Ontology Based Statistical Automated Inference - New Approach to Artificial Intelligence

Statistical analysis requires understanding the nature of the phenomenon under study, as well as understanding sense of mathematical statistics. Bridging the gap between semantic web based on knowledge representation languages, and concepts described by mathematical formula is a challenge for AI. In order to overcome this gap the ontology language P-ONT (based on directed graph) has been invented. To illustrate the capabilities of the P-ONT language, semantic web (built on the P-ONT ontology) OLAP cube, relational data bases and generalized hierarchical statistical regression models are presented

HaoLap: a Hadoop based OLAP system for big data

International audienceIn recent years, facing information explosion, industry and academia have adopted distributed file system and MapReduce programming model to address new challenges the big data has brought. Based on these technologies, this paper presents HaoLap (Hadoop based oLap), an OLAP (OnLine Analytical Processing) system for big data. Drawing on the experience of Multidimensional OLAP (MOLAP), HaoLap adopts the specified multidimensional model to map the dimensions and the measures; the dimension coding and traverse algorithm to achieve the roll up operation on dimension hierarchy; the partition and linearization algorithm to store dimensions and measures; the chunk selection algorithm to optimize OLAP performance; and MapReduce to execute OLAP. The paper illustrates the key techniques of HaoLap including system architecture, dimension definition, dimension coding and traversing, partition, data storage, OLAP and data loading algorithm. We evaluated HaoLap on a real application and compared it with Hive, HadoopDB, HBaseLattice, and Olap4Cloud. The experiment results show that HaoLap boost the efficiency of data loading, and has a great advantage in the OLAP performance of the data set size and query complexity, and meanwhile HaoLap also completely support dimension operations

Towards a model for the multidimensional analysis of field data

International audienceIntegration of spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Usually, SOLAP models exploit discrete spatial data. Few works integrate continuous field data into dimensions and measures. In this paper, we provide a multidimensional model that supports measures and dimension as continuous field data, independently of their implementation

Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales

Optimization Research of the OLAP Query Technology Based on P2P

With the increasing data of the application system, the fast and efficient access to the information of support decision-making analysis has become more and more difficult and the original OLAP technology have also revealed many shortcomings. Using the method of P2P network technology and OLAP storage query and query method, the paper has constructed a distributed P2P-OLAP network model and put forward the storage and sharing scheme of multidimensional data, OLAP query scheme based on collaboration support. Finally, the paper has shown that the scheme can effectively improve the performance of decision analysis by the experiment

Transformação digital: integração de ferramentas BI com CRM e dados de vendas

In recent years, technological advancements and the escalating volume of data generated have encouraged companies to adopt new and improved management procedures, supported by business software able to accommodate this new reality. Business Intelligence (BI) tools serve this purpose, with the goal of providing businesses with the ability to extract useful information from available data, enabling them to position themselves in the target market with a greater understanding of the challenges and what can be done to achieve better results. BI involves the collection, processing, cleansing, and storage of data, as well as the implementation of analytical tools. Following this, as part of a comprehensive Digital Transformation (DT) project, Amorim, in particular the Amorim Cork SGPS (AC-SGPS) business unit, has observed a constant increase in the data flow and volume, as well as a growing need to maximize data utility. Consequently, one of the Amorim’s primary objectives is to invest in BI tools that support and simplify data analysis. In this context, the primary objective of my internship project was to integrate data from a CRM tool and the currently implemented ERP system into PowerBI (PBI) in order to facilitate the analysis of existing data. The project was divided into two case studies - Customer Service departments and Commercial departments of two Amorim group companies - where the PBI was implemented independently. Each case began with an analysis of the department’s data structure, followed by the collection of initial requirements from stakeholders, and concluded with the development and implementation of the solution. Special attention was deposited on the continuous participation of stakeholders throughout the development process so that they could make optimal use of the BI tools after implementation. Subsequently, a survey was conducted with the end users in order to collect and analyze the results and inquire the added value to the covered companies. According to the members of the departments, the new access to information is clearly superior to the methods previously used, as it makes the information easier to locate and contributes to a more independent and productive method of working. In addition, the significance of implementing this type of tool for monitoring and correcting processes from a factual and quantifiable data perspective while supporting the decision-making process was emphasized. Consequently, it is anticipated that the project will contribute, on the one hand, to a reduction in costs, as a result of increased productivity and a faster and more effective decision-making process, and, on the other hand, to an increase in revenues and profits, as a result of increased customer retention and attraction, as well as greater user satisfaction and motivation. Together, these contributions are intrinsically linked to the success of AC-SGPS’s multi-departmental DT project’s business strategy and long-term objectives.Nos últimos anos, os avanços tecnológicos e a crescente quantidade de dados gerados levaram as empresas a adotar novos e melhores procedimentos de gestão, apoiados por software empresarial com capacidade de fazer frente a esta nova realidade. É aqui que entram as ferramentas de Business Intelligence (BI), com o objetivo de proporcionar às empresas a capacidade de extrair informação útil dos dados disponíveis, permitindo que se posicionem no mercado alvo com uma maior compreensão dos desafios e do que pode ser feito para alcançar melhores resultados. BI engloba métodos de recolha, tratamento, limpeza e armazenamento de dados e a implementação de ferramentas analíticas. Neste seguimento, como parte de um projeto abrangente de Transformação Digital (TD), a Amorim, e em particular a unidade de negócios Amorim Cork SGPS (AC-SGPS), tem notado um aumento constante no fluxo e volume de dados, associado a uma necessidade crescente de tirar o máximo partido dos mesmos. Consequentemente, um dos principais objetivos do grupo Amorim passa pelo investimento em ferramentas de BI que tornem a análise de dados mais fácil e intuitiva. Neste contexto, o foco do meu projeto de estágio passou pela integração de dados de uma ferramenta CRM e do sistema ERP, atualmente implementados, com Power BI (PBI), a fim de simplificar e melhorar a análise da informação existente. O projeto foi dividido em dois casos de estudo - departamentos de Serviço de Apoio ao Cliente e departamentos Comerciais de duas empresas do grupo Amorim -, onde a implementação do PBI foi realizada separadamente. Para cada caso, começou-se por realizar uma análise da estrutura de dados da empresa, tendo-se seguido uma recolha de requisitos iniciais dos stakeholders, culminando com o seu desenvolvimento e implementação. Durante todo o processo de desenvolvimento, foi dada prioridade à participação constante dos stakeholders para que, numa fase pós-implementação, pudessem fazer o melhor uso possível das ferramentas BI. Posteriormente foi realizado um inquérito aos utilizadores como forma de recolher e analisar os resultados e averiguar o valor acrescentado às empresas abrangidas. De um ponto de vista geral, segundo os membros dos departamentos, o novo acesso à informação é claramente superior aos métodos utilizados até então, tornando-a mais fácil de encontrar e consequentemente contribuindo para um método de trabalho mais independente e produtivo. Foi também salientada a importância da implementação deste tipo de ferramenta na monitorização e correção de processos a partir de uma perspetiva factual e quantificável dos dados, ao mesmo tempo que apoia o processo de tomada de decisão. Por conseguinte, prevê-se que o projeto venha a contribuir, por um lado para uma redução dos custos, resultante de uma maior produtividade e de um processo de tomada de decisão mais rápido e eficaz, e por outro lado para um aumento das receitas e dos lucros, como resultado de uma maior retenção e atracão de clientes, a par de uma maior satisfação e motivação dos utilizadores. Estas contribuições, no seu conjunto, estão intrinsecamente ligadas ao sucesso da estratégia empresarial e dos objetivos a longo prazo definidos no projeto multidepartamental de TD da AC-SGPS.Mestrado em Engenharia e Gestão Industria

A Comparison of OLAP and Coherence Large Scale Aggregations

The goal of the project is to develop a generic mechanism for cache-based aggregation of data. This will allow the end user to have a summary view with multiple pivot points. To achieve this goal, the following steps will be taken: 1. Converting the database to files 2. Loading the files into caches 3. Aggregating the data in the caches Coherence and aggregators will be used to complete these tasks. To write the code for these processes, Java, C# and SQL are the programming languages that will be used

EXPLANATORY ANALYSIS IN BUSINESS INTELLIGENCE SYSTEMS

In this paper we describe a method for the discovery of exceptional values in business intelligence (BI) systems, in particular OLAP information systems. We also show how exceptional values can be explained by underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of different views and managerial reporting facilities. The purpose of the methods and algorithms presented here, is to extend OLAP based systems with more powerful analysis and reporting functions. We describe how exceptional values at any level in the data, can be automatically detected by statistical models. Secondly a generic model for diagnosis of atypical values is realized in the OLAP context. By applying it, a full explanation tree of causes at successive levels can be generated. If the tree is too large, the analyst can use appropriate filtering measures to prune the tree to a manageable size. This methodology has a wide range of applications such as interfirm comparison, analysis of sales data and the analysis of any other data that possess a multi-dimensional hierarchical structure. The method is demonstrated in a case study on financial data

Building a Data Warehouse step by step

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be properly achieved from operational systems. This paper presents the ways in which a data warehouse may be developed and the stages of building it.data warehouse, data mart, data integration, database management system, OLAP, data mining