Data, Content and Media

Objective: The PA objective is both to advance methods and technologies and to discover piece of knowledge for a better understanding of individual and collective phenomena and behaviors and for the construction of intelligent and autonomous systems in challenging domains.

The main research and development challenges concern the
  • modelling, analysis, and visualization of data, which cannot be processed with traditional methods;
  • extraction of knowledge and learning predictive models from multi-dimensional, multi-sources, networked, and dynamic data based on artificial intelligence, data mining and network science methods;
  • intelligent processing of image, audio, and audio-visual content for the development of applications based on content recognition;
  • analysis and comparison of digital content for 3D models, and more generally, multi-dimensional representations;
  • development of applied ontologies of socio-technical systems and semantic technologies for their treatment based on the languages of the semantic web and for semantic interoperability;
  • natural interaction with computer systems based on multimodal paradigms that make it accessible and usable.
The AP research and development activities involve
  • 15 CNR Institutes: ISTI, IMATI, ITC, IIT, ISTC, IREA, IEIIT, ICAR, IASI, IAC, STIIMA, IFAC, GI, IRC, ISTEC
  • a total of around 600 person-months per year.
Approach: The AP approach and research activities will be developed according to the following research lines
  • DATA
    • Line1: Big data Sensing and Management
    • Line2: Knowledge Representation, Reasoning and Engineering
    • Line3: Knowledge Extraction and Semantic enrichment
  • CONTENT
    • Line 4: Data Mining and Machine Learning
    • Line 5: Network Analysis
    • Line 6: Behavior Analysis
  • MEDIA
    • Line 7: Acquisition, modelling, and analysis of images, videos, 3D and multidimensional data
    • Line 8: Multimodal Interaction and Accessibility
Scientific Impact & Results will cover
  • database and semantic web technologies
  • knowledge representation and management
  • data visualization, data mining and pattern recognition
  • machine learning, and artificial intelligence, complex system theory and network science
  • information retrieval and text mining, statistics and applied mathematics
  • natural language processing, computer vision and computer graphics
  • user modelling and cognitive computing

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