Analytiikkajärjestelmän käyttöönottoprojekti, kustannukset ja kustannusten hallinta

Perinteisten kilpailuetujen katoaminen ja kilpailun kiristyminen haastavat yrityksiä etsimään keinoja kilpailukyvyn säilyttämiseksi. Tietotekniikan nopea kehitys ja liiketoiminnassa syntyvän datan määrän kasvu luovat yrityksille mahdollisuuden hyödyntää analytiikkaa päätöksenteon tukena ja liiketoiminnan tehostamisessa. Työ on kirjallisuuskatsaus ja sen tavoitteena on selvittää analytiikkajärjestelmän käyttöönottoprojektin vaiheet, käyttöönottoon liittyvät kustannukset ja miten kustannuksia voidaan hallita. Lisäksi esitetään tiivis katsaus analytiikan kehitykseen ja nykytilaan sekä tarkastellaan hankintamalleja, hankkeiden taloudellista arviointia ja käyttöönottoprojektin kriittisiä menestystekijöitä. Käyttöönottoprojekti on monivaiheinen ja se alkaa liiketoiminnan analysoinnista sekä järjestelmän suunnittelusta ulottuen aina sen toteutukseen ja jälkiarviointiin. Käyttöönottoon liittyy useita kustannuseriä, joita voidaan luokitella niiden ominaisuuksien perusteella. Projektin kustannusten hallinnan prosesseja ovat kustannusten hallinnan suunnittelu, kustannusten arviointi, budjetin määrittäminen ja kustannusten valvonta, jotka limittyvät käyttöönoton vaiheiden kanssa.

Aplicação das tecnologias de informação e comunicação como estratégia para a retenção de aunos

Este artigo aborda a aplicação das Tecnologias de Informação e Comunicação como estratégias de boas práticas que conduzem à retenção e captura de alunos do ensino superior. É baseado num estudo de caso de oito anos, de utilização de um completo sistema de informação. Este sistema, além de constituir um ERP, de suporte às actividades de gestão académica, dispõe ainda de uma forte componente de SRM que confere suporte às actividades administrativas e lectivas. É descrito em que medida o sistema apresentado facilita a interacção e comunicação entre os membros da comunidade académica, recorrendo à internet, com serviços disponíveis na Web complementando-os com correio electrónico, SMS e CTI. Através de uma percepção, sustentada por análise empírica e por resultados de inquéritos, demonstra-se como este tipo de boas práticas pode elevar o nível de satisfação da comunidade. Muito em particular, é possível combater o insucesso escolar, evitar que alunos abandonem os seus cursos antes do seu término e que os recomendem a potenciais alunos. Em complemento, este tipo de estratégia permite ainda fortes economias na gestão da instituição, elevando o seu valor. Como trabalho futuro, é apresentada a nova fase do projecto que envereda pela aplicação de Business Intelligence para optimização do processo de gestão, tornando-o pró-activo. Também é apresentada a visão tecnológica que orienta os novos desenvolvimentos, para uma arquitectura baseada em serviços Web e linguagens de definição processual.

Recomendr-entity recommendation based on ad-hoc dimensions

The growing availability and popularity of opinion rich resources on the online web resources, such as review sites and personal blogs, has made it convenient to find out about the opinions and experiences of layman people. But, simultaneously, this huge eruption of data has made it difficult to reach to a conclusion. In this thesis, I develop a novel recommendation system, Recomendr that can help users digest all the reviews about an entity and compare candidate entities based on ad-hoc dimensions specified by keywords. It expects keyword specified ad-hoc dimensions/features as input from the user and based on those features; it compares the selected range of entities using reviews provided on the related User Generated Contents (UGC) e.g. online reviews. It then rates the textual stream of data using a scoring function and returns the decision based on an aggregate opinion to the user. Evaluation of Recomendr using a data set in the laptop domain shows that it can effectively recommend the best laptop as per user-specified dimensions such as price. Recomendr is a general system that can potentially work for any entities on which online reviews or opinionated text is available.

Modelo de gestão do conhecimento para a unidade organizacional de saúde

Nos últimos anos têm surgido vários debates e estudos sobre a importância do conhecimento. As organizações consideram a Gestão do Conhecimento (GC) uma vantagem competitiva, capaz de gerar riqueza e poder. Para tal necessitam de desenvolver mecanismos e de ter pessoas com capacidade de criar, compartilhar e disseminar conhecimentos na organização. As Tecnologias de Informação e os Sistemas de Informação são uma ferramenta de grande impacto na GC, pois assumem um papel importante no sucesso e na renovação dos conhecimentos. A partir do Modelo de GC desenvolvido por Nonaka e Takeuchi, o Modelo Metavisão, e as técnicas de Business lntelligence, elaborou-se um Modelo de Gestão do conhecimento para a Unidade de Saúde. Com este modelo pretende-se obter beneficies, que passam pelo desenvolvimento de mecanismos de comunicação interna, formação e melhorias no processo de tomada de decisão. ABSTRACT; During the last years, debates and studies have arisen in relation to the importance of knowledge. The organizations consider Knowledge Management a competitive advantage, capable of generating wealth and power. Therefore, new mechanisms have to be developed in the organizations and employ people with capacity to create, share and disseminate knowledge. The information and Communication Technology is an important tool and has a great impact on Knowledge Management because it assumes an important role in the success and the renovation of knowledge. The Model of Knowledge Management developed by Nonaka and Takeuchi, the "Metavision" Model and the techniques of Business intelligence were the starting point to elaborate a Model of Knowledge Management for a Health Unit. With this model we aim to obtain benefits, such as development of mechanisms of internal communication, training plans and improvement during the process of decision making.

Insights

The South Carolina Department of Employment and Workforce Business Intelligence Department monthly publishes Insights in conjunction with the U.S. Department of Labor, Bureau of Labor Statistics. The monthly newsletter provides economic indicators, employment rates and changes by county, nonfarm employment trends, and other statistics.

Uma arquitetura de referência para o processamento distribuído de stream de dados em soluções analíticas de near real-time

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Virtual Enterprise Process Management: An Application to Industrial Maintenance

Part 2: Behaviour and Coordination

Inteligencia de negocios aplicando la metodología RFM a las cuentas de los Socios de la COAC Jardín Azuayo

El presente proyecto:Inteligencia de negocios, aplicando la metodología RFM a las cuentas de los socios de la COAC Jardín Azuayo, se desarrolla sobre la necesidad de la institución de contar con herramientas eficientes y eficaces para la toma de decisiones y conocimiento del socio. Primero, se determina la importancia de construir una herramienta de Inteligencia de Negocios dentro de Jardín Azuayo que permita obtener información clara y concisa en tiempo real para la toma de decisiones. Segundo, se continúa con el desarrollo de metodologías para la gestión del valor del socio a través del conocimiento de sus necesidades analizando la información histórica de su última transacción realizada, la frecuencia con la que acude para acceder a los servicios que ofrece la Cooperativa y el monto promedio por transacción. Finalmente, al combinar la herramienta de Inteligencia de Negocios para la obtención de información y la aplicación de metodologías para el conocimiento del socio, se ha podido plantear dos estrategias básicas para la afianzar la fidelización del socio con la Cooperativa.

V for variety: Lessons learned from complex smart cities data harmonization and integration

With emerging trends for Internet of Things (IoT) and Smart Cities, complex data transformation, aggregation and visualization problems are becoming increasingly common. These tasks support improved business intelligence, analytics and enduser access to data. However, in most cases developers of these tasks are presented with challenging problems including noisy data, diverse data formats, data modeling and increasing demand for sophisticated visualization support. This paper describes our experiences with just such problems in the context of Household Travel Surveys data integration and harmonization. We describe a common approach for addressing these harmonizations. We then discuss a set of lessons that we have learned from our experience that we hope will be useful for others embarking on similar problems. We also identify several key directions and needs for future research and practical support in this area.

Monitor objectivity with important clients: evidence from auditor opinions around the world

We examine whether monitors are likely to compromise their monitoring objectivity in the face of economically important clients in international business settings. In the context of external auditing and assurance services, we measure monitor objectivity by whether auditors are more (or less) likely to issue to their important clients modified audit opinions, that is, audit opinions provided to outside investors about the firm that demotes explicit areas of concern. Using a large cross-country sample, we document that auditors are more likely to issue modified opinions to their economically important clients relative to other clients. Furthermore, we find that this association is stronger (1) for Big N auditors, (2) for multinational audit clients, and (3) in countries with stronger legal regimes. These results suggest that monitors prioritize the protection of their reputation over lucrative economic relationships, and such information certification function is more pronounced for international auditors, multinational client firms, and in strong legal regimes.

Aspectos introdutórios de um modelo baseado no indivíduo para estudo da dinâmica espacial do HLB do citros.

RESUMO - O Huanglongbing (HLB) é uma doença incurável que afeta plantas de citros em todo o país. Como o Brasil é um dos maiores produtores de citros do mundo, essa doença pode causar um grande impacto econômico na agricultura brasileira. Visando contribuir para novas estratégias de controle da doença, estão sendo realizados estudos focados na modelagem baseada no indivíduo (MBI) para avaliar a propagação espaço-temporal da doença em áreas de plantio com a presença de um novo hospedeiro alternativo mais atrativo. Este trabalho tem como objetivo desenvolver a estrutura computacional de um MBI, utilizando o software R e o pacote Shiny que possibilita executar as simulações via web, a partir de premissas e estudos biológicos prévios da doença. As simulações iniciais indicam que a estrutura computacional concebida possibilita uma melhor visualização da progressão da doença, bem como facilita o teste de diferentes geometrias de plantio envolvendo os hospedeiros principal e alternativo.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Granule-based knowledge representation for intra and inter transaction association mining

Abstract With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis. Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules. To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Hot topic detection in news blogs from the perspective of W2T

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.