Quantitative analysis of data warehouse design quality

Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making.

Efficient data warehouse (DW) design depends on the quality of a conceptual model. In this paper, a set of quality metrics have been proposed along with the theoretical validation for quality measurement of conceptual level object-oriented multidimensional data model. The paper also describes the designer level and user level viewpoints of quality evaluation through the criteria like complexity, completeness, expressiveness and analysability. Finally, the work focuses on the empirical validation of the set of metrics and measurements. The empirical validation process shows the usefulness of the proposed metrics for the assessment of operability factor of conceptual level multidimensional data model. Significant correlations exist among the proposed quality measurements and operability factors. The proposed quality evaluation framework is verified in context of a graph semantic-based object-oriented multidimensional data model GOOMD.

Data analysis has become a vital requirement in various fields. The results of data analysis can be used as a data warehouse design that is expected to help in solving problems, evaluating learning outcomes and supporting decision making. In the field of education, student achievement is a reference for achieving quality learning. This success can be seen from the achievement of student learning completeness that is the basis for evaluating and making decisions. The data warehouse design can be used as a basis in knowing student learning progress that can be seen from the value obtained. The diversity of data makes managing values difficult and delayed, data warehouse design using Pentaho can help and simplify data integration. The results of the analysis and design of the data warehouse will be presented in a multidimensional form that can be seen through dimension tables and fact tables.

Conceptual modeling is widely recognized to be the necessary foundation for building a database that is well-documented and fully satisfies the user requirements. In particular, from the designer point of view the availability of a conceptual model provides a higher level of abstraction in describing the warehousing process and its architecture in all its aspects. Typically conceptual models rely on a graphical notation that facilitates writing, understanding, and managing conceptual schemata by both designers and users. The Entity/Relationship (E/R) model (Chen, 1976) is widespread in the enterprises as a conceptual formalism to provide standard documentation for relational information systems; nevertheless, as E/R is oriented to support queries that navigate associations between data rather than synthesize them, it is not well-suited for data warehousing (Kimball, 1998). Actually, the E/R model has enough expressivity to represent most concepts necessary for modeling a Data Warehouse (DW); on the other hand, in its basic form, it is not able to properly emphasize the key aspects of the multidimensional model, so that its usage for DWs is expensive from the point of view of the graphical notation and not intuitive (Rizzi, 2006). Some designers claim that star schemata are expressive enough for conceptual modeling. Actually, a star schema is just a (denormalized) relational schema, so it merely defines a set of relations and integrity constraints. Using star schema for conceptual modeling is like starting to build a complex software by writing the code, without the support of any static, functional, or dynamic model, which typically leads to very poor results from the points of view of adherence to user requirements, maintenance, and reuse. For all these reasons, in the last few years the research literature has proposed several original approaches for modeling a DW, some based on extensions of known conceptual formalisms (e.g. E/R, Unified Modeling Language (UML)), some based on ad hoc ones. Remarkably, a comparison of the different models made by Abello (2006) pointed out that, abstracting from their graphical form, the core expressivity is similar, thus proving that the academic community reached an informal agreement on the required expressivity. This paper discusses the expressivity of an ad hoc conceptual model, the Dimensional Fact Model (DFM), in order to let the user verify the usefulness of a conceptual modeling step in DW design. After a brief listing of the main conceptual model proposals, the basic and advanced features in DW conceptual modeling are introduced and described by examples. Finally, the current trends in DW conceptual modeling are reported and the conclusions are drawn.

A company, majorly company that active in commercial (profit orientation) need to analyze their sales performance. By analyzing sales performance, company can increase their sales performance. One of method to analyze sales performance is by collecting historical data that relates to sales and then process that data so that produce information that show company sales performance. A data warehouse is a set of data that has characteristic subject oriented, time variant, integrated, and nonvolatile that help company management in processing of decision making. Design of data warehouse is started from collecting data that relate to sales such as product, customer, sales area, sales transaction, etc. After collecting the data, next is data extraction and transformation. Data extraction is a process f or selecting data that will be loaded into data warehouse. Data transformation is making some change to the data afte r extracted to be more consistent. After transformation processing, data are loaded into data warehouse. Data in data warehouse is processed by OLAP (On Line Analytical Processing) to produce information. Information that are produced from data processing by OLAP are chart and query reporting. Chart reporting are sales chart based on cement type, sales chart based on sales area, sales chart based on plant, monthly and year ly sales chart, and chart based on customer feedback. Query reporting are sales based on cement type, sales area, plant and customer. Keywords: Data warehouse; OLAP; Sales performance analysis; Ready mix market

As necessidades de informações para tomadas de decisão ocorrem em qualquer tipo de empresa. Informações de baixa qualidade podem prejudicar estas decisões. Empresas de manufatura baseiam muitas decisões nas informações sobre a produção realizada, que é gerada pelo processo de controle da produção. Os sistemas de informação normalmente utilizados para suporte a este processo são limitados para a geração de informações abrangentes e integradas. O objetivo deste artigo é apresentar a proposta de um Data Warehouse (DW) para geração de informações de suporte ao processo de controle da produção, com a garantia de melhoria na qualidade da informação disponibilizada aos usuários. Para isto, serão analisadas quais informações devem ser disponibilizadas, com base no alinhamento entre o desenvolvimento do DW e a estratégia de manufatura da empresa. O projeto do DW será baseado em algumas atividades que garantem a qualidade da informação. Como resultado, será apresentada uma proposta de melhoria da qualidade da informação no controle da produção como uma seqüência de etapas para o desenvolvimento do DW.

The data warehouse is based on the development of relational databases, parallel processing and distributed processing technologies, as well as online analytical processing technologies. SAP BW is widely used in many enterprise data warehouses. It can issue reports of enterprises at the group level, manage the group's business operations, help find the laws and trends behind the performance of corporations and ultimately provide decision-making support for decision makers of enterprises. Therefore, how to use the SAP BW model architecture to better realize enterprise data warehouses is one of the hot topics in the data warehouse industry today. For a large corporation, its branches and subsidiaries have deployed multiple sets of enterprise data warehouses. At the same time, because there are multiple sets of data warehouse systems, when the data model needs to be modified at the group level, the data model of the branch also needs to be modified accordingly, and the flexibility of maintenance is poor. Finally, every time the corporation needs to upgrade the version of the data warehouse, it needs to maintain and upgrade the version of the data warehouse of each branch and the maintenance cost is high. Therefore, how to integrate multiple sets of data warehouses distributed in various subsidiaries into a corporation enterprise data warehouse by designing the model hierarchy, so as to ensure the reliability of data, enhance the flexibility of model modification and reduce the cost of data warehouse. This paper completes the design of an enterprise data warehouse of a large corporation, which can be used for the summary and collation of data from multiple branches such as finance and sales of a corporation, effectively improving the efficiency of data utilization and ensuring the authenticity of data. At the same time, through hierarchical design, the problems of poor flexibility in model modification and high system maintenance costs are solved.

The proliferation of projects that are part of the semantic Web is truly impressive. In fact, ontologies become increasingly present in information systems, they constitute great data sources that arouse the interest of being analyzed. Ontologies are used for standardizing, structuring and formalizing the Web, Web Service, E-learning systems, and other fields. Regarding multidimensional approaches, researches in this field have focused on direct Data Warehouse conception from an ontology, which do not integrate the intervention of the expert in this process. In this case, the transformations are global and not very customizable; it can reproduce the inherent defects from the data sources into the resulting data warehouse. In this paper, we propose a new multi-approach model based on the coupling of relational database design approaches from an ontology with Data Warehouse design approaches from a relational database. Our model is semi-automatic allowing Data Warehouse design from an ontology by giving the designer more ability to intervene in this process and closely control the transformations. To assess the usefulness of our approach, we evaluated it by applying it on an example case study. The results of the example show that our approach is more accurate in terms of useful data filtering and adaptation of the multidimensional model to the end-users business-needs.

Grants is a program launched by the government as a means to develop the potential of the region or contribute to society in the form of money or objects In submitting as Grant there are many sources of data stored in the system e -Grant, The amount of the proposal Grant is very much in each period so as to be reorganized, One solution for the storage of e-submission of the Grant is to build a data warehouse, Due to the technological development of data warehouse will be able to support the creation of a single access door managerial parties in obtaining information in the data warehouse, The design of the data warehouse were calculated using a four step Kimball, Methods Kimball 4 step has the advantages of a more compact, dynamic, and supports the creation of a data warehouse that is structured output of the data warehouse to the e-Grant in the form of Reports Grant and a recapitulation of Grant where reports Grant is being used as an attachment to a defense in response to the report Expenditure Provincial Bali whereas Summary of Grants used for List SKPD to evaluate the Grant recipient.

In data warehouse (DW) development, a series of mappings must be specified between user concepts and data source elements, in order to identify which sources must undergo an integration process. Until now, these mappings are either assumed to be implied by name matching or identified according to the designer's experience. Then, the result is implemented as Extraction/Transformation/Loading (ETL) processes. Since ETL processes relate elements at the logical level, designers cannot adequately analyze how a change in requirements or in the data sources affects the analysis capabilities. Furthermore, this approach makes it difficult to perform incremental changes in DW design, requiring in some cases to perform the whole analysis again. In this paper we present a set of semantic mappings that relate user concepts specified by requirements to those obtained from data sources. In turn, this allows us to accurately identify how any potential change affects the different structures and ETL processes. As a DW evolves over time, our approach easily allows us to incorporate new concepts, as well as any change introduced at requirements or data sources into the DW repository with no need to redesign the whole DW. In order to show the application of our proposal, we show a real case study focusing on the Digital library of the University of Alicante.

A data warehouse is a data repository which collects and maintains a large amount of data from multiple distributed, autonomous and possibly heterogeneous data sources. Often the data is stored in the form of materialized views in order to provide fast access to the integrated data. However, to maintain the data in the warehouse consistent with the source data is a challenging task in a multiple remote source environment. Transactions containing multiple updates at one or more sources further complicate the consistency issue. In this paper we first consider improving the refresh time of select-project-join (SPJ) type materialized views in a data warehouse by presenting a frequency-partitioned based algorithm, which takes into account the source update frequencies and the total space for auxiliary data. We then propose a solution in the design of data warehouses which can handle a variety of materialized views with different refreshment requirements.

Data warehousing has recently emerged as a possible solution to enable end user access to the increasing volumes of business oriented data. The foundation of any modern decision support architecture is the data warehouse. In this paper we will give a framework for the use of data warehouses in the tourism domain. The framework will cover the whole life cycle of the design, specification and application of data warehouses and their use in tourist information systems (TIS) and strategic decision support systems in tourism. Since many tourist information systems offer WWW-interfaces based on HTML-files and Java applets, we consider to use internet/intranet as the end user interface for any analysis, report and retrieval operation on the data warehouse. Special efforts will be given on the potential of data mining techniques, which are used on top of modern data warehouses

Multi-company companies have challenges in managing subsidiary data into performance information of all subsidiaries in one window. This is due to the variation of data dimensions according to the business processes of each subsidiary. CV. XYZ is a holding company engaged in the food & beverage business. CV. XYZ currently manages three companies consisting of restaurants, catering, and tent and decoration rental. The problem faced by the company owner is the limited access to company performance in one information window. Each company has working papers in Excel format to record expenditure and income transactions. This article proposes the development of a website-based business intelligence system to overcome the problems of CV. XYZ. The purpose of developing a business intelligence system in this article is to provide access to the performance of each subsidiary in one website media. The business intelligence system is developed through the stages of data collection and analysis, data warehouse design, ETL process, and data visualization with Microsoft Power BI. The data warehouse design uses Kimball's nine-step method which produces a data warehouse with a star scheme. The developed Business Intelligence system was tested using the UAT method. The UAT test results show that the system development is following the company's needs as indicated by the UAT score of 92%

While data warehousing (DW) has emerged as a key component of many organizations' information systems, few studies have assessed companies' DW practices. This research examines a range of DW development and management issues. Data collected from more than two dozen large companies suggest that the DW concept has been implemented quite differently across enterprises, and that DW practices are still at an early stage of development. The results are also compared across two different DW architecture types, the hub & spoke and federated data mart approaches. This analysis suggests that the choice of architecture appears to have an important effect on a number of DW development and management measures. The results of this study should be useful to companies looking to initiate or expand their DW operations and to researchers in understanding the current scope and operations of companies' data warehousing efforts.

Big data represents a new technology for managing data with high velocity, volume, variety and contributes to creating value for companies. As quoted in1, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends. The Big Data Era has largely contributed in accelerating the development strategic plans issued from governments and research organisms, coving the management, exploitation and analysis of these data by taking into account the different V's of Big Data. Among these plans, we can cite for instance the development of: (a) large-scale platforms (ex. data-clusters, distributed data clusters), (b) Software Defined Environments (SDE) (ex. IBM SDE), (c) advanced programming paradigms (ex. map-reduce, Spark, etc.), Data Analytics Tools (Rapid Miner, Google Fusion Tables, Solver), (d) Visualization tools (Google Chart, Tableau, Oracle Visual Analyzer), and (h) high quality and valuable Knowledge Bases (KB), constructed either by academicians (e.g., Cvc, DBpedia, Freebase, and YAGO) and industrials (e.g., Google Knowledge Graph, Facebook Knowledge Graph, Amazon Knowledge Graph, Credit Rating Agencies, Enterprise Knowledge Base, etc.). In this talk, we would like to foster the creation of a think tank dedicated to getting the best from Big Data V's and the efforts related to it to revisit our research activities without compromising them. In this talk, we would like to share the experience conducted with our Model and Data Engineering Team of the LIAS Laboratory at ISAE-ENSMA, which aims at the design of data warehousing applications. Based on the literature, this design is based on two main approaches: (i) a supply-driven approach (also called data-driven) that starts with an analysis of operational data sources in order to identify all the available data and (ii) a user-driven approach (also known as requirement-driven or goal-orientated) which stems from the determination of the information requirements of different business users. Several studies and experiments show that resorting to these two approaches entails a high risk for companies, since some functional requirements cannot be satisfied. This is due to the lack of relevant data in sources. In parallel, reference studies have identified the crucial role of knowledge bases (KB) for analytical tasks, by offering analysts more entities (people, places, products, etc.). The availability of a huge, high quality valuable KB is an asset for data warehousing designers and decision-makers to construct/exploit a valuable data warehouse. So, faced with this situation, we here present a value-driven approach that revisits the traditional life cycle of the design of data warehouses, by considering KB as an external resource. These different phases are illustrated via the YAGO KB.