Merging graph-based and rule-based computation : the language G-Log

In this paper we discuss the merging of two different computation paradigms: the fixpoint computation for deductive databases and the pattern-matching computation for graph-based languages. We show how these paradigms can be combined on the example of the declarative, graph-based, database query language G-Log. A naive algorithm to compute G-Log programs turns out to be very inefficient. However, we also present a backtracking fixpoint algorithm for Generative G-Log, a syntactical sublanguage of G-Log that, like G-Log, is non-deterministic complete. This algorithm is considerably more efficient, and reduces to the standard fixpoint computation for a sublanguage of Generative G-Log that is a graphical equivalent of Datalog. The paper also studies some interesting properties like satisfiability and triviality, that are undecidable for full G-Log and turn out to be decidable for sufficiently general classes of Generative G-Log programs

Preference Mining in the Travel Domain

Personalization of user experience through recommendations involves understanding their preferences and the context they are living in. In this work, we present a method to rank travel offers returned in response to a travel request made by a user. To give a sensible answer, we learn users' preferences over time and use them to understand travelers' needs. Our solution is based on a data-mining-based recommender system. We first design a database of historical traveler data and populate it with data generated according to rules mimicking the features of actual user profiles. These rules are then used as ground truth to validate the accuracy of the proposed learning algorithm. After performing data pre-processing, a knowledge base is set up by mining association rules from the database, which will then be used along with the travel request to assign a score to each of the potential travel offers, thus ranking them. To test the proposed methodology, we generate synthesized data according to some distributions. The results of the experiments approve the effectiveness of the proposed ranking mechanisms. Finally, we demonstrate the presentation of the ranked offers to the user via some mock-ups of the intended application

Merging graph-based and Rule-based Computation: the language g-log

In this paper we discuss the merging of two different computation paradigms: the fixpoint computation for deductive databases and the pattern-matching computation for graph-based languages. We show how these paradigms can be combined on the example of the declarative, graph-based, database query language G-Log. A naive algorithm to compute G-Log programs turns out to be very inefficient. However, we also present a backtracking fixpoint algorithm for Generative G-Log, a syntactical sublanguage of G-Log that, like G-Log, is non-deterministic complete. This algorithm is considerably more efficient, and reduces to the standard fixpoint computation for a sublanguage of Generative G-Log that is a graphical equivalent of Datalog. The paper also studies some interesting properties like satisfiability and triviality, that are undecidable for full G-Log and turn out to be decidable for sufficiently general classes of Generative G-Log programs

G-Log: A declarative graphical query language

In this paper we introduce G-Log, a declarative graphical query language which combines of the expressive power of logic, the modelling power of objectorientedness and the representation power of graphs. As in the case of prolog, G-Log may be used in a totally declarative way, as well as in a more procedural way. Furthermore, it provides an intuitive and flexible graphical tool for non-expert database users. We prove that G-Log is a graphical equivalent of the first order predicate calculus. Finally, we study its features as a non-deterministic language and compare it with other existing non-deterministic languages

Checking functional consistency in deductive databases

In this paper we address the problem of integrating functions in Datalog. We extend Datalog to a language containing negation, functions, and a strong type system. This type system is used by an algorithm that, given a program, checks for its -consistency (a stronger form of consistency) by using the notions of local dependencies and global dependencies

Integration of functions in logic database systems

We extend Datalog, a logic programming language for rule-based systems, by respectively integrating types, negation and functions. This extention of Datalog is called MilAnt. Furthermore, MilAnt consistency is defined as a stronger form of consistency for functions. It is known that consistency for functions is undecidable. We prove that MilAnt consistency is decidable and an algorithm is given to detect the MilAnt consistency of a MilAnt program. To this end, we use a mixture of dependencies that are local to a rule and dependencies that are global for the whole program