The goal: consistency maintenance when insertions or deletions are performed on a valid database containing marked nulls.
The prototype offers a platform where facts and rules are inserted (to simulate the database). Then insertions and deletions can be performed. The platform shows how our method uses chase and core algorithms to implement our update solution.
Our algorithms allow the use of constraints such as tuple generating dependencies (TGD) which are not weakly acyclic.
Our team (programmers are marked with (*))
Jacques Chabin, LIFO, Université d'Orléans, France (*).
Mirian Halfeld Ferrari, LIFO, Université d'Orléans, France.
Dominique Laurent, ETIS, Université Paris Seine, CNRS, Cergy-Pontoise, France.
Lucas Moret-Bailly, training at LIFO 2019, Université d'Orléans, France (*).
Julien Revaud, training at LIFO 2018, Université d'Orléans, France (*).
Downloadable packages
Our prototype is available for use:
Version V1: specially implemented by Julien Revaud and Jacques Chabin
README: general instructions to execute and test our platform.
README1: general instructions to execute and test our platform (pdf format).
Our approach has been described in the following publications:
Mirian Halfeld Ferrari Alves, Dominique Laurent: Updating RDF/S Databases Under Constraints. ADBIS 2017: 357-371
LOIR, POTI, and WALO
Three different versions of a refinement method for service composition:
The approach: an automatic selection task, guided by quantified indicators (obtained as results of the recommendation routine), allows the construction of solutions adapted to each user. Our refinement routine renders possible to translate abstract specifications into concrete ones. An algorithm is used to find a combination of concrete services to implement the functionality defined by the higher-level specification. We suppose that the capabilities of each concrete service are also specified in terms of abstract building blocks, similar to those used in the abstract composition. Refinement is guided by a recommendation system and provides results in decreasing order of preferences. The goal of this preference-driven recommendation system is to help users deal with information overloading.
Our translation technique is based on the MiniCon algorithm for relational query rewriting, by Pottinger and Halevy (see [PH01]).
By restraining the search space, via the introduction of coverage domains, and by adopting a preference-driven traversal of this search space, our approach alleviates the effects of the exponential time complexity of the refinement algorithm, bringing our complexity to a polynomial perspective, in practice.
The versions differ in implementation options or in the methods used to combine preferences.
These three algorithms have been elaborated on the basis of Minicon (whose implementation was gently given by their authors). The modifications and extensions have been developed by:
Thiago Cerqueira, DIMAp, Universidade Federal do Rio Grande do Norte, Natal, Brazil.
Umberto Costa, DIMAp, Universidade Federal do Rio Grande do Norte, Natal, Brazil.
Mirian Halfeld Ferrari, LIFO, Université d'Orléans, France.
Martin A. Musicante, DIMAp, Universidade Federal do Rio Grande do Norte, Natal, Brazil.
Sophie Robert, LIFO, Université d'Orléans, France.
The following colleagues have participated in the development of previous versions:
Remy Ferre.
Simon Munier.
Véronika Peralta, LI, Université François Rabelais Tours, France.
Downloadable packages
LOIR, POTI, and WALO are free software. They can be used under the terms of the GNU LGPL v3 license.
The current distribution package is downloadable from this page. The following packages are available:
README: general instructions to compile and execute the programs.
Files LOIR.zip, POTI.zip, and WALO.zip containing the sources of the three versions. Each one has its own README.
File scripts.zip contains some bash scripts used to generate data files for our tests.
Associated Publications
Our approach has been described in the following publications:
Cheikh Ba, Thiago Cerqueira, Umberto Souza da Costa, Mírian Halfeld Ferrari, Martin A. Musicante, Sophie Robert: Experiments on Service Composition Refinement on the basis of Preference-driven Recommendation. Submitted to the International Journal of Web and Grid Services, 2016.
Cheikh Ba, Umberto Souza da Costa, Mírian Halfeld Ferrari, Rémy Ferré, Martin A. Musicante, Verónika Peralta, Sophie Robert: Preference-driven Refinement of Service Compositions. CLOSER 2014: 268-275
Umberto Souza da Costa, Mirian Halfeld Ferrari Alves, Martin A. Musicante, Sophie Robert: Automatic Refinement of Service Compositions. ICWE 2013: 400-407