A Method for Developing Model to Text Transformations

In the field of business process development, model transformations play a key role, for example for moving from business process models to either code or inputs for simulation systems, as well as to convert models expressed with notation A into equivalent models expressed with notation B. In the literature, many cases of useful transformations of business process models can be found. However, in general each transformation has been developed in an ad-hoc fashion, at a quite low-level, and its quality is often neglected. To ensure the quality of the transformations is important to apply to them all the well-known software engineering principles and practices, from the requirements definition to the testing activities. For this reason, we propose a method, MeDMoT, for developing non-trivial Model to Text Transformations, which prescribes how to: (1) capture and specify the transformation requirements; (2) design the transformation, (3) implement the transformation and (4) test the transformation. The method has been applied in several case studies, including a transformation of UML business processes into inputs for an agent-based simulator

Business Grid Services

Grid services have come to represent the synthesis of web services and grid computing paradigms. Web services provide the means to modularize software, enabling loosely coupled and novel synthesis. Grid computing removes the binding between functional software components and specific hosting hardware, enabling software to be deployed dynamically over a network (e.g. intra-, extra- or inter-net). Applying the constructs of grid computing to the service orientation of enterprise software will allow business service networks to utilize more specialized services. An upper service ontology that enables business grid services to be described and then related to the grid hosting platform is presented. Explicit knowledge is required for enterprise software, hosting servers and the domain that can then be utilized by both SLA and reservation systems. The ontology presented is derived from and validated using a collection of web services taken from leading investment banks

Design for geospatially enabled climate modeling and alert system (CLIMSYS):A position paper

The paper brings the focus on to multi-disciplinary approach of presenting climate analysis studies, taking help of interdisciplinary fields to structure the information. The system CLIMSYS provides the crucial element of spatially enabling climate data processing. Even though climate change is a matter of great scientific relevance and of broad general interest, there are some problems related to its communication. Its a fact that finding practical, workable and cost-efficient solutions to the problems posed by climate change is now a world priority and one which links government and non-government organizations in a way not seen before. An approach that should suffice is to create an accessible intelligent system that houses prior knowledge and curates the incoming data to deliver meaningful results. The objective of the proposed research is to develop a generalized system for climate data analysis that facilitates open sharing, central implementation, integrated components, knowledge creation, data format understanding, inferencing and ultimately optimal solution delivery, by the way of geospatial enablement

Spatial Aggregation: Theory and Applications

Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.Comment: See http://www.jair.org/ for any accompanying file

Building Integrated Ontological Knowledge Structures with Efficient Approximation Algorithms

Publisher’s version made available under a Creative Commons license.The integration of ontologies builds knowledge structures which brings new understanding on existing terminologies and their associations. With the steady increase in the number of ontologies, automatic integration of ontologies is preferable over manual solutions in many applications. However, available works on ontology integration are largely heuristic without guarantees on the quality of the integration results. In this work, we focus on the integration of ontologies with hierarchical structures. We identified optimal structures in this problem and proposed optimal and efficient approximation algorithms for integrating a pair of ontologies. Furthermore, we extend the basic problem to address the integration of a large number of ontologies, and correspondingly we proposed an efficient approximation algorithm for integrating multiple ontologies. The empirical study on both real ontologies and synthetic data demonstrates the effectiveness of our proposed approaches. In addition, the results of integration between gene ontology and National Drug File Reference Terminology suggest that our method provides a novel way to perform association studies between biomedical terms

“Refactoring” Refactoring

Code refactoring’s primary impetus is to control technical debt, a metaphor for the cost in software development due to the extraneous human effort needed to resolve confusing, obfuscatory, or hastily-crafted program code. While these issues are often described as causing “bad smells,” not all bad smells emanate from the code itself. Some (often the most pungent and costly) originate in the formation, or expressions, of the antecedent intensions the software proposes to satisfy. Paying down such technical debt requires more than grammatical manipulations of the code. Rather, refactoring in this case must attend to a more inclusive perspective; particularly how stakeholders perceive the artifact; and their conception of quality – their appreciative system. First, this paper explores refactoring as an evolutionary design activity. Second, we generalize, or “refactor,” the concept of code refactoring, beyond changes to code structure, to improving design quality by incorporating the stakeholders’ experience of the artifact as it relates to their intensions. Third, we integrate this refactored refactoring as the organizing principle of design as a reflective practice. The objective is to improve the clarity, understandability, maintainability, and extensibility manifest in the stakeholder intensions, in the artifact, and in their interrelationship

Integrating Ontologies and Relational Data

In recent years, an increasing number of scientific and other domains have attempted to standardize their terminology and provide reasoning capabilities through ontologies, in order to facilitate data exchange. This has spurred research into Web-based languages, formalisms, and especially query systems based on ontologies. Yet we argue that DBMS techniques can be extended to provide many of the same capabilities, with benefits in scalability and performance. We present OWLDB, a lightweight and extensible approach for the integration of relational databases and description logic based ontologies. One of the key differences between relational databases and ontologies is the high degree of implicit information contained in ontologies. OWLDB integrates the two schemes by codifying ontologies\u27 implicit information using a set of sound and complete inference rules for SHOIN (the description logic behind OWL ontologies. These inference rules can be translated into queries on a relational DBMS instance, and the query results (representing inferences) can be added back to this database. Subsequently, database applications can make direct use of this inferred, previously implicit knowledge, e.g., in the annotation of biomedical databases. As our experimental comparison to a native description logic reasoner and a triple store shows, OWLDB provides significantly greater scalability and query capabilities, without sacrifcing performance with respect to inference

Multiagent-Based Model For ESCM

Web based applications for Supply Chain Management (SCM) are now a necessity for every company in order to meet the increasing customer demands, to face the global competition and to make profit. Multiagent-based approach is appropriate for eSCM because it shows many of the characteristics a SCM system should have. For this reason, we have proposed a multiagent-based eSCM model which configures a virtual SC, automates the SC activities: selling, purchasing, manufacturing, planning, inventory, etc. This model will allow a better coordination of the supply chain network and will increase the effectiveness of Web and intel-ligent technologies employed in eSCM software

Towards an interoperable decision support platform for eco-labeling process

Along with the rising concern of environmental performance, eco-labelling is becoming more popular. However, the complex process of eco-labelling demotivated manufacturers and service providers to be certificated. In this paper, we propose a decision support system aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels. This decision support system will be based upon a comprehensive knowledge base composed of various domain ontologies covering the whole life cycle of a product or service. Through continuous enrichment on the knowledge base in modular ontologies and by defing standard RDF and OWL format interfaces, the decision support system will stimulate domain knowledge sharing and have the interoperability to be applied into other practice