Innovación de un nuevo sistema de gestión empresarial aplicación e implementación nuevo software a nivel nacional METROKIA S.A.

Kia Motors Corporation (KMC) tiene como objetivo desde hace algunos años, la creación e implementación de una solución de negocios enfocada en una gestión empresarial estandarizada a todos los distribuidores de Kia a nivel latinoamericano: Colombia, Perú, Ecuador y Chile. El proceso actual con el que cuentan los distribuidores en América Latina con sus concesionarios es enviar toda la información relacionada con los estatutos financieros a través de correo electrónico junto con una base de datos física, la cual se va archivando. El proceso es manual siendo de mucha dedicación y tiempo requerido para cumplir con las funciones pedidas. El enfoque actual de este proceso es claro: analizar el desempeño y rendimiento de cada uno de los concesionarios de la red junto con la identificación de oportunidades para mejorar. KMC junto con todos sus distribuidores están interesados en buscar un sistema de gestión empresarial sencillo, adecuado y de fácil manejo que permitirá únicamente a todos los concesionarios presentar sus estados de cuenta y desarrollo de una manera estandarizada a su distribuidor directamente. Entonces, el sistema deseado debe ser capaz de generar resultados basándose en lo comunicado por los distribuidores y proporcionar un conjunto de características bajo una adecuada funcionalidad para permitir a todos los usuarios de la red (concesionarios, distribuidores y KMC) analizar el rendimiento y desempeño de la empresa e identificar las áreas que requieren una mejora. En el siguiente documento, podremos ver el desarrollo que ha tenido METROKIA S.A para la creación y aplicación de una herramienta tecnológica (software) enfocada en lo mencionado anteriormente. Ha sido un proceso de varias etapas en donde tanto las variables como los indicadores de desempeño han tenido correcciones con el fin de poder ser leídos y entendidos fácilmente por toda la organización y red de concesionarios afiliados.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

An empirical study of process-related attributes in segmented software cost-estimation relationships

Parametric software effort estimation models consisting on a single mathematical relationship suffer from poor adjustment and predictive characteristics in cases in which the historical database considered contains data coming from projects of a heterogeneous nature. The segmentation of the input domain according to clusters obtained from the database of historical projects serves as a tool for more realistic models that use several local estimation relationships. Nonetheless, it may be hypothesized that using clustering algorithms without previous consideration of the influence of well-known project attributes misses the opportunity to obtain more realistic segments. In this paper, we describe the results of an empirical study using the ISBSG-8 database and the EM clustering algorithm that studies the influence of the consideration of two process-related attributes as drivers of the clustering process: the use of engineering methodologies and the use of CASE tools. The results provide evidence that such consideration conditions significantly the final model obtained, even though the resulting predictive quality is of a similar magnitude.

A 'Kimberley protest': diamond mining, export sanctions, and poverty in Awatia, Ghana

The temporary suspension of diamond exports in Ghana in 2006 and 2007 is arguably the most significant move to address mounting criticisms of the Kimberley Process Certification Scheme (KPCS), an international initiative aimed at stemming the flow of rough diamonds used to finance wars. The ban, which took effect in November 2006, was much praised, particularly in civil society circles, where it continues to be seen as a genuine effort to prevent the smuggling of ‘conflict diamonds’. At the time, Ghana was accused of harbouring stones originating from rebel-held territories in neighbouring Côte d’Ivoire. No evidence was found in support of the case that it was a repository for ‘conflict diamonds’, however, and exports resumed early in March 2007. This article examines the context for the accusations of Ghana’s implication in the smuggling of illicit diamonds, and draws on recent fieldwork to explain how the suspension has affected Akwatia, the country’s main diamondiferous area. The actions taken raise important questions about how suspected violators – particularly smaller diamond-producing nations – of the KPCS should be handled, and underscore how global compacts can have a host of negative repercussions at the village level.

Fully automated software solution for protein quantitation by global metabolic labeling with stable isotopes

Metabolic stable isotope labeling is increasingly employed for accurate protein (and metabolite) quantitation using mass spectrometry (MS). It provides sample-specific isotopologues that can be used to facilitate comparative analysis of two or more samples. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has been used for almost a decade in proteomic research and analytical software solutions have been established that provide an easy and integrated workflow for elucidating sample abundance ratios for most MS data formats. While SILAC is a discrete labeling method using specific amino acids, global metabolic stable isotope labeling using isotopes such as (15)N labels the entire element content of the sample, i.e. for (15)N the entire peptide backbone in addition to all nitrogen-containing side chains. Although global metabolic labeling can deliver advantages with regard to isotope incorporation and costs, the requirements for data analysis are more demanding because, for instance for polypeptides, the mass difference introduced by the label depends on the amino acid composition. Consequently, there has been less progress on the automation of the data processing and mining steps for this type of protein quantitation. Here, we present a new integrated software solution for the quantitative analysis of protein expression in differential samples and show the benefits of high-resolution MS data in quantitative proteomic analyses.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

A contribuição da fábrica de software e de seus produtos para o processo de flexibilização organizacional na empresa cliente

Os avanços tecnológicos, principalmente os relacionados tecnologia da informação das telecomunicações, transformaram as organizações sociedade, são objeto de conflitos empresariais devido às mudanças que produzem na vida das pessoas nas formas de trabalho. Este estudo procura analisar contribuição da TI, em especial dos produtos de software, na discutível mudança do paradigma fordista para pós-fordista. adoção dessas tecnologias pode se configurar como competência essencial fator de competitividade da empresa, no entanto os impactos dessa nova dinâmica empresarial que faz uso intensivo da TI carecem de maior estudo compreensão para verificar se nessa dinâmica está envolvido um processo efetivo de flexibilização ou apenas uma sistemática para redução de custos. trabalho foi baseado em um estudo de caso, por meio de pesquisa realizada com usuários de produtos de software em uma grande empresa do setor elétrico, para verificar contribuição desses produtos no processo de flexibilização organizacional. Os resultados indicam a existência de fatores facilitadores do processo de flexibilização apoiados na utilização de sistemas informatizados. Os produtos de software alteram de forma significativa processo de comunicação aproximam as pessoas entre diferentes níveis hierárquicos. No entanto, tecnologia ainda não utilizada de forma disseminada como recurso para flexibilizar as relações de trabalho, principalmente no que se refere execução de atividades em locais horários de trabalho não convencionais. Para operacionalização de um modelo de gestão flexível, com características pós-fordistas, há necessidade de desenvolvimento de um novo perfil nas relações de trabalho que ainda têm características do modelo fordista de produção.

Impact assessment and quality analysis of corporate social responsability (CSR) programs of mining companies and their contribution to sustainable development in the communities they operate: case study Tenke Fungurume Mining SA in the Democratic Republic of Congo (DRC)

Purpose – This case study presents an impact assessment of Corporate Social Responsibility (CSR) programs of the TFM Company in order to understand how they contribute to the sustainable development of communities in areas in which they operate. Design/Methodology/Approach - Data for this study was collected using qualitative data methods that included semi-structured interviews and Focus Group Discussions most of them audio and video recorded. Documentary analysis and a field visit were also undertaken for the purpose of quality analysis of the CSR programs on the terrain. Data collected was analyzed using the Seven Questions to sustainability (7Qs) framework, an evaluation tool developed by the Mining, Minerals and Sustainable Development (MMSD) North America chapter. Content analysis method was on the other hand used to examine the interviews and FGDs of the study participants. Findings - Results shows that CSR programs of TFM SA do contribute to community development, as there have been notable changes in the communities’ living conditions. But whether they have contributed to sustainable development is not yet the case as programs that enhance the capacity of communities and other stakeholders to support these projects development beyond the implementation stage and the mines operation lifetime need to be considered and implemented. Originality/Value – In DRC, there is paucity of information of research studies that focus on impact assessment of CSR programs in general and specifically those of mining companies and their contribution to sustainable development of local communities. Many of the available studies cover issues of minerals and conflict or conflict minerals as mostly referred to. This study addressees this gap.

A JML-Based strategy for incorporating formal specifications into the software development process

This thesis presents a JML-based strategy that incorporates formal specifications into the software development process of object-oriented programs. The strategy evolves functional requirements into a “semi-formal” requirements form, and then expressing them as JML formal specifications. The strategy is implemented as a formal-specification pseudo-phase that runs in parallel with the other phase of software development. What makes our strategy different from other software development strategies used in literature is the particular use of JML specifications we make all along the way from requirements to validation-and-verification.

SysPRE: systematized process for requirements engineering in knowledge discovery projects

The domain of Knowledge Discovery (KD) and Data Mining (DM) is of growing importance in a time where more and more data is produced and knowledge is one of the most precious assets. Having explored both the existing underlying theory, the results of the ongoing research in academia and the industry practices in the domain of KD and DM, we have found that this is a domain that still lacks some systematization. We also found that this systematization exists to a greater degree in the Software Engineering and Requirements Engineering domains, probably due to being more mature areas. We believe that it is possible to improve and facilitate the participation of enterprise stakeholders in the requirements engineering for KD projects by systematizing requirements engineering process for such projects. This will, in turn, result in more projects that end successfully, that is, with satisfied stakeholders, including in terms of time and budget constraints. With this in mind and based on all information found in the state-of-the art, we propose SysPRE - Systematized Process for Requirements Engineering in KD projects. We begin by proposing an encompassing generic description of the KD process, where the main focus is on the Requirements Engineering activities. This description is then used as a base for the application of the Design and Engineering Methodology for Organizations (DEMO) so that we can specify a formal ontology for this process. The resulting SysPRE ontology can serve as a base that can be used not only to make enterprises become aware of their own KD process and requirements engineering process in the KD projects, but also to improve such processes in reality, namely in terms of success rate.