A Constraint-based Approach for Generating Transformation Patterns

Undoing operations is an indispensable feature for many collaborative applications, mainly collaborative editors. It provides the ability to restore a correct state of shared data after erroneous operations. In particular, selective undo allows to undo any operation and is based on rearranging operations in the history thanks to the Operational Transformation (OT) approach. OT is an optimistic replication technique allowing for updating the shared data concurrently while maintaining convergence. It is a challenging task how to meaningfully combine OT and undo approaches. Indeed, undoing operations that are received and executed out-of-order at different sites leads to divergence cases. Even though various undo solutions have been proposed over the recent years, they are either limited or erroneous. In this paper, we propose a constraint-based approach to address the undo problem. We use Constraint Satisfaction Problem (CSP) theory to devise correct and undoable transformation patterns (w.r.t OT and undo properties) which considerably simplifies the design of collaborative objects.Comment: In Proceedings FOCLASA 2015, arXiv:1512.0694

Constraint Handling Rules with Binders, Patterns and Generic Quantification

Constraint Handling Rules provide descriptions for constraint solvers. However, they fall short when those constraints specify some binding structure, like higher-rank types in a constraint-based type inference algorithm. In this paper, the term syntax of constraints is replaced by $\lambda$-tree syntax, in which binding is explicit; and a new $\nabla$ generic quantifier is introduced, which is used to create new fresh constants.Comment: Paper presented at the 33nd International Conference on Logic Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017 16 pages, LaTeX, no PDF figure

The Maximum Entropy principle and the nature of fractals

We apply the Principle of Maximum Entropy to the study of a general class of deterministic fractal sets. The scaling laws peculiar to these objects are accounted for by means of a constraint concerning the average content of information in those patterns. This constraint allows for a new statistical characterization of fractal objects and fractal dimension.Comment: 7 pages, RevTex, includes 2 PS figure

A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database

Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In the context of sequential pattern mining, a large number of devoted techniques have been developed for solving particular classes of constraints. The aim of this paper is to investigate the use of Constraint Programming (CP) to model and mine sequential patterns in a sequence database. Our CP approach offers a natural way to simultaneously combine in a same framework a large set of constraints coming from various origins. Experiments show the feasibility and the interest of our approach

Global SPACING Constraint (Technical Report)

We propose a new global SPACING constraint that is useful in modeling events that are distributed over time, like learning units scheduled over a study program or repeated patterns in music compositions. First, we investigate theoretical properties of the constraint and identify tractable special cases. We propose efficient DC filtering algorithms for these cases. Then, we experimentally evaluate performance of the proposed algorithms on a music composition problem and demonstrate that our filtering algorithms outperform the state-of-the-art approach for solving this problem