Abstract
Due to the ever-growing amount of data, computer-aided methods and systems to detect weak signals and trends for corporate foresight are in increasing demand. To this day, many papers on this topic have been published. However, research so far has only dealt with specific aspects, but it has failed to provide a comprehensive overview of the research domain. In this paper, we conduct a systematic literature review to organize existing insights and knowledge. The 91 relevant papers, published between 1997 and 2017, are analyzed for their distribution over time and research outlets. Classifying them by their distinct properties, we study the data sources exploited and the data mining techniques applied. We also consider eight different purposes of analysis, namely weak signals and trends concerning political, economic, social and technological factors. The results of our systematic review show that the research domain has indeed been attracting growing attention over time. Furthermore, we observe a great variety of data mining and visualization techniques, and present insights on the efficacy and effectiveness of the data mining techniques applied. Our results reveal that a stronger emphasis on search strategies, data quality and automation is required to greatly reduce the human actor bias in the early stages of the corporate foresight process, thus supporting human experts more effectively in later stages such as strategic decision making and implementation. Moreover, systems for detecting weak signals and trends need to be able to learn and accumulate knowledge over time, attaining a holistic view on weak signals and trends, and incorporating multiple source types to provide a solid foundation for strategic decision making. The findings presented in this paper point to future research opportunities, and they can help practitioners decide which sources to exploit and which data mining techniques to apply when trying to detect weak signals and trends.
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References
Abbas A, Zhang L, Khan SU (2014) A literature review on the state-of-the-art in patent analysis. World Patent Inf 37:3–13. https://doi.org/10.1016/j.wpi.2013.12.006
Abe H, Tsumoto S (2010) Trend detection from large text data. In: IEEE international conference on systems man and cybernetics (SMC) pp 310–315. https://doi.org/10.1109/ICSMC.2010.5641682
Adedoyin-Olowe M, Gaber MM, Stahl F (2013) Trcm: a methodology for temporal analysis of evolving concepts in twitter. In: International conference on artificial intelligence and soft computing, Springer, Berlin Heidelberg, pp 135–145. https://doi.org/10.1007/978-3-642-38610-7_13
Aiello LM, Petkos G, Martin C, Corney D, Papadopoulos S, Skraba R, Goker A, Kompatsiaris I, Jaimes A (2013) Sensing trending topics in twitter. IEEE Trans Multimed 15(6):1268–1282. https://doi.org/10.1109/TMM.2013.2265080
Al-Azmi AAR (2013) Data, text and web mining for business intelligence: a survey. Int J Data Min Knowl Manag Process 3(2):1–21. https://doi.org/10.5121/ijdkp.2013.3201
Ansoff HI (1975) Managing strategic surprise by response to weak signals. Calif Manag Rev 18(2):21–33
Bao BK, Xu C, Min W, Hossain MS (2015) Cross-platform emerging topic detection and elaboration from multimedia streams. ACM Trans Multimed Comput Commun Appl 11(4):1–21. https://doi.org/10.1145/2730889
Barirani A, Agard B, Beaudry C (2013) Discovering and assessing fields of expertise in nanomedicine: a patent co-citation network perspective. Scientometrics 94(3):1111–1136. https://doi.org/10.1007/s11192-012-0891-6
Bello-Orgaz G, Menendez H, Okazaki S, Camacho D (2014) Combining social-based data mining techniques to extract collective trends from twitter. Malays J Comput Sci 27(2):95–111
Bernard HR (2006) Social research methods: qualitative and quantitative approaches, reprinted edn. SAGE, Thousand Oaks, Calif
Blomqvist E (2014) The use of semantic web technologies for decision support—a survey. Semant Web 5(3):177–201. https://doi.org/10.3233/SW-2012-0084
Bolelli L, Ertekin Ş, Giles CL (2009) Topic and trend detection in text collections using latent Dirichlet allocation. In: Proceedings of the 31th European conference on IR research on advances in information retrieval (ECIR ’09). https://doi.org/10.1007/978-3-642-00958-7_4
Bun KK, Ishizuka M (2006) Emerging topic tracking system in www. Knowl Based Syst 19(3):164–171. https://doi.org/10.1016/j.knosys.2005.11.008
Carr LP, Nanni AJ (2009) Delivering results: managing what matters. Springer, New York
Cataldi M, Di Caro L, Schifanella C (2013) Personalized emerging topic detection based on a term aging model. ACM Trans Intell Syst Technol (TIST) 5(1):1–27. https://doi.org/10.1145/2542182.2542189
Caviggioli F (2016) Technology fusion: identification and analysis of the drivers of technology convergence using patent data. Technovation 55–56:22–32. https://doi.org/10.1016/j.technovation.2016.04.003
Chang PL, Wu CC, Leu HJ (2010) Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display. Scientometrics 82(1):5–19. https://doi.org/10.1007/s11192-009-0033-y
Chen C (2006) Citespace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inform Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317
Chen H, Zhang G, Zhu D, Lu J (2015) A patent time series processing component for technology intelligence by trend identification functionality. Neural Comput Appl 26(2):345–353. https://doi.org/10.1007/s00521-014-1616-y
Cheng Q, Lu X, Liu Z, Huang J (2015) Mining research trends with anomaly detection models: the case of social computing research. Scientometrics 103(2):453–469. https://doi.org/10.1007/s11192-015-1559-9
Chi Y, Tseng BL, Tatemura J (2006) Eigen-trend: Trend analysis in the blogosphere based on singular value decompositions. In: Proceedings of the 15th ACM international conference on information and knowledge management, New York, pp 68–77
Choi S, Yoon J, Kim K, Lee JY, Kim CH (2011) Sao network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells. Scientometrics 88(3):863–883. https://doi.org/10.1007/s11192-011-0420-z
Curran CS, Leker J (2011) Patent indicators for monitoring convergence—examples from nff and ict. Technol Forecast Soc Change 78(2):256–273. https://doi.org/10.1016/j.techfore.2010.06.021
Dai XY, Chen QC, Wang XL, Xu J (2010) Online topic detection and tracking of financial news based on hierarchical clustering. In: 2010 International conference on machine learning and cybernetics (ICMLC), pp 3341–3346. https://doi.org/10.1109/ICMLC.2010.5580677
Dueñas-Fernández R, Velásquez JD, L’Huillier G (2014) Detecting trends on the web: a multidisciplinary approach. Inf Fusion 20:129–135. https://doi.org/10.1016/j.inffus.2014.01.006
Eckhoff R, Markus M, Lassnig M, Schön S (2014) Detecting weak signals with technologies: overview of current technology-enhanced approaches for the detection of weak signals. Int J Trends Econ Manag Technol (IJTEMT) USA 5(5):1–7
Ena O, Mikova N, Saritas O, Sokolova A (2016) A methodology for technology trend monitoring: the case of semantic technologies. Scientometrics 108(3):1013–1041. https://doi.org/10.1007/s11192-016-2024-0
Fan TK, Chang CH (2008) Exploring evolutionary technical trends from academic research papers. In: Kise K (ed) The eighth IAPR international workshop on document analysis systems, 2008, IEEE, Piscataway, NJ, pp 574–581. https://doi.org/10.1109/DAS.2008.25
Fang Y, Zhang H, Ye Y, Li X (2014) Detecting hot topics from twitter: a multiview approach. J Inf Sci 40(5):578–593. https://doi.org/10.1177/0165551514541614
Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–54
Ferber R (2003) Information Retrieval: suchmodelle und data-mining-Verfahren für Textsammlungen und das Web, 1st edn. Dpunkt-Verl, Heidelberg
Gaul W, Vincent D (2017) Evaluation of the evolution of relationships between topics over time. Adv Data Anal Classif 11(1):159–178. https://doi.org/10.1007/s11634-016-0241-2
Gayle HM, Blake BM (1980) Coal in West Virginia: geology and current mining trends. AAPG Bull 64(8):1287–1288
Gerken JM, Moehrle MG (2012) A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis. Scientometrics 91(3):645–670. https://doi.org/10.1007/s11192-012-0635-7
Geum Y, Jeon J, Seol H (2013) Identifying technological opportunities using the novelty detection technique: a case of laser technology in semiconductor manufacturing. Technol Anal Strat Manag 25(1):1–22. https://doi.org/10.1080/09537325.2012.748892
Glänzel W, Thijs B (2012) Using ’core documents’ for detecting and labelling new emerging topics. Scientometrics 91(2):399–416. https://doi.org/10.1007/s11192-011-0591-7
González-Alcaide G, Llorente P, Ramos JM (2016) Bibliometric indicators to identify emerging research fields: publications on mass gatherings. Scientometrics 109(2):1283–1298. https://doi.org/10.1007/s11192-016-2083-2
Goorha S, Ungar L (2010) Discovery of significant emerging trends. In: Proceedings of the 16th ACM international conference on knowledge discovery and data mining, pp 57–64. https://doi.org/10.1145/1835804.1835815
Grandjean N, Charpiot B, Pena CA, Peitsch MC (2005) Competitive intelligence and patent analysis in drug discovery: mining the competitive knowledge bases and patents. Drug Discov Today Technol 2(3):211–215. https://doi.org/10.1016/j.ddtec.2005.08.007
Guo H, Weingart S, Börner K (2011) Mixed-indicators model for identifying emerging research areas. Scientometrics 89(1):421–435. https://doi.org/10.1007/s11192-011-0433-7
Hennig P, Berger P, Meinel C (2013) Identify emergent trends based on the blogosphere. In: 2013 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), p 3. https://doi.org/10.1109/WI-IAT.2013.147
Hiltunen E (2008) The future sign and its three dimensions. Futures 40(3):247–260. https://doi.org/10.1016/j.futures.2007.08.021
Huang MH, Chang CP (2014) Detecting research fronts in oled field using bibliographic coupling with sliding window. Scientometrics 98(3):1721–1744. https://doi.org/10.1007/s11192-013-1126-1
Huang Y, Zhang Y, Ma J, Porter A, Wang X (2015) Tracing technology evolution pathways by combining tech mining and patent citation analysis. In: 2015 Portland international conference on management of engineering and technology
Huang J, Peng M, Wang H, Cao J, Gao W, Zhang X (2017) A probabilistic method for emerging topic tracking in microblog stream. World Wide Web 20(2):325–350. https://doi.org/10.1007/s11280-016-0390-4
Imran H, Sharan A (2010) A framework for automatic query expansion. In: International conference on web information systems and mining. Springer, Berlin Heidelberg, pp 386–393. https://doi.org/10.1007/978-3-642-16515-3_48
Jeong Y, Yoon B (2015) Development of patent roadmap based on technology roadmap by analyzing patterns of patent development. Technovation 39–40:37–52. https://doi.org/10.1016/j.technovation.2014.03.001
Jun S, Park SS, Jang DS (2012a) Patent management for technology forecasting: a case study of the bio-industry. J Intell Prop Rights 17(6):539–546
Jun S, Sung Park S, Sik Jang D (2012b) Technology forecasting using matrix map and patent clustering. Ind Manag Data Syst 112(5):786–807. https://doi.org/10.1108/02635571211232352
Kämpf M, Tessenow E, Kenett DY, Kantelhardt JW (2015) The detection of emerging trends using Wikipedia traffic data and context networks. PLOS One 10(12):e0141892. https://doi.org/10.1371/journal.pone.0141892
Keller J, von der Gracht HA (2014) The influence of information and communication technology (ICT) on future foresight processes: results from a Delphi survey. Technol Forecast Soc Change 85(C):81–92. https://doi.org/10.1016/j.techfore.2013.07.010
Kim B, Gazzola G, Lee JM, Kim D, Kim K, Jeong MK (2014) Inter-cluster connectivity analysis for technology opportunity discovery. Scientometrics 98(3):1811–1825. https://doi.org/10.1007/s11192-013-1097-2
Kim D, Kim D, Hwang E, Rho S (2015a) TwitterTrends: a spatio-temporal trend detection and related keywords recommendation scheme. Multimed Syst 21(1):73–86. https://doi.org/10.1007/s00530-013-0342-0
Kim J, Hwang M, Jeong DH, Jung H (2012) Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Syst Appl 39(16):12618–12625. https://doi.org/10.1016/j.eswa.2012.05.021
Kim N, Lee H, Kim W, Lee H, Suh JH (2015b) Dynamic patterns of industry convergence: evidence from a large amount of unstructured data. Res Policy 44(9):1734–1748. https://doi.org/10.1016/j.respol.2015.02.001
Kim S, Kim YE, Bae KJ, Choi SB, Park JK, Koo YD, Park YW, Choi HK, Kang HM, Hong SW (2013) Nest: a quantitative model for detecting emerging trends using a global monitoring expert network and Bayesian network. Futures 52:59–73. https://doi.org/10.1016/j.futures.2013.08.004
Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering: Technical report ebse 2007-001. Keele university and Durham University joint report
Kontostathis A, Galitsky LM, Pottenger WM, Roy S, Phelps DJ (2004) A survey of emerging trend detection in textual data mining. https://doi.org/10.1007/978-1-4757-4305-0_9
Kuosa T (2010) Futures signals sense-making framework (fssf): a start-up tool to analyse and categorise weak signals, wild cards, drivers, trends and other types of information. Futures 42(1):42–48. https://doi.org/10.1016/j.futures.2009.08.003
Lau JY (2012) On-line trend analysis with topic models. In: Proceedings of the 24th international conference on computational linguistics (24)
Lee C, Jeon J, Park Y (2011) Monitoring trends of technological changes based on the dynamic patent lattice: a modified formal concept analysis approach. Technol Forecast Soc Change 78(4):690–702. https://doi.org/10.1016/j.techfore.2010.11.010
Lee JY, Kim H, Kim PJ (2010) Domain analysis with text mining: analysis of digital library research trends using profiling methods. J Inf Sci 36(2):144–161. https://doi.org/10.1177/0165551509353251
Lee S, Yoon B, Park Y (2009) An approach to discovering new technology opportunities: keyword-based patent map approach. Technovation 29(6–7):481–497. https://doi.org/10.1016/j.technovation.2008.10.006
Lee WH (2008) How to identify emerging research fields using scientometrics: an example in the field of information security. Scientometrics 76(3):503–525. https://doi.org/10.1007/s11192-007-1898-2
Lee Y, Kim SY, Song I, Park Y, Shin J (2014) Technology opportunity identification customized to the technological capability of smes through two-stage patent analysis. Scientometrics 100(1):227–244. https://doi.org/10.1007/s11192-013-1216-0
Lent B, Agrawal R, Srikant R (1997) Discovering trends in text databases. In: Proceedings of the third international conference on knowledge discovery and data mining, pp 227–230
Liu DR, Shih MJ, Liau CJ, Lai CH (2009) Mining the change of event trends for decision support in environmental scanning. Expert Syst Appl 36(2):972–984. https://doi.org/10.1016/j.eswa.2007.10.016
Lu Y, Zhang P, Liu J, Li J, Deng S (2013) Health-related hot topic detection in online communities using text clustering. Plos One 8(2):56221. https://doi.org/10.1371/journal.pone.0056221
Luo J, Pan X, Zhu X (2015) Identifying digital traces for business marketing through topic probabilistic model. Technol Anal Strat Manag 27(10):1176–1192. https://doi.org/10.1080/09537325.2015.1061118
Ma J, Porter AL (2015) Analyzing patent topical information to identify technology pathways and potential opportunities. Scientometrics 102(1):811–827. https://doi.org/10.1007/s11192-014-1392-6
Madani F (2015) ’technology mining’ bibliometrics analysis: applying network analysis and cluster analysis. Scientometrics 105(1):323–335. https://doi.org/10.1007/s11192-015-1685-4
Mayer JH, Steinecke N, Quick R (2011) Improving the applicability of environmental scanning systems: state of the art and future research. In: Nüttgens M (ed) IFIP Advances in information and communication technology governance and sustainability in information systems, vol 366. Springer, Berlin, pp 207–223. https://doi.org/10.1007/978-3-642-24148-2_13
Mei Q, Zhai C (2005) Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: Grossman R, Bayardo R, Bennett K (eds) Proceeding of the eleventh ACM SIGKDD international conference, pp 198–207. https://doi.org/10.1145/1081870.1081895
Milanez DH, De Faria LIL, Do Amaral RM, Leiva DR, Gregolin JAR (2014) Patents in nanotechnology: an analysis using macro-indicators and forecasting curves. Scientometrics 101(2):1097–1112. https://doi.org/10.1007/s11192-014-1244-4
Moreira ALM, Hayashi TWN, Coelho GP, da Silva Ana, Antunes Estela (2015) A clustering method for weak signals to support anticipative intelligence. Int J Artif Intell Expert Syst(IJAE) 6(1):
Mryglod O, Holovatch Y, Kenna R, Berche B (2016) Quantifying the evolution of a scientific topic: reaction of the academic community to the chornobyl disaster. Scientometrics 106(3):1151–1166. https://doi.org/10.1007/s11192-015-1820-2
Murtaza SS, Khreich W, Hamou-Lhadj A, Bener AB (2016) Mining trends and patterns of software vulnerabilities. J Syst Softw 117:218–228. https://doi.org/10.1016/j.jss.2016.02.048
Nasraoui O, Rojas C, Cardona C (2006) A framework for mining evolving trends in web data streams using dynamic learning and retrospective validation. Comput Netw 50(10):1488–1512. https://doi.org/10.1016/j.comnet.2005.10.021
Nguyen KL, Byung-Joo Shin, Seong Joon Yoo (2016) Hot topic detection and technology trend tracking for patents utilizing term frequency and proportional document frequency and semantic information. In: 2016 international conference on big data and smart computing (BigComp), pp 223–230. https://doi.org/10.1109/BIGCOMP.2016.7425917
Noh H, Song YK, Lee S (2016) Identifying emerging core technologies for the future: case study of patents published by leading telecommunication organizations. Telecommun Policy 40(10–11):956–970. https://doi.org/10.1016/j.telpol.2016.04.003
Nohuddin PNE, Sunayama W, Christley R, Coenen F, Setzkorn C (2014) Trend mining in social networks: from trend identification to visualization. Expert Syst 31(5):457–468. https://doi.org/10.1111/exsy.12024
Palomino MA, Vincenti A, Owen R (2013) Optimising web-based information retrieval methods for horizon scanning. Foresight 15(3):159–176. https://doi.org/10.1108/fs-10-2011-0045
Park H, Kim E, Bae KJ, Hahn H, Sung TE, Kwon HC (2011) Detection and analysis of trend topics for global scientific literature using feature selection based on gini-index. 2011 IEEE international conference on tools with artificial intelligence. pp 965–969. https://doi.org/10.1109/ICTAI.2011.166
Park S, Kim J, Lee H, Jang D, Jun S (2016) Methodology of technological evolution for three-dimensional printing. Ind Manag Data Syst 116(1):122–146. https://doi.org/10.1108/IMDS-05-2015-0206
Parker J, Wei Y, Yates A, Frieder O, Goharian N (2013) A framework for detecting public health trends with twitter. In: Rokne J, Faloutsos C (eds) Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2013), pp 556–563. https://doi.org/10.1145/2492517.2492544
Pinto JCL, Chahed T, Altman E (2015) Trend detection in social networks using Hawkes processes. In: Pei J, Silvestri F, Tang J (eds) The 2015 IEEE/ACM international conference, pp 1441–1448. https://doi.org/10.1145/2808797.2814178
Porter AL, Youtie J, Shapira P, Schoeneck DJ (2008) Refining search terms for nanotechnology. J Nanopart Res 10(5):715–728. https://doi.org/10.1007/s11051-007-9266-y
Preschitschek N, Niemann H, Lenker J, Moehrle MG (2013) Anticipating industry convergence: semantic analyses vs ipc co-classification analyses of patents. Foresight 15(6):446–464. https://doi.org/10.1108/FS-10-2012-0075
Rill S, Reinel D, Scheidt J, Zicari RV (2014) Politwi: early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowl Based Syst 69:24–33. https://doi.org/10.1016/j.knosys.2014.05.008
Robertson S (2004) Understanding inverse document frequency: on theoretical arguments for IDF. J Doc 60(5):503–520. https://doi.org/10.1108/00220410410560582
Rodriguez A, Tosyali A, Kim B, Choi J, Lee JM, Coh BY, Jeong MK (2016) Patent clustering and outlier ranking methodologies for attributed patent citation networks for technology opportunity discovery. IEEE Trans Eng Manage 63(4):426–437. https://doi.org/10.1109/TEM.2016.2580619
Rohrbeck R, Bade M (2012) Environmental scanning, futures research, strategic foresight and organizational future orientation: a review, integration, and future research directions: Ispim annual conference, Barcelona, spain
Rohrbeck R, Thom N, Arnold HM (2015) It tools for foresight: the integrated insight and response system of Deutsche telekom innovation laboratories. Technol Forecast Soc Chang 97(8):115–126
Santo Md, Coelho GM, dos Santos DM, Filho LF (2006) Text mining as a valuable tool in foresight exercises: a study on nanotechnology. Technol Forecast Soc Change 73(8):1013–1027. https://doi.org/10.1016/j.techfore.2006.05.020
Saritas O, Smith JE (2011) The big picture—trends, drivers, wild cards, discontinuities and weak signals. Futures 43(3):292–312. https://doi.org/10.1016/j.futures.2010.11.007
Schult R, Spiliopoulou M (2006) Discovering emerging topics in unlabelled text collections. In: Manolopoulos Y (ed) Advances in databases and information systems, Lecture Notes in Computer Science, vol 4152, Springer, Berlin [u.a.], pp 353–366. https://doi.org/10.1007/11827252_27
Schwarz JO (2005) Pitfalls in implementing a strategic early warning system. Foresight 7(4):22–30. https://doi.org/10.1108/14636680510611813
Shibata N, Kajikawa Y, Takeda Y, Sakata I, Matsushima K (2011) Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technol Forecast Soc Change 78(2):274–282. https://doi.org/10.1016/j.techfore.2010.07.006
Shih MJ, Liu DR, Hsu ML (2010) Discovering competitive intelligence by mining changes in patent trends. Expert Syst Appl 37(4):2882–2890. https://doi.org/10.1016/j.eswa.2009.09.001
Song M, Kim MC, Jeong YK (2014) Analyzing the political landscape of 2012 Korean presidential election in twitter. IEEE Intell Syst 29(2):18–26. https://doi.org/10.1109/MIS.2014.20
Steinecke NC, Quick R, Mohr T (2011) Environmental scanning systems: state of the art and first instantiation. PACIS 2011 Proceedings
Takahashi T, Tomioka R, Yamanishi K (2014) Discovering emerging topics in social streams via link-anomaly detection. IEEE Trans Knowl Data Eng 26(1):120–130. https://doi.org/10.1109/TKDE.2012.239
Tho QT, Hui SC, Fong A (2003) Web mining for identifying research trends. In: International conference on Asian digital libraries, pp 290–301. https://doi.org/10.1007/978-3-540-24594-0_28
Thorleuchter D, van den Poel D (2013) Weak signal identification with semantic web mining. Expert Syst Appl 40(12):4978–4985. https://doi.org/10.1016/j.eswa.2013.03.002
Thorleuchter D, Scheja T, van den Poel D (2014) Semantic weak signal tracing. Expert Syst Appl 41(11):5009–5016. https://doi.org/10.1016/j.eswa.2014.02.046
Trappey CV, Wu HY, Taghaboni-Dutta F, Trappey AJ (2011) Using patent data for technology forecasting: China rfid patent analysis. Adv Eng Inform 25(1):53–64. https://doi.org/10.1016/j.aei.2010.05.007
Tu YN, Hsu SL (2016) Constructing conceptual trajectory maps to trace the development of research fields. J Assoc Inf Sci Technol 67(8):2016–2031. https://doi.org/10.1002/asi.23522
Tu YN, Seng JL (2012) Indices of novelty for emerging topic detection. Inf Process Manag 48(2):303–325. https://doi.org/10.1016/j.ipm.2011.07.006
Veugelers M, Bury J, Viaene S (2010) Linking technology intelligence to open innovation. Technol Forecast Soc Change 77(2):335–343. https://doi.org/10.1016/j.techfore.2009.09.003
Vidhya KA, Aghila G (2010) Text mining process, techniques and tools: an overview. Int J Inf Technol Knowl Manag 2:613–622
vom Brocke J, Simons A, Riemer K, Niehaves B, Plattfaut R, Cleven A (2015) Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun Assoc Inf Syst 37(1):9
von der Gracht HA, Vennemann CR, Darkow IL (2010) Corporate foresight and innovation management: a portfolio-approach in evaluating organizational development. Learn Future Faster 42(4):380–393. https://doi.org/10.1016/j.futures.2009.11.023
Wang J, Li L, Tan F, Zhu Y, Feng W (2015a) Detecting hotspot information using multi-attribute based topic model. PLOS ONE 10(10):e0140539. https://doi.org/10.1371/journal.pone.0140539
Wang MY, Chang DS, Kao CH (2010) Identifying technology trends for R&D planning using triz and text mining. R&D Manag 40(5):491–509. https://doi.org/10.1111/j.1467-9310.2010.00612.x
Wang X, McCallum A (2006) Topics over time: a non-markov continuous-time model of topical trends. In: 2006 Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. https://doi.org/10.1145/1150402.1150450
Wang X, Cheng Q, Lu W (2014) Analyzing evolution of research topics with neviewer: a new method based on dynamic co-word networks. Scientometrics 101(2):1253–1271. https://doi.org/10.1007/s11192-014-1347-y
Wang X, Qiu P, Zhu D, Mitkova L, Lei M, Porter AL (2015b) Identification of technology development trends based on subject-action-object analysis: the case of dye-sensitized solar cells. Technol Forecast Soc Change 98:24–46. https://doi.org/10.1016/j.techfore.2015.05.014
Wanner F, Stoffel A, Jäckle D, Kwon BC, Weiler A, Keim DA (2014) State-of-the-art report of visual analysis for event detection in text data streams. The Eurographics Association
Weenen TC, Ramezanpour B, Pronker ES, Commandeur H, Claassen E (2013) Food-pharma convergence in medical nutrition—best of both worlds? PLOS ONE 8(12):e82609. https://doi.org/10.1371/journal.pone.0082609
Wetzker R, Zimmermann C, Bauckhage C (2010) Detecting trends in social bookmarking systems. Int J Data Warehouse Min 6(1):38–57. https://doi.org/10.4018/jdwm.2010090803
Woon WL, Madnick S (2012) Semantic distances for technology landscape visualization. J Intell Inf Syst 39(1):29–58. https://doi.org/10.1007/s10844-011-0182-3
Woon WL, Henschel A, Madnick S (2009) A framework for technology forecasting and visualization. In: International conference on innovations in information technology (IIT), pp 115–159. https://doi.org/10.1109/IIT.2009.5413768
Wu FS, Shiu CC, Lee PC, Su HN (2010) Integrated methodologies for mapping and forecasting science and technology trends: a case of etching technology. 2010 Technology Management for Global Economic Growth (PICMET) pp 1–23
Xie W, Zhu F, Jiang J, Lim EP, Wang K (2016) Topicsketch: real-time bursty topic detection from twitter. IEEE Trans Knowl Data Eng 28(8):2216–2229. https://doi.org/10.1109/TKDE.2016.2556661
Yang L, Lin H, Lin Y, Liu S (2016) Detection and extraction of hot topics on chinese microblogs. Cognit Comput 8(4):577–586. https://doi.org/10.1007/s12559-015-9380-6
Yoon B, Park Y (2007) Development of new technology forecasting algorithm: hybrid approach for morphology analysis and conjoint analysis of patent information. IEEE Trans Eng Manag 54(3):588–599. https://doi.org/10.1109/TEM.2007.900796
Yoon J, Kim K (2012) Detecting signals of new technological opportunities using semantic patent analysis and outlier detection. Scientometrics 90(2):445–461. https://doi.org/10.1007/s11192-011-0543-2
Acknowledgements
This study was sponsored by the German Federal Ministry of Education and Research, grant number 02K16C191.
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Appendix
Appendix
To make the tables in this appendix more readable (Tables 5, 6, 7), we will use the following abbreviations for the categories introduced in Sect. 3.3 wherever appropriate:
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1.
Research discipline (RSD)
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(a)
Weak signal detection (WSD)
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(b)
Emerging trend detection (ETD)
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(a)
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2.
Data mining approach (DMA)
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(a)
Text mining (TM)
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(b)
Bibliometric analysis (BA)
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(c)
Joint analysis (JA)
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(a)
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3.
Data mining task (DMT), grouping the Data mining methods (DMM)
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(a)
Change and deviation detection (CDD)
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(b)
Clustering (CLU)
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(c)
Classification (CLA)
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(d)
Dependency modeling (DEM)
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(e)
Regression (REG)
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(f)
Summarization (SUM)
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(a)
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4.
Data mining process (DMP)
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(a)
Data collection (DAC)
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(b)
Data cleaning and pre-processing (DCP)
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(c)
Data projection and transformation (DPT)
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(d)
Data mining (DAM)
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(e)
Data visualization (DAV)
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(a)
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Mühlroth, C., Grottke, M. A systematic literature review of mining weak signals and trends for corporate foresight. J Bus Econ 88, 643–687 (2018). https://doi.org/10.1007/s11573-018-0898-4
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DOI: https://doi.org/10.1007/s11573-018-0898-4
Keywords
- Machine learning
- Weak signal detection
- Emerging trend detection
- Corporate foresight
- Environmental scanning
- Strategic decision making
- Big data