Knowledge and Information Systems

, Volume 28, Issue 2, pp 283–310 | Cite as

CAS-Mine: providing personalized services in context-aware applications by means of generalized rules

  • Elena Baralis
  • Luca Cagliero
  • Tania Cerquitelli
  • Paolo GarzaEmail author
  • Marco Marchetti
Regular Paper


Context-aware systems acquire and exploit information on the user context to tailor services to a particular user, place, time, and/or event. Hence, they allow service providers to adapt their services to actual user needs, by offering personalized services depending on the current user context. Service providers are usually interested in profiling users both to increase client satisfaction and to broaden the set of offered services. Novel and efficient techniques are needed to tailor service supply to the user (or the user category) and to the situation in which he/she is involved. This paper presents the CAS-Mine framework to efficiently discover relevant relationships between user context data and currently asked services for both user and service profiling. CAS-Mine efficiently extracts generalized association rules, which provide a high-level abstraction of both user habits and service characteristics depending on the context. A lazy (analyst-provided) taxonomy evaluation performed on different attributes (e.g., a geographic hierarchy on spatial coordinates, a classification of provided services) drives the rule generalization process. Extracted rules are classified into groups according to their semantic meaning and ranked by means of quality indices, thus allowing a domain expert to focus on the most relevant patterns. Experiments performed on three context-aware datasets, obtained by logging user requests and context information for three real applications, show the effectiveness and the efficiency of the CAS-Mine framework in mining different valuable types of correlations between user habits, context information, and provided services.


Generalized association rules Context-aware applications User and service profiling Itemset mining Rule classification 


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Elena Baralis
    • 1
  • Luca Cagliero
    • 1
  • Tania Cerquitelli
    • 1
  • Paolo Garza
    • 2
    Email author
  • Marco Marchetti
    • 3
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTorinoItaly
  2. 2.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly
  3. 3.Telecom Italia LabTorinoItaly

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