Equipe de recherche LACODAM
Data collection is ubiquitous nowadays and it is providing our society with tremendous volumes of knowledge about human, environmental, and industrial activity. This ever-increasing stream of data holds the keys to new discoveries, both in industrial and scientific domains. However, those keys will only be accessible to those who can make sense out of such data. Making sense out of data is a hard problem. It requires a good understanding of the data at hand, proficiency with the available analysis tools and methods, and good deductive skills. All these skills have been grouped under the umbrella term “Data Science” and universities have put a lot of effort in producing professionals in this field. “Data Scientist” is currently the most sought-after job in the USA, as the demand far exceeds the number of competent professionals. Despite its boom, data science is still mostly a “manual” process: current data analysis tools still require a significant amount of human effort and know-how. This makes data analysis a lengthy and error-prone process. This is true even for data science experts, and current approaches are mostly out of reach of non-specialists.
We claim that nowadays, Data Science is in its “Iron Age”: Good tools are available, however skilled craftsmen are required to use them in order to transform raw material (the data) into finished products (knowledge, decisions). We foresee that in a decade from now, we should be in an “Industrial Age” of Data Science, where more elaborate tools will alleviate a lot of the human work required in Data Science. Basic Data Science tasks will no longer require a skilled data scientist; instead software tools will enable small companies or even individuals to get valuable knowledge from their data. Skilled data scientists will thus be fully available to work on the hard tasks that matter. This will entail a drastic improvement in productivity thanks to a new generation of tools that will do the tedious work for data analysts and scientists.
The objective of the LACODAM team is to facilitate the process of making sense out of large amounts of data. This can serve the purpose of deriving knowledge and insights for better decision-making. Since data science in its current state involves lots of human intervention, we envision a novel generation of data analysis and decision support tools that require significantly less tedious human work. Such solutions will rely only on a few interactions between the user and the system with high added value. We foresee solutions that bridge data mining techniques with artificial intelligence (AI) approaches, in order to integrate existing automated reasoning techniques in knowledge discovery workflows. Such solutions can be seen as “second order” AI tasks: they exploit AI techniques (for example, planning) in order to pilot more classical AI tasks such as data mining and decision support.
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