Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Data Science and Analytics
Published in: International Journal of Data Science and Analytics 2/2022

17-06-2022 | Editorial

Collective intelligence and knowledge exploration: an introduction

Authors: Salma Sassi, Mirjana Ivanovic, Richard Chbeir, Rajendra Prasath, Yannis Manolopoulos

Published in: International Journal of Data Science and Analytics | Issue 2/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Collective intelligence and Knowledge Exploration (CI and KE) have been adopted to solve many problems. They are particularly used by companies as a support for innovation to efficiently obtain usable results. CI is usually defined as a group ability to perform consistently well across a wide variety of tasks, and it has to be combined with KD to ensure processes optimization, efficient management process, participative management, leadership, continuous teamwork, and so on. The importance of innovation grows the same way as the importance of mixing CI and KE, ensuring the successful exploitation of knowledge. Here, we present a quick review of current knowledge-oriented CI developments and applications. It aims at showing some observations about what's currently missing. Our editorial presents some recent interesting studies that we have gathered after a tight selection process. It also concludes by proposing avenue challenges to continue pushing CI and KE research forward, particularly regarding knowledge exploration.
next article AutoML: state of the art with a focus on anomaly detection, challenges, and research directions

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Weschsler, D.: Concept of collective intelligence. Am. Psychol. 26(10), 904–907 (1971)CrossRef
2.
Wheeler, W.M.: The ant-colony as an organism. J. Morphol. 22(2), 307–325 (1991)CrossRef
3.
Lévy, P.: Collective Intelligence: Mankind’s Emerging World in Cyberspace. Plenum Press, London, UK (1997)
4.
Malone, T.W., Bernstein, M.S.: Handbook of Collective Intelligence. MIT Press, Cambridge (2015)
5.
Sulis W.: “Collective Intelligence: Observations and Models”, Chapter in Book “Chaos and Complexity in Psychology: The Theory of Nonlinear Dynamical Systems” by Guastello S., Koopmans M., Pincus D. (eds.), 41–72, Cambridge University Press, (2008)
6.
Williams, W.M., Sternberg, R.J.: Group intelligence: Why some groups are better than others. Intelligence 12(4), 351–377 (1988)CrossRef
7.
Surowiecki, J.: “The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations”, Doubleday, (2004)
8.
9.
Weick, K.E., Roberts, K.H.: Collective mind in organizations: heedful interrelating on flight decks. Adm. Sci. Q. 38(3), 357–381 (1993)CrossRef
10.
Sandelands, L.E., Stablein, R.E.: The concept of organization mind. Res. Sociol. Organ. 5, 135–161 (1987)
11.
Woolley, A.W., Chabris, C.F., Pentland, A., Hashmi, N., Malone, T.W.: Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004), 686–688 (2010)CrossRef
12.
Spearman, C.: General intelligence, objectively determined and measured. Am. J. Psychol. 15(2), 201–292 (1904)CrossRef
13.
Aggarwal, I., Woolley, A.W., Chabris, C.F., Malone, T.W.: The impact of cognitive style diversity on implicit learning in teams, Front. Psychol., 10, (2019)
14.
Engel, D., Woolley, A.W., Jing, L.X., Chabris, C.F., Malone, T.W.: Reading the mind in the eyes or reading between the lines? Theory of mind predicts Collective Intelligence equally well online and face-to-face. PLoS ONE 9, e115212 (2014)CrossRef
15.
Meslec N., Aggarwal I., Curseu P.L.: The insensitive ruins it all: Compositional and compilational influences of social sensitivity on collective intelligence in groups, Front. Psychol., 7, (2016)
16.
Credé, M., Howardson, G.: The structure of group task performance—a second look at Collective Intelligence: Comment on Woolley et al. (2010), J. Appl. Psychol., 102(10), 1483–1492 (2017)
17.
Barlow, J.B., Dennis, A.R.: Not as smart as we think: A study of collective intelligence in virtual groups. J. Manag. Inf. Syst. 33, 684–712 (2016)CrossRef
18.
Bates, T.C., Gupta, S.: Smart groups of smart people: evidence for IQ as the origin of collective intelligence in the performance of human groups. Intelligence 60, 46–56 (2017)CrossRef
19.
Malone, T.W., Klein, M.: Harnessing collective intelligence to address global climate change. Innov. Technol. Gov. Glob. 2(3), 15–26 (2007)
20.
Kapetanios, E.: Quo vadis computer science: from Turing to personal computer, personal content and collective intelligence. Data Knowl. Eng. 67(2), 286–292 (2008)MathSciNetCrossRef
21.
Šikýř M.: Human resource management best practices in managing knowledge workers, Theory Management, 2, 79–84, Žilina, (2010)
22.
Malone T.W.: “Superminds: The Surprising Power of People and Computers Thinking Together”, Little, Brown and Co, New York, NY, (2018)
23.
Cao, L., Zhang, C.: Domain-driven actionable knowledge discovery in the real world, Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 821–830, Singapore (2006)
24.
Iandolo, F., Loia, F., Fulco, I., Nespoli, C., Caputo, F.: Combining big data and artificial intelligence for managing collective knowledge in unpredictable environment—Insights from the Chinese case in facing COVID-19, J. Knowledge Econ., 1–15, (2020)
25.
Jiang, M.: Improving situational awareness with collective artificial intelligence over knowledge graphs. Proc. SPIE 11413, 114130J (2020)
26.
Namnual, T., Wannapiroon, P.: Development of Digital Repository system for Knowledge Management by Using Collective Intelligence and Big data for SMEs. Int. J. e-Educ. e-Bus. e-Manag. e-Learn. 10, 167–173 (2010)
27.
Smirnov, A.V., Shilov, N., Ponomarev, A.: Context-aware knowledge management for socio-cyber-physical systems: New trends towards human-machine collective intelligence, Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering & Knowledge Management (IC3K), 3, 5–17, (2020)
28.
Li, Z., Xu, Y., Li, K.: Networking and knowledge creation: social capital and collaborative innovation in responding to the COVID-19 crisis, J. Innov. Knowl., 7(2), (2022)
29.
Wang, G., Hu, Y., Tian, X., Geng, J., Hu, G., Zhang, M.: An integrated open approach to capturing systematic knowledge for manufacturing process innovation based on collective intelligence. Appl. Sci. 8(3), 340 (2018)CrossRef
30.
Nguyen, V.D., Tran, V.C., Truong, H.B., Nguyen, N.T.: Social networks as platforms for enhancing collective intelligence. Cybern. Syst. 53(5), 425–442 (2021)CrossRef
31.
Matzler, K., Strobl, A., Bailom, F.: Leadership and the wisdom of crowds: How to tap into the collective intelligence of an organization. Strategy & Leadersh. 44(1), 30–35 (2016)CrossRef
32.
Skarzauskiene, A., Maciuliene, M.: Modelling the index of collective intelligence in online community projects, Proceedings of the 10th International Conference on Cyber Warfare and Security (ICCWS), Kruger National Park, South Africa, (2015)
33.
Salminen J.: The role of collective intelligence in crowdsourcing innovation, PhD Dissertation, Lappeenranta University of Technology, (2015)
34.
Georgi, S., Jung, R.: Collective intelligence model: How to describe collective intelligence”, Proceedings of the 2nd Symposium on Collective Intelligence (COLLIN), 53–64, Jeju Island, South Korea, (2012)
35.
Lykourentzou, I., Vergados, D., Kapetanios, E., Loumos, V.: Collective Intelligence Systems: Classification and Modeling, J. Emerg. Technol. Web Intell., 3(3), (2011)
36.
Gregg, D.: Designing for collective intelligence. Commun. ACM 53(4), 134–138 (2010)CrossRef
37.
Schut, M.: On model design for simulation of collective intelligence. Inf. Sci. 180(1), 132–155 (2010)CrossRef
38.
Vergados, D., Lykourentzou, I., Kapetanios, E.: A resource allocation framework for collective intelligence System Engineering, Proceedings of the International Conference on Management of Emergent Digital EcoSystems (MEDES), 182–188. Bangkok, Thailand, (2010)
39.
Malone, T., Laubacher, R., Dellarocas, C.: Harnessing crowds: Mapping the genome of collective intelligence, MIT Sloan Research Paper, 4732 (2009)
40.
Iandoli, L.: Leveraging the power of collective intelligence through IT-enabled global collaboration. J. Glob. Inf. Technol. Manag. 12(3), 1–6 (2009)
41.
Boder, A.: Collective intelligence: a keystone in knowledge management. J. Knowl. Manag. 10(1), 81–93 (2006)CrossRef
42.
Bonabeau, E.: Decisions 2.0: the power of collective intelligence. MIT Sloan Manag. Rev. 50(2), 45–52 (2009)
43.
Surowiecki, J.: The Wisdom of Crowds. Anchor Books, NY (2005)
44.
Nguyen, V.D., Nguyen, N.T.: Intelligent collectives: theory, applications, and research challenges. Cybern. Syst. 49(5–6), 261–279 (2018)CrossRef
45.
Anderson, L., Holt, C.: Information cascades in the laboratory. Am. Econ. Rev. 87(5), 847–862 (1997)
46.
O’Reilly T.: What is Web 2.0: Design patterns and business models for the next generation of software, Int. J. Digital Econ., 65(17–37), (2007)
47.
Watkins J.: Prediction markets as an aggregation mechanism for collective intelligence, Proceedings of the Lake Arrowhead Conference, escholarship.org, Open Access Publications from the University of California, (2007)
48.
Zettsu, K., Kiyoki, Y.: Towards knowledge management based on harnessing collective intelligence on the Web, Proceedings of the International Conference on Knowledge Engineering and Knowledge Management (EKAW), 350–357, Podebrady, Czech Republic, (2006)
49.
Luo, S., Xia, H., Yoshida, T., Wang, Z.: Toward collective intelligence of online communities: a primitive conceptual model, J. Syst. Sci. Syst. Eng., 18(2), (2009)
50.
Lykourentzou, I., Vergados, D., Loumos, V.: Collective intelligence system engineering, Proceedings of the International Conference on Management of Emergent Digital EcoSystems (MEDES), 131–140, Lyon, France, (2009)
51.
Easterday, M., Gerber, E., Rees, L.D.: Social innovation networks: a new approach to social design education and impact. Des. Issues 34(2), 64–76 (2018)CrossRef
52.
Glenn, J.: Collective intelligence and an application by the millennium project. World Futur. Rev. 5(3), 235–243 (2013)CrossRef
53.
Grasso, A., Convertino, G.: Collective intelligence in organizations: tools and studies. Comput. Support. Coop. Work 21(4–5), 357–369 (2012)CrossRef
54.
Schoder, D., Gloor, P., Metaxas, P.T.: Social media and collective intelligence—ongoing and future research streams. Künstl. Intell. 27(1), 9–15 (2013)CrossRef
55.
Woodley, M., Bell, E.: Is collective intelligence (mostly) the general factor of personality? A comment on Woolley, Chabris, Pentland, Hashmi and Malone (2010), Intelligence, 39(2), 79–81, (2011)
56.
Levy, P.: Collective Intelligence: Mankind’s Emerging World in Cyberspace. Perseus Books, Cambridge, MA (1997)
57.
Valentine, M., Retelny, D., To, A., Rahmati, N., Doshi, T., Bernstein, M.: Flash organizations: Crowdsourcing complex work by structuring crowds as organizations, Proceedings of the Conference on Human Factors in Computing Systems (CHI), 3523–3537, Denver, CO, (2017)
58.
Dow, S., Kulkarni, A., Klemmer, S., Hartmann, B.: Shepherding the crowd yields better work, Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW), 1013–1022, Seattle, WA, (2012)
59.
Pan D.: A formal framework for data mining process model, Proceedings of the Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA), 126–129, Wuhan, China, (2009)
60.
Crespo, M.A.: Modeling critical failures maintenance: a case study for mining. J. Qual. Maint. Eng. 11(4), 301–317 (2005)CrossRef
61.
Chen, C.Y., Chou, T.Y., Mu, C.Y., Lee, B.J., Chandramouli, M., Chao, H.: Using Data Mining Techniques on Fleet Management System, Proceedings of the ESRI International User Conference, (2018)
62.
Luo, D., Cao, L., Luo, C., Zhang, C., Wang, W.: Towards business interestingness in actionable knowledge discovery, Proceedings of the Conference on Applications of Data Mining in E-Business and Finance, 99–109, Nanjing, China (2008)
63.
Cao, L., Zhang, C.: The evolution of KDD: towards domain-driven data mining. Int. J. Pattern Recognit Artif Intell. 21(4), 677–692 (2007)CrossRef
64.
Cao, L., Yu, P.S., Zhang, C., Zhao, Y.: Domain Driven Data Mining. Springer, Berlin (2010)CrossRef
Metadata
Title
Collective intelligence and knowledge exploration: an introduction
Authors
Salma Sassi
Mirjana Ivanovic
Richard Chbeir
Rajendra Prasath
Yannis Manolopoulos
Publication date
17-06-2022
Publisher
Springer International Publishing
Published in
International Journal of Data Science and Analytics / Issue 2/2022
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-022-00338-9

Other articles of this Issue 2/2022

International Journal of Data Science and Analytics 2/2022 Go to the issue

Regular Paper

Accurate and efficient privacy-preserving string matching

Review

AutoML: state of the art with a focus on anomaly detection, challenges, and research directions

Regular Paper

Video emotion analysis enhanced by recognizing emotion in video comments

Regular Paper

Comparative analysis of different crossover structures for solving a periodic inventory routing problem

Regular Paper

Adaptive k-center and diameter estimation in sliding windows

Regular Paper

Telugu named entity recognition using bert

Premium Partner

    Image Credits