Modeling views in the layered view model for XML using UML

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction

Pattern Reification as the Basis for Description-Driven Systems

One of the main factors driving object-oriented software development for information systems is the requirement for systems to be tolerant to change. To address this issue in designing systems, this paper proposes a pattern-based, object-oriented, description-driven system (DDS) architecture as an extension to the standard UML four-layer meta-model. A DDS architecture is proposed in which aspects of both static and dynamic systems behavior can be captured via descriptive models and meta-models. The proposed architecture embodies four main elements - firstly, the adoption of a multi-layered meta-modeling architecture and reflective meta-level architecture, secondly the identification of four data modeling relationships that can be made explicit such that they can be modified dynamically, thirdly the identification of five design patterns which have emerged from practice and have proved essential in providing reusable building blocks for data management, and fourthly the encoding of the structural properties of the five design patterns by means of one fundamental pattern, the Graph pattern. A practical example of this philosophy, the CRISTAL project, is used to demonstrate the use of description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure

Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

Managing Metadata in Data Warehouses: Pitfalls and Possibilities

This paper motivates a comprehensive academic study of metadata and the roles that metadata plays in organizational information systems. While the benefits of metadata and challenges in implementing metadata solutions are widely addressed in practitioner publications, explicit discussion of metadata in academic literature is rare. Metadata, when discussed, is perceived primarily as a technology solution. Integrated management of metadata and its business value are not well addressed. This paper discusses both the benefits offered by and the challenges associated with integrating metadata. It also describes solutions for addressing some of these challenges. The inherent complexity of an integrated metadata repository is demonstrated by reviewing the metadata functionality required in a data warehouse: a decision support environment where its importance is acknowledged. Comparing this required functionality with metadata management functionalities offered by data warehousing software products identifies crucial gaps. Based on these analyses, topics for further research on metadata are proposed

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

The MetaMIS approach for the specification of management views on business processes

The specification of management views on business processes is a main development task in the requirements specification of reporting systems for the management. Today the information warehouse is the accepted IT architecture for this purpose. The paper summarises the state of the research on the so called MetaMIS Approach. The methodological background is discussed and the modelling language comprising the conceptual and the representational language aspects is specified. Guidance rules complete the MetaMIS Modelling Technique. Examples show how the modelling technique works in enterprise environments. Some remarks on problem solving techniques as part of the MetaMIS Modelling Method are given. The appendix gives a detailed overview over the required linguistic operations for the construction of the modelling language.<br/

Design Patterns for Description-Driven Systems

In data modelling, product information has most often been handled separately from process information. The integration of product and process models in a unified data model could provide the means by which information could be shared across an enterprise throughout the system lifecycle from design through to production. Recently attempts have been made to integrate these two separate views of systems through identifying common data models. This paper relates description-driven systems to multi-layer architectures and reveals where existing design patterns facilitate the integration of product and process models and where patterns are missing or where existing patterns require enrichment for this integration. It reports on the construction of a so-called description-driven system which integrates Product Data Management (PDM) and Workflow Management (WfM) data models through a common meta-model.Comment: 14 pages, 13 figures. Presented at the 3rd Enterprise Distributed Object Computing EDOC'99 conference. Mannheim, Germany. September 199