Modeling Virtual Organization Architecture with the Virtual Organization Breeding Methodology

While Enterprise Architecture Modeling (EAM) methodologies become more and more popular, an EAM methodology tailored to the needs of virtual organizations (VO) is still to be developed. Among the most popular EAM methodologies, TOGAF has been chosen as the basis for a new EAM methodology taking into account characteristics of VOs presented in this paper. In this new methodology, referred as Virtual Organization Breeding Methodology (VOBM), concepts developed within the ECOLEAD project, e.g. the concept of Virtual Breeding Environment (VBE) or the VO creation schema, serve as fundamental elements for development of VOBM. VOBM is a generic methodology that should be adapted to a given VBE. VOBM defines the structure of VBE and VO architectures in a service-oriented environment, as well as an architecture development method for virtual organizations (ADM4VO). Finally, a preliminary set of tools and methods for VOBM is given in this paper.Comment: 9 pages, 4 figure

Meeting of the MINDS: an information retrieval research agenda

Since its inception in the late 1950s, the field of Information Retrieval (IR) has developed tools that help people find, organize, and analyze information. The key early influences on the field are well-known. Among them are H. P. Luhn's pioneering work, the development of the vector space retrieval model by Salton and his students, Cleverdon's development of the Cranfield experimental methodology, Spärck Jones' development of idf, and a series of probabilistic retrieval models by Robertson and Croft. Until the development of the WorldWideWeb (Web), IR was of greatest interest to professional information analysts such as librarians, intelligence analysts, the legal community, and the pharmaceutical industry

Designing a consulting services architecture model

textDuring my years of experience in the technology industry, it has become obvious that standard processes and methodologies within the engineering discipline are at a mature state. The realization though is that software engineering specifically lags behind. Most software engineering methodologies that I have studied focus on the mission of software development. It is this realization and the need for structure that led me to review existing methodologies used within my company's software services organization. The definition of what a successful software services methodology entails is rather limited. This report will provide a history of existing software engineering methodologies that I have studied, describe an initial services method that was being developed within my organization, develop a new model that addresses previous shortcomings and identify additional components required to further define a strong software services-oriented delivery methodology.Electrical and Computer Engineerin

Some Issues on Ontology Integration

The word integration has been used with different meanings in the ontology field. This article aims at clarifying the meaning of the word “integration” and presenting some of the relevant work done in integration. We identify three meanings of ontology “integration”: when building a new ontology reusing (by assembling, extending, specializing or adapting) other ontologies already available; when building an ontology by merging several ontologies into a single one that unifies all of them; when building an application using one or more ontologies. We discuss the different meanings of “integration”, identify the main characteristics of the three different processes and proposethree words to distinguish among those meanings:integration, merge and use

The impact of enterprise application integration on information system lifecycles

Information systems (IS) have become the organisational fabric for intra-and inter-organisational collaboration in business. As a result, there is mounting pressure from customers and suppliers for a direct move away from disparate systems operating in parallel towards a more common shared architecture. In part, this has been achieved through the emergence of new technology that is being packaged into a portfolio of technologies known as enterprise application integration (EAI). Its emergence however, is presenting investment decision-makers charged with the evaluation of IS with an interesting challenge. The integration of IS in-line with the needs of the business is extending their identity and lifecycle, making it difficult to evaluate the full impact of the system as it has no definitive start and/or end. Indeed, the argument presented in this paper is that traditional life cycle models are changing as a result of technologies that support their integration with other systems. In this paper, the need for a better understanding of EAI and its impact on IS lifecycles are discussed and a classification framework proposed.Engineering and Physical Sciences Research Council (EPSRC) Grant Ref: (GR/R08025) and Australian Research Council (DP0344682)

A quality management based on the Quality Model life cycle

Managing quality is a hard and expensive task that involves the execution and control of processes and techniques. For a good quality management, it is important to know the current state and the objective to be achieved. It is essential to take into account with a Quality Model that specifies the purposes of managing quality. QuEF (Quality Evaluation Framework) is a framework to manage quality in MDWE (Model-driven Web Engineering). This paper suggests managing quality but pointing out the Quality Model life cycle. The purpose is to converge toward a quality continuous improvement by means of reducing effort and time.Ministerio de Ciencia e Innovación TIN2010-20057-C03-02Ministerio de Ciencia e Innovación TIN 2010-12312-EJunta de Andalucía TIC-578

Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment

Aligning a Service Provisioning Model of a Service-Oriented System with the ITIL v.3 Life Cycle

Bringing together the ICT and the business layer of a service-oriented system (SoS) remains a great challenge. Few papers tackle the management of SoS from the business and organizational point of view. One solution is to use the well-known ITIL v.3 framework. The latter enables to transform the organization into a service-oriented organizational which focuses on the value provided to the service customers. In this paper, we align the steps of the service provisioning model with the ITIL v.3 processes. The alignment proposed should help organizations and IT teams to integrate their ICT layer, represented by the SoS, and their business layer, represented by ITIL v.3. One main advantage of this combined use of ITIL and a SoS is the full service orientation of the company.Comment: This document is the technical work of a conference paper submitted to the International Conference on Exploring Service Science 1.5 (IESS 2015