Frameworks for Collective Intelligence
| Autor: | Shweta Suran, Vishwajeet Pattanaik, Dirk Draheim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Web 2.0 Computer science business.industry 05 social sciences Collective intelligence 02 engineering and technology Crowdsourcing Data science Theoretical Computer Science Domain (software engineering) Wisdom of crowds Resource (project management) Systematic review 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business 050203 business & management |
| Zdroj: | ACM Computing Surveys. 53:1-36 |
| ISSN: | 1557-7341 0360-0300 |
| DOI: | 10.1145/3368986 |
| Popis: | Over the last few years, Collective Intelligence (CI) platforms have become a vital resource for learning, problem solving, decision-making, and predictions. This rising interest in the topic has to led to the development of several models and frameworks available in published literature. Unfortunately, most of these models are built around domain-specific requirements, i.e., they are often based on the intuitions of their domain experts and developers. This has created a gap in our knowledge in the theoretical foundations of CI systems and models, in general. In this article, we attempt to fill this gap by conducting a systematic review of CI models and frameworks, identified from a collection of 9,418 scholarly articles published since 2000. Eventually, we contribute by aggregating the available knowledge from 12 CI models into one novel framework and present a generic model that describes CI systems irrespective of their domains. We add to the previously available CI models by providing a more granular view of how different components of CI systems interact. We evaluate the proposed model by examining it with respect to six popular, ongoing CI initiatives available on the Web. |
| Databáze: | OpenAIRE |
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