Introduction to the TPLP special issue, logic programming in databases: From Datalog to semantic-web rules

Much has happened in data and knowledge base research since the introduction of the relational model in Codd (1970) and its strong logical foundations influence its advances ever since. Logic has been a common ground where Database and Artificial Intelligence research competed and collaborated with each other for a long time (Abiteboul et al. 1995). The product of this joint effort has been a set of logic-based formalisms, such as the Relational Calculus (Codd 1970), Datalog (Ceri et al. 1990), Description Logics (Baader et al. 2007), etc., capturing not only the structure but also the semantics of data in an explicit way, thus enabling complex inference procedures.This special issue contains three rigorously reviewed articles addressing problems that span from Query Answering to Data Mining. All these contributions have their roots in the foundational formalisms of Data and Knowledge Bases such as Logic Programming, Description Logic and Hybrid Logics, representing a clear example of the effort that the Database and the Semantic-Web communities are producing to bridge the various schools of thinking in modern Data and Knowledge Management

Pushing context-awareness down to the core: moreflexibility for the PerLa language

Information technology is increasingly pervading our envi- ronment, making real Mark Weiser’s vision of a “disappear- ing technology”. The work described in this paper focuses on using context to enable pervasive system personaliza- tion, allowing context-aware sensor-data tailoring. Since sensor networks, besides data collection, are also able to pro- duce active behaviours, the tailoring capabilities are also ex- tended to these, thus applying context-awareness to generic system operations. Moreover, because the number of pos- sible context can grow rapidly with the complexity of the application, the design phase is also supported by the possi- bility to speed-up and modularize the definition of the data and operations associated with each specific context, pro- ducing a support tool that eases the job of the designers of modern context-aware pervasive systems

Towards autonomic pervasive systems: the PerLa context language

The property of context-awareness, inherent to a Pervasive System, requires a clear definition of context and of how the context parameter values must be extracted from the real world. Since often the same variables are common to the operational system and to the context it operates into, the usage of the same language to manage both the application and the context can lead to substantial savings in application development time and costs. In this paper we propose a context-management extension to the PerLa language and middleware that allows for declarative gathering of context data from the environment, feeding this data to the internal context model and, once a context is active, acting on the relevant resources of the pervasive system, according to the chosen contextual policy