Model and Data Engineering

Distributed algorithms are present in our daily life and we depend on the correct functioning of complex distributed computing systems as, for instance, communication protocols for establishing sessions between a smartphone and a bank account or synchronisation and management of shared resources among competing processes. Generally, the design and the implementation of distributed algorithms are still error prone and it is mainly due to the relationship between the theory of distributed computing and practical techniques for designing and verifying the correctness of reliable distributed systems. Formal proofs of distributed algorithms are long, hard and tedious and the gap between the real algorithm and its formal proof is very important. In this talk, we consider the correct-by-construction approach based on the refinement of state-based models, which are progressively transformed, in order to obtain a state-based model that is translated into a distributed algorithm. The stepwise development of algorithms has been first initiated in the seminal works of Dijkstra [15], Back [7] or Morgan [23]. Next, UNITY [14] has proposed a rich framework for designing distributed algorithms combining a simple temporal logic for expressing required properties and a simple language for expressing actions modifying state variables under fairness assumption. TLA/TLA [18] proposes a general modelling language based on a temporal ogic of actions combined with a set-theoretical modelling language for data and is extended by a specific algorithmic language namely PlusCAL, which is translated into TLA and which is closer to the classical way to express a distributed algorithm. Finally, Event-B [2,12] is a modelling language which can describe state-based models and required safety properties. The main objective is to provide a technique for incremental and proof-based development of reactive systems. It integrates set-theoretical notations and a first-order predicate calculus, models called machines; it includes the concept of refinement expressing the simulation of machine by another one. An Event-B machine models a reactive system i.e. a system driven by its environment and reacting to its stimuli. An important property of these machines is that its events preserve the invariant properties defining a set of reachable states. The Event-B method has been developed from the classical B method [1] and it offers a general framework for developing the correctby-construction systems by using an incremental approach for designing the models by refinement. Refinement [7,15] is a relationship relating two models such that one model is refining or simulating the other one. When an abstract model is refined by a concrete model, it means that Y. Ait Ameur et al. (Eds.): MEDI 2014, LNCS 8748, pp. 1–3, 2014. c © Springer International Publishing Switzerland 2014

[1]  Vít Novácek,et al.  Empirical Merging of Ontologies - A Proposal of Universal Uncertainty Representation Framework , 2006, ESWC.

[2]  Jalel Akaichi,et al.  Efficient Algorithm to Approximate Values with Non-uniform Spreads Inside a Histogram Bucket , 2014, MEDI.

[3]  Omar Boussaïd,et al.  Modèle multidimensionnel d'objets complexes. Du modèle d'objets aux cubes d'objets complexes , 2011, Ingénierie des Systèmes d Inf..

[4]  Aditya Agrawal,et al.  On the use of Graph Transformations in the Formal Specification of Computer-Based Systems kle , 2003 .

[5]  Dongwook Shin,et al.  BUS: an effective indexing and retrieval scheme in structured documents , 1998, DL '98.

[6]  Jamel Feki,et al.  CobWeb Multidimensional Model: From Modeling to Querying , 2014, MEDI.

[7]  Alexander Maedche,et al.  Clustering Ontology-Based Metadata in the Semantic Web , 2002, PKDD.

[8]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[9]  Ronan Tournier Analyse en ligne (OLAP) de documents , 2007 .

[10]  Erhard Rahm,et al.  ATOM: Automatic target-driven ontology merging , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[11]  Adam Souzis,et al.  Semantic Annotations For Wsdl And Xml Schema , 2015 .

[12]  Rachid Chalal,et al.  Merging ontology by semantic enrichment and combining similarity measures , 2013, Int. J. Metadata Semant. Ontologies.

[13]  Zhongbo Wu,et al.  Web Service Matching for RESTful Web Services Based on Parameter Semantic Network , 2014 .

[14]  Tok Wang Ling,et al.  DDE: from dewey to a fully dynamic XML labeling scheme , 2009, SIGMOD Conference.

[15]  Can Türker SQL:1999 & SQL:2003 - Objektrelationales SQL, SQLJ & SQL/XML , 2003 .

[16]  Roberto Chinnici,et al.  Web Services Description Language (WSDL) Version 2.0 Part 1: Core Language , 2007 .

[17]  Jérôme Euzenat,et al.  Ontology Matching: State of the Art and Future Challenges , 2013, IEEE Transactions on Knowledge and Data Engineering.

[18]  Jiawei Han,et al.  Topic Cube: Topic Modeling for OLAP on Multidimensional Text Databases , 2009, SDM.

[19]  Erhard Rahm,et al.  COMA - A System for Flexible Combination of Schema Matching Approaches , 2002, VLDB.

[20]  Bernard Dousset,et al.  DocCube: Multi-dimensional visualisation and exploration of large document sets , 2003, J. Assoc. Inf. Sci. Technol..

[21]  Ralph Kimball,et al.  The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses , 1996 .

[22]  Mark A. Musen,et al.  PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment , 2000, AAAI/IAAI.

[23]  Kai Schweinsberg Abbildung von XML-Dokumenten auf SQL:2003-konforme Datentypen , 2012 .

[24]  Jesús Manuel Almendros-Jiménez,et al.  An XQuery-Based Model Transformation Language , 2014, MEDI.

[25]  Guanyu Li,et al.  Multi-mapping based ontology merging system design , 2010, 2010 2nd International Conference on Advanced Computer Control.

[26]  Bo Zhao,et al.  Text Cube: Computing IR Measures for Multidimensional Text Database Analysis , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[27]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[28]  Chun Zhang,et al.  Storing and querying ordered XML using a relational database system , 2002, SIGMOD '02.

[29]  Erhard Rahm,et al.  Towards a Benchmark for Ontology Merging , 2012, OTM Workshops.

[30]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[31]  Hans-Jörg Schek,et al.  The relational model with relation-valued attributes , 1986, Inf. Syst..

[32]  Hyoil Han,et al.  XML-OLAP: A Multidimensional Analysis Framework for XML Warehouses , 2005, DaWaK.

[33]  M. Amparo Vila,et al.  Using Textual Dimensions in Data Warehousing Processes , 2010, IPMU.

[34]  Yassin Chabeb Contributions à la description et la découverte de services web sémantiques. (Contributions to semantic web services description and discovery) , 2011 .

[35]  X. Wu,et al.  A prime number labeling scheme for dynamic ordered XML trees , 2004, Proceedings. 20th International Conference on Data Engineering.