Smart Information Systems
The SIS group develops innovative digital technologies for building and applying intelligent information systems for real-world problems.
Research in Information Retrieval and Case-Based Reasoning uses Data Analytics and Text Mining to transform databases, text/Web documents and multimedia collections into smart information systems.
SIS research addresses the question of how to extract context-aware knowledge from data, text or multimedia content for decision support.
Similarity knowledge and semantic indexing algorithms are developed to enable knowledge-rich content representations that can transform experiential content into case-based decision support systems.
Context-aware knowledge discovery algorithms further enrich these representations through the integration of user behaviour, temporal and geospatial dimensions to support smarter retrieval, recommender and analytics systems.
SIS research on smart technologies is applied in intelligent search and browsing tools, personalised recommender systems, context-aware information retrieval, incident reporting and decision support systems.
Smart Systems have been developed for various application domains including Oil & Gas, Healthcare, Journalism, Tourism and Social Media.
Group members interests in field of:
- Case-Based Reasoning
- Data, Text and Web mining
- Information Retrieval
- Sentiment Analysis and Data Analytics
- SocialSensor: Sensing user generated input for improved media discovery and experience (EU FP7)
- Survey response data analytics (KTP with Pexel Ltd)
- Smart Beacons (Horizon Smart Tourism with Museums Galleries Scotland & Neatebox)
- Living History (Horizon Smart Tourism with Historic Scotland & AmbieSense)
- Classification and sentiment analysis of #ACC tweets (project with Aberdeen city council)
- Audio Feature Extraction for Music Recommendation
- AHRC - STAR Accessing Implicit Knowledge of Textiles and Design (AHRC with Heriot-Watt & Johnstons)
- Textual Revision Applications in NLG and Textual CBR (NRP with Aberdeen University)
- Knowledge Acquisition for Textual CBR (UKIERI with IIT Chennai and IBM India)
- Project Planning for Well Engineering (KTP with XCD)
- CBR for Remote Patient Health-Care Monitoring (KTP with AxSys Ltd)
- CBR for Anomaly Report Processing (with European Space Agency)
- Recipe recommendation with Food nutrient analytics (with EatBalanced)
- Hybrid User Profiling and Adaptation (NRP with Yahoo!)
- Context-sensitive High-order Language Model (RSE/NSFC with Tianjin)
Susan is a Research Professor in Case-Based Reasoning, Data/Text Mining, Knowledge Discovery
Professor Ayse Goker is an expert in user-centred and context-aware information retrieval. She is theme leader in Digital Technologies at the IDEAS Research Institute and RGUs Principal Investigator for the SocialSensor project.
Research interest focuses on Textual Case-based Reasoning and related areas.
Active researcher in Case-Based Reasoning, Data/Text Mining, Knowledge Discovery and Personalisation
Professor in Computing. Her research interests are in knowledge discovery from data and text and Case-based Reasoning (CBR) Systems
Research Student focusing on Argumentation in the Social Web
Personalised E-learning System
- Leszek Kaliciak
- Jose Lloret Perez
- Ralf Bierig (Vienna University of Technology)
- Sutanu Chakraborti (IIT Chennai)
- David Harper (Google)
- Daqing He (University of Pittsburgh)
- Jacek Jarmulak (Ingenuity Systems CA)
- Joemon Jose (Glasgow University)
- Ivan Koychev (University of Sofia)
- Keith Matthews (James Hutton Institute)
- Rahman Mukras (Amec)
- Gheorghe Muresan (Glassdoor.com)Peng Zhang (Tianjin University)
- Rainer Schmidt (University Rostock)