs.2016.21.2.151
  24. Knaflic, C. N., Storytelling with data: A data visualization guide for business professionals, John Wiley & Sons, 2015.
  25. Kreuseler, M., Lopez, N., and Schumann, H., "A scalable framework for information visualization," In IEEE Symposium on Information Visualization 2000, pp. 27-36, 2000.
  26. 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.
  27. Lee, H. Y. and Ong, K. L., “Visualization support for data mining,” IEEE Expert, Vol. 11, No. 5, pp. 69-75, 1996. https://doi.org/10.1109/64.539019
  28. 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.
  29. 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. https://doi.org/10.14778/2732279.2732282
  30. 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.
  31. 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.
  32. 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.
  33. Perkel, J. M., “Data visualization tools drive interactivity and reproducibility in online publishing,” Nature, Vol. 554, No. 7690, pp. 133-134, 2018. https://doi.org/10.1038/d41586-018-01322-9
  34. 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. https://doi.org/10.1057/PALGRAVE.IVS.9500183
  35. 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.
  36. 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. https://doi.org/10.1109/tkde.2018.2833443
  37. Shin, D. H., “Analysis of online social networks: A cross-national study,” Online Information Review, Vol. 34, No. 3, pp. 473-495, 2010. https://doi.org/10.1108/14684521011054080
  38. Singhal, A., "Introducing the knowledge graph: Things, not strings," Available: https://googleblog.blogspot.mx/2012/05/introducing-knowledge-graph-things-not.html, 2016.
  39. 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.
  40. Symons, D., Konczewski, A., Johnston, L. D., Frensko, B., and Kraemer, K., "Enriching student learning with data visualization," 2017.
  41. 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.
  42. Tegarden, D. P., “Business information visualization,” Communications of the Association for Information Systems, Vol. 1, No. 4, pp. 1-38, 1999. https://doi.org/10.17705/1CAIS.00104
  43. 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.
  44. 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.
  45. Vessey, I., "Cognitive Þt: A theory-based analysis of graphs versus tables literature," Decision Sciences, Vol. 22, pp. 219-240, 1991.
  46. Voigt, M., Pietschmann, S., Grammel, L., and MeiBner, K., "Context-aware recommendation of visualization components," In The Fourth International Conference on Information, Process, and Knowledge Management (eKNOW), pp. 101-109, 2012.
  47. 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. https://doi.org/10.1016/j.eswa.2016.09.040
  48. 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. https://doi.org/10.1016/j.dss.2015.08.008
  49. 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.
  50. 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. https://doi.org/10.1108/IntR-05-2017-0211