Seeking meaning: A process approach to library and information services

Beyond Objectivism and Relativism: Science, Hermeneutics, and Praxis

Business Intelligence Applications and the Web: Models, Systems and Technologies summarizes current research advances in BI and the Web, emphasizing research solutions, techniques, and methodologies which combine both areas in the interest of building better BI solutions. This comprehensive collection aims to emphasize the interconnections that exist among the two research areas and to highlight the benefits of combined use of BI and Web practices, which so far have acted rather independently, often in cases where their joint application would have been sensible.

Business Intelligence Applications and the Web: Models, Systems and Technologies

With the rapid development of web technology, an increasing number of enterprises having been seeking for a method to facilitate business decision making process, power the bottom line, and achieve a fully coordinated organization, called business intelligence (BI). Unfortunately, traditional BI tends to be unmanageable, risky and prohibitively expensive, especially for Small and Medium Enterprises (SMEs). The emergence of cloud computing and Software as a Service (SaaS) provides a cost effective solution. Recently, business intelligence applications delivered via SaaS, termed as Business Intelligence as a Service (SaaS BI), has proved to be the next generation in BI market. However, since SaaS BI just comes in its infant stage, a general framework maybe poorly considered. Therefore, in this paper we proposed a general conceptual framework for SaaS BI, and presented several possible future directions of SaaS BI.

A Conceptual Framework for Business Intelligence as a Service (SaaS BI)

Business analysts frequently use Cockpits or Dashboards as front ends to data warehouses for inspecting and comparing multidimensional data at various levels of detail. These tools, however, perform badly in supporting a business analyst in his or her business intelligence task of understanding and evaluating a business within its environmental context through comparative data analysis. With important business knowledge either unrepresented or represented in a form not processable by automatic reasoning, the analyst is limited in the analyses that can be formulated and she or he heavily suffers from information overload with the need to re-judge similar situations again and again, and to re-discriminate between already explained and novel relationships between data. In an ongoing research project we try to overcome these limitations by applying and extending semantic technologies, such as ontologies and business rules, for comparative data analysis. The resulting Semantic Cockpit assists and guides the business analyst due to reasoning about various kinds of knowledge, explicitly represented by machine-processable ontologies, such as organisation-internal knowledge, organisation external domain knowledge, the semantics of measures and scores, knowledge about insights gained from previous analysis, and knowledge about how to act upon unusually low or high comparison scores. This paper outlines the architecture of the Semantic Cockpit and introduces its core ideas by a sample use case.

Semantic cockpit: an ontology-driven, interactive business intelligence tool for comparative data analysis

Despite the importance of analytical tools to organizations, they still lack the inference power needed to solve the requests of decision makers in a flexible and smarter way. We present a framework called SBI -- Semantic Business Intelligence - in which we apply ontologies on the description of business rules and concepts in order to support semantic-analytical functionalities that extend traditional OLAP operations. Such approach enables developers to customize BI solutions according to organizations' specific analytical requirements and allows developers align BI solutions to the latest business analytic requirements. We present how semantic inference is supported using batch and on-the-fly based strategies. In addition, we show how such semantic infrastructure makes the access to heterogeneous data sources transparent. Finally, we illustrate the benefits of our approach through Extracta, an analytical tool that relies on SBI ontologies and modules. Extracta enables an easy access to information and provides novel exploratory funcionalities based on semantics towards faster and smarter decisions.

SBI: a semantic framework to support business intelligence

A memoria organizacional pode ser definida como um sistema que armazena as experiencias vividas da organizacao. E o conhecimento de como fazer as coisas, a forma de abordar os problemas e questoes. A memoria organizacional se preocupa com a reutilizacao e compartilhamento de este conhecimento, mas a construcao de sistemas de informacao de memoria organizacional nao e uma atividade meramente tecnologia, requer de analises de processos intensivos em conhecimento. Por outro lado, engenharia do conhecimento centra sua atencao na aplicacao de metodologias e tecnicas visando adquirir, estruturar, formalizar e operacionalizar informacoes e conhecimentos existentes em dominios de problemas intensivos em conhecimento. Neste artigo descreve-se a memoria organizacional a partir da engenharia do conhecimento, analisando-se os metodos e tecnicas que podem ser usados para suportar os processos envolvidos em torno a memoria organizacional. A engenharia do conhecimento fornece um conjunto de ferramentas que podem ser usadas para a aquisicao, organizacao e recuperacao do conhecimento assim como para suportar o processo de desenvolvimento de este tipo de sistemas.

A Memória Organizacional no Contexto da Engenharia do Conhecimento

improve IR systems regarding its retriev al and presentation of information, which make the task of finding information more effective, efficient, and interact ive. In this paper we argue that ontologies also greatly improve the e ngineering of such systems. We created a framework that uses ontology to drive the process of engineering an IR system. We develop ed a prototype that shows how a domain specialist withou t knowledge in the IR field can build an IR system with interac tive components. The resulting system provides support for users not only to find their information needs but also to ex tend their state of knowledge. This way, our approach to ontology-enabled information retrieval addresses both the engineerin g aspect described here and also the usability aspect descri bed elsewhere.

Hermeneus: An Architecture for an Ontology-Enabled Information Retrieval

The information requirement in the health area and the increase difficult for knowledge retrieval inside this domain, demands the existence of tools to support the processing of large amount of existing texts in the information repositories sources. Addressing this problem this work introduces an architecture aiming to support knowledge retrieval process from databases using annotated images in the anatomical domain. The architecture was implemented using five layers (the domain ontology, the annotation, the support, the retrieval and visualization layers) based on the Foundational Model of Anatomy and the Unified Medical Language System, reference ontologies in the biomedical domain. This architecture improves substantially the knowledge retrieval due to the use of images that link its regions with concepts of biomedical ontologies. The architecture proposed can be generalized and applied in other domains employing other reference ontologies.

Knowledge retrieval in the anatomical domain

This paper presents a model that aims to facilitate the visualization of the knowledge stored in digital repositories using visual archetypes. Archetypes are structures that contain visual representations of the real world that are known a priori by the target group, and which have semantic structures for identifying the entities of the domain represented in each region. The proposed model is supported by the framework for knowledge visualization proposed by Burkhard and describes the users’ interactions with visual archetypes. The user through the archetypes can retrieve and view the knowledge related to the entities represented in the archetypes’ images. A prototype was developed to demonstrate the feasibility of the model using archetypes in the biomedical field, the Foundational Model of Anatomy and the Unified Medical Language System as domain knowledge and the Scientific Electronic Library Online database as a document repository. The use of visual representations in archetypes facilitates the dissemination of knowledge, because these are part of the world view of users and can easily be related with prior knowledge. Visual representations are processed quickly in the brain and require less effort than the processing of textual information.

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Fabiano Duarte Beppler

Papers

SBI: a semantic framework to support business intelligence

A Memória Organizacional no Contexto da Engenharia do Conhecimento

Hermeneus: An Architecture for an Ontology-Enabled Information Retrieval

Knowledge retrieval in the anatomical domain

Um modelo para a visualização do conhecimento baseado em arquétipos visuais