Weak signal identification with semantic web mining

Expert Systems with Applications

Volumn 40, Issue 12, 2013, Pages 4978-4985

  Thorleuchter, Dirk ^a   Van Den Poel, Dirk ^b  

^a FRAUNHOFER INT   (Germany)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

Ansoff; Latent semantic indexing; SVD; Weak Signal; Web mining

Indexed keywords

DATA MINING; FORECASTING; INDEXING (OF INFORMATION); KNOWLEDGE BASED SYSTEMS; SINGULAR VALUE DECOMPOSITION; STRATEGIC PLANNING; TECHNOLOGICAL FORECASTING; WEB CRAWLER;

ANSOFF; AUTOMATED IDENTIFICATION; INTERNET INFORMATION; KNOWLEDGE-BASED APPROACH; LATENT SEMANTIC INDEXING; WEAK SIGNAL IDENTIFICATION; WEAK SIGNALS; WEB MINING;

SEMANTIC WEB;

EID: 84885063026     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.03.002     Document Type: Article

References (81)

1. Abebe, M., Angriawan, A., & Tran, H. (2010). Chief executive external network ties and environmental scanning activities: Anempirical examination. Strategic Management Review, 4(1), 30-43.
2. Alallak, B. (2010). Evaluating the adoption and use ofInternet-based marketing Information systems to improve marketing intelligence. International Journal of Marketing Studies, 2(2), 87-101.
3. Ansoff, I. H. (1975). Managing strategic surprise byresponse toweak signals. California Management Review, 18(2), 21-33.
4. Ansoff, I. H. (1984). Implanting strategic management. New Jersey:Prentice Hall.
5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4-5), 993-1022.
6. Buckinx, W., Moons, E., Van den Poel, D., & Wets, G. (2004). Customer-adapted coupon targeting using feature selection. Expert Systems with Applications, 26(4), 509-518.
7. Chen, M. C., Chiu, A. L., & Chang, H. H. (2005). Mining changes incustomer behavior in retail marketing. Expert System with Applications, 28(4), 773-781.
8. Chen, M.-Y., Chu, H.-C., & Chen, Y.-M. (2010). Developing asemantic-enable information retrieval mechanism. Expert Systems with Applications, 37(1), 322-340.
9. Choi, C., Kim, S., & Park, Y. (2007). Apatent-based cross impact analysis for quantitative estimation oftechnological impact:The case ofinformation and communication technology. Technological Forecasting and Social Change, 74(2007), 1296-1314. (Pubitemid 47338190)
10. Choo, C. W., & Auster, E. (1993). Environmental scanning:Acquisition and use of information by managers. Annual Review ofInformation Science and Technology, 28, 279-314.
11. Christidis, K., Mentzas, G., & Apostolou, D. (2012). Using latent topics toenhance search and recommendation inEnterprise Social Software. Expert Systems with Applications, 39(10), 9297-9307.
12. Decker, R., Wagner, R., & Scholz, S. W. (2005). Aninternet-based approach to environmental scanning inmarketing planning. Marketing Intelligence & Planning, 23(2), 189-200.
13. Deerwester, S., Dumais, S., Furnas, G., Landauer, T., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-391.
14. DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves:A nonparametric approach. Biometrics, 44(3), 837-837.
15. D'Haen, J., Van den Poel, D., & Thorleuchter, D. (2013). Predicting customer profitability during acquisition:Finding the optimal combination ofdata source and data mining technique. Expert Systems with Applications, 40(6), 2007-2012.
16. Finzen, J., Kintz, M., & Kaufmann, S. (2012). Aggregating web-based ideation platforms. International Journal ofTechnology Intelligence and Planning, 8(1), 32-46.
17. Gericke, W., Thorleuchter, D., Weck, G., Reiländer, F., & Loß, D. (2009). Vertrauliche verarbeitung staatlich eingestufter information -die informationstechnologie im geheimschutz. Informatik Spektrum, 32(2), 102-109.
18. Halpern, E. J., Albert, M., Krieger, A. M., Metz, C. E., & Maidment, A. D. (1996). Comparison of receiver operating characteristic curves onthe basis ofoptimal operating points. Academic Radiology, 3(3), 245-253.
19. Hanley, J. A., & McNeil, B. J. (1982). The meaning and use ofthe area under areceiver operating characteristic (ROC)curve. Radiology, 143 (1), 29-36. (Pubitemid 12142173)
20. Hiltunen, E. (2008). The future sign and its three dimensions. Futures, 40(3), 247-260.
21. Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the twenty-second annual international SIGIR conference onresearch and development in, information retrieval (SIGIR-99).
22. Holopainen, M., & Toivonen, M. (2012). Weak signals:Ansoff today. Futures, 44(3), 198-205.
23. Ilmola, L., & Kuusi, O. (2006). Filters ofweak signals hinder foresight:Monitoring weak signals efficiently incorporate decision-making. Futures, 38(8), 908-924. (Pubitemid 44276107)
24. Jiang, J., Berry, M. W., Donato, J. M., Ostrouchov, G., & Grady, N. W. (1999). Mining consumer product data via latent semantic indexing. Intelligent Data Analysis, 3(5), 377-398. (Pubitemid 33260722)
25. Ko, Y., & Seo, J. (2009). Text classificationfrom unlabeled documents with bootstrapping and feature projection techniques. Information Processing & Management, 45(1), 70-83.
26. Kobayashi, M., & Takeda, K. (2000). Information retrieval onthe web. Association for Computing Machinery, 32(2), 144.
27. Kosala, R., & Blockeel, H. (2000). Web research:Asurvey. ACM SIGKDD Explorations Newsletter, 2(1).
28. Kuosa, T. (2010). Futures signals sense-making framework (FSSF):Astart-up tool to analyse and categorise weak signals, wild cards, drivers, trends, and other types of information. Futures, 42(1), 42-48.
29. Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Advances in neural information processing systems 13. Proceedings ofthe 2000 conference (pp. 556-562). MIT Press.
30. Lee, D. D., & Seung, H. S. (1999). Learning the parts ofobjects bynon-negative matrix factorization. Nature, 401 (6755), 788-791.
31. Lee, C. H., & Wang, S. H. (2012). Aninformation fusion approach tointegrate image annotation and text mining methods for geographic knowledge discovery. Expert Systems with Applications, 39(10), 8954-8967.
32. Lin, M.-H., & Hong, C.-F. (2011). Opportunities for crossing the chasm between early adopters and the early majority through new uses ofinnovative products. The Review of Socionetwork Strategies, 5(2), 27-42.
33. Luo, Q., Chen, E., & Xiong, H. (2011). Asemantic term weighting scheme for text categorization. Expert Systems with Applications, 38(10), 12708-12708.
34. Mendonça, S., Cardoso, G., & Caraça, J. (2012). The strategic strength ofweak signal analysis. Futures, 44(3), 218-218.
35. Mendonça, S., Pina eCunha, M., Kaivo-oja, J., & Ruff, F. (2004). Wild cards, weak signals and organisational improvisation. Futures, 36(2), 201-218. (Pubitemid 38013071)
36. Migueis, V. L., Van den Poel, D., Camanho, A. S., & Cunha, J. F. (2012). Modeling partial customer churn:Onthe value offirstproduct-category purchase sequences. Expert Systems with Applications, 39(12), 11250-11256.
37. Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). Aliterature review and classification ofrecommender systems research. Expert Systems with Applications, 39(11), 10059-10072.
38. Prinzie, A., & Van den Poel, D. (2006). Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models. European Journal of Operational Research, 170 (3), 710-734. (Pubitemid 41607633)
39. Prinzie, A., & Van den Poel, D. (2007). Predicting home-appliance acquisition sequences: Markov/Markov for discrimination and survival analysis for modeling sequential information in NPTB models. Decision Support Systems, 44(1), 28-45. (Pubitemid 47374297)
40. Purandre, P. (2008). Web mining:Akey toimprove business onweb. In IADIS European conference data mining (pp. 155-159).
41. Ramírez, E. H., & Brena, R. F. (2012). Query based topic modeling:Aninformation- theoretic framework for semantic analysis inlarge-scale collections. In Quantitative semantics and soft computing methods for the web:Perspectives and applications (pp. 69-95). USA:Information Science Pub.
42. Ramirez, E. H., Brena, R. F., Magatti, D., & Stella, F. (2012). Topic model validation. Neurocomputing, 76(1), 125-133.
43. Rossel, P. (2009). Weak signals asaflexibleframing space for enhanced management and decision-making. Technology Analysis &Strategic Management, 21(3), 291-305.
44. Salton, G., Allan, J., & Buckley, C. (1994). Automatic structuring and retrieval oflarge text files. Communications of the ACM, 37(2), 97-108.
45. Salton, G., & Buckley, C. (1988). Term-weighting approaches inautomatic text retrieval. Information Processing &Management, 24(5), 513-523.
46. Schwarz, J. O. (2005). Pitfalls inimplementing astrategic early warning system. Future Studies, 7(4), 22-31.
47. Shi, L., & Setchi, R. (2012). User-oriented ontology-based clustering ofstored memories. Expert Systems with Applications, 39(10), 9730-9730.
48. Sparck Jones, K. (1973). Index term weighting. Information Storage and Retrieval, 9(11), 619-633.
49. Sudhamathy, G., & Jothi Venkateswaran, C. (2012). Fuzzy temporal clustering approach for e-commerce websites. International Journal of Engineering and Technology, 4(3), 119-132.
50. Tabatabei, N. (2011). Detecting weak signals byinternet-based environmental scanning. Waterloo:Master Thesis, Waterloo University.
51. Teo, T. S., & Choo, W. Y. (2001). Assessing the impact ofusing Internet for competitive intelligence. Information & Management, 39(1), 67-83. (Pubitemid 32820894)
52. Thorleuchter, D., Van den Poel, D., & Prinzie, A. (2010d). Extracting consumers needs for new products -Aweb mining approach. In Proceedings WKDD 2010 (pp. 441). Los Alamitos:IEEE Computer Society.
53. Thorleuchter, D., Herberz, S., & Van den Poel, D. (2012). Mining social behavior ideas of przewalski horses. Lecture Notes in Electrical Engineering, 121, 649-656.
54. Thorleuchter, D. (2008). Finding technological ideas and inventions with text mining and technique philosophy. InC.
55. Preisach, H. Burkhardt, L. Schmidt- Thieme, &R. Decker (Eds.), Data analysis, machine learning, and applications (pp. 413-420). Berlin:Springer.
56. Thorleuchter, D., Schulze, J., & Van den Poel, D. (2012). Improved emergency management by loosely coupled logistic system. Communications in Computer and Information Science, 318, 5-8.
57. Thorleuchter, D., & Van den Poel, D. (2011a). Semantic technology classification-a defence and security case study. In Proceeding uncertainty reasoning and knowledge engineering (pp. 36-39). New York:IEE.
58. Thorleuchter, D., & Van den Poel, D. (2011b). Companies website optimising concerning consumer's searching for new products. In Proceeding uncertainty reasoning and knowledge engineering (pp. 40-43). New York:IEE.
59. Thorleuchter, D., & Van den Poel, D. (2012a). Extraction ofideas from microsystems technology. Advances in Intelligent and Soft Computing, 168, 563-568.
60. Thorleuchter, D., & Van den Poel, D. (2012b). Predicting e-commerce company success by mining the text ofits publicly-accessible website. Expert Systems with Applications, 39(17), 13026-13034.
61. Thorleuchter, D., & Van den Poel, D. (2012c). Using NMF for analyzing war logs. Communications in Computer and Information Science, 318, 73-76.
62. Thorleuchter, D., & Van den Poel, D. (2012d). Using webcrawling ofpublicly- available websites toassess e-commerce relationships. In SRII Global Conference 2012 (pp. 402-410). San Jose, CA, USA:IEE.
63. Thorleuchter, D., & Van den Poel, D. (2012e). Improved multilevel security with latent semantic indexing. Expert Systems with Applications, 39(18), 13462-13471.
64. Thorleuchter, D., & Van den Poel, D. (2013a). Technology classificationwith latent semantic indexing. Expert Systems with Applications, 40(5), 1786-1795.
65. Thorleuchter, D., & Van den Poel, D. (2013b). Protecting research and technology from espionage. Expert Systems with Applications, 40(9), 3432-3440.
66. Thorleuchter, D., & Van den Poel, D. (2013c). Web Mining based Extraction of Problem Solution Ideas. Expert Systems with Applications. http://dx.doi.org/10.1016/j.eswa.2013.01.013.
67. Thorleuchter, D., & Van den Poel, D. (2013d). Analyzing Website Content for Improved R&T Collaboration Planning. Advances in Intelligent Systems and Computing, 206, 567-573.
68. Thorleuchter, D., Van den Poel, D., & Prinzie, A. (2010a). Mining ideas from textual information. Expert Systems with Applications, 37(10), 7182-7188.
69. Thorleuchter, D., Van den Poel, D., & Prinzie, A. (2010b). Acompared R&D-based and patent-based cross impact analysis for identifying relationships between technologies. Technological Forecasting and Social Change, 77(7), 1037-1050.
70. Thorleuchter, D., Van den Poel, D., & Prinzie, A. (2012). Analyzing existing customers' websites toimprove the customer acquisition process aswell as the profitability prediction inB-to-B marketing. Expert Systems with Applications, 39(3), 2597-2605.
71. Thorleuchter, D., Van den Poel, D., & Prinzie, A. (2010c). Mining innovative ideas to support new product research and development. InH. Locarek-Junge &C. Weihs (Eds.), Classification asatool for research (pp. 587-594). Berlin:Springer.
72. Thorleuchter, D., & Van den Poel, D. (2011c). High granular multi-level-security model for improved usability. In System science, engineering design and manufacturing informatization 1 (pp. 191-194). New York:IEEE.
73. Thorleuchter, D., Weck, G., & Van den Poel, D. (2012a). Granular deleting inmulti level security models -anelectronic engineering approach. Lecture Notes in Electrical Engineering, 1(177), 609-614.
74. Tsai, H. H. (2012). Global data mining:Anempirical study ofcurrent trends, future forecasts and technology diffusions. Expert Systems with Applications, 39(9), 8172-8181.
75. Uskali, T. (2005). Paying attention toweak signals:The key concept for innovation journalism. Innovation Journalism, 2(11), 19.
76. Van den Poel, D., DeSchamphelaere, J., & Wets, G. (2004). Direct and indirect effects of retail promotions onsales and profitsinthe do-it-yourself market. Expert Systems with Applications, 27(1), 53-53.
77. Van Erkel, A. R., & Pattynama, P. M. T. (1998). Receiver operating characteristic (ROC) analysis:Basic principles and applications inradiology. European Journal of Radiology, 27(2), 88-94. (Pubitemid 28234366)
78. Yoon, J. (2012). Detecting weak signals for long-term business opportunities using text mining of Web news. Expert Systems with Applications, 39(16), 12543-12550.
79. Zeng, J., Duan, J., Cao, W., & Wu, C. (2012). Topics modeling based onselective Zipf distribution. Expert Systems with Applications, 39(7), 6541-6546.
80. Zhong, J., & Li, X. (2010). Unifiedcollaborative filteringmodel based oncombination of latent features. Expert Systems with Applications, 37(8), 5666-5672.
81. Zipf, G. K. (1949). Human behaviour and the principle ofleast effort. Cambridge: Addison-Wesley.