Abstract
Declarative programming in the style of functional and logic programming has been hailed as an alternative parallel programming style where computer programs are automatically parallelized without programmer control. Although this approach removes many pitfalls of explicit parallel programming, it hides important information about the underlying parallel architecture that could be used to improve the scalability and efficiency of programs. In this paper, we present a novel programming model that allows the programmer to reason about thread state in data-driven declarative programs. This abstraction has been implemented on top of Linear Meld, a linear logic programming language that is designed for writing graphbased programs. Wepresent several programs that show theflavorofour new programming model, including graph algorithms and a machine learning algorithm. Our goal is to show thatitis possible to take advantage of architectural details without losing the key advantages of logic programming.

Abstract
Linear logic programs are challenging to implement efficiently because facts are asserted and retracted frequently. Implementation is made more difficult with the introduction of useful features such as rule priorities, which are used to specify the order of rule inference, and comprehensions or aggregates, which are mechanisms that make data iteration and gathering more intuitive. In this paper, we describe a compilation scheme for transforming linear logic programs enhanced with those features into efficient C++ code. Our experimental results show that compiled logic programs are less than one order of magnitude slower than hand-written C programs and much faster than interpreted languages such as Python.

Abstract
In this work, we introduce the MuSer, a propositional framework that explores temporal information available in multi-relational databases. At the core of this system is an encoding technique that translates the temporal information into a propositional sequence of events. By using this technique, we are able to explore the temporal information using a propositional sequence miner. With this framework, we mine each class partition individually and we do not use classical aggregation strategies, like window aggregation. Moreover, in this system we combine feature selection and propositionalization techniques to cast a multi-relational classification problem into a propositional one. We empirically evaluate the MuSer framework using two real databases. The results show that mining each partition individually is a time-and memory-efficient strategy that generates a high number of highly discriminative patterns.

Abstract

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Abstract
Adverse Drug Events (ADEs) are a major health problem, and developing accurate prediction methods may have a significant impact in public health. Ideally, we would like to have predictive methods, that could pinpoint possible ADRs during the drug development process. Unfortunately, most relevant information on possible ADRs is only available after the drug is commercially available. As a first step, we propose using prior information on existing interactions through recommendation systems algorithms. We have evaluated our proposal using data from the ADReCS database with promising results.