Showing papers on "Data mart published in 2020"

Design and Implementation of Efficient Decision Support System Using Data Mart Architecture

Abstract: This paper presents and discuss advantage about the design and implementation of Decision Support System by building data mart, which is a place for the storage and participation of data, which users can readily utilize to product improved decisions. Due to the problems within the systems of Information and Telecommunication Public Company obtaining the data in a successful method requires a harmonious potential Thus, This paper presents proposed framework of Decision Support System for Information and Telecommunication Public Company by which discusses the advantage about design, integration of the building data mart and what is the difference between it and relational database for-decision makers to obtain information that contains four layers (Interface layer determines data source, Extract Transformation Load Layer determines design process in SQL Server Integration service, Data mart layer determines data mart that conform Star Schema, Presentation layer determines the results from the system).

Model for Semantic Base Structuring of Digital Data to Support Agricultural Management

Abstract: This article presents a semantic model for structuring digital databases to function in a cloud environment and connect to data sources originating from Big Data. The work examines the process of receiving structured, semi-structured and unstructured data for use in agricultural risk management. It is conceived as an architecture that combines Data Mart, Data Warehouse (NoSQL), and Data Lake resources to support decision making, through knowledge discovery and applies algorithms for data mining by machine learning resources. The configuration presented addresses scenarios involving agricultural data, obtained from sensors operating in multiple modes.

Comprehensive Guide to Implementation of Data Warehouse in Education

Abstract: Data warehouse, otherwise known as DW, is a central repository to hold the history of data which will be created by the integration or consolidation of data from several external sources and from the operational and transactional data stores of the organization. This data repository will be majorly used by the firms to analyze the data for empowering ideal business decisions to get decided. It performs the role of a system for decision support (DSS), to obtain conclusion based on the analyzed pattern of data to the decision maker(s) of the organization. Usage of data warehouse in education will give a great benefit to the government/private educational officers, by obtaining a single version of the truth of school information. This paper analyzes the work implemented in the education domain focusing on DW concepts in detail.

Data mart design in bkpp bandung using from enterprise models to dimensional models method

Abstract: The data warehouse is a single data storage that is complete and consistent with subject-oriented, integrated, non-volatile, and time-variant characteristics that can be used to support decisions. While Data mart is a subset of a data warehouse that supports the information needs of certain departments or business process functions. Badan Kepegawaian, Pendidikan Pelatihan (BKPP) of Bandung Municipality has a history of employee mutation transactions that have not been optimally utilized so it will be very useful if the data can be made into a data mart. This data mart development method uses the From Enterprise Models to Dimensional Models method as its design method and the Bottom-Up Approach as an approach in developing a data mart. This data mart developed using the PostgreSQL database and the PHP language. The purpose of this research is to develop human resource data mart by optimizing the utilization of historical data from employee mutation transactions. The results of this research are human resources data mart that accommodates historical data on employee mutation transactions and produces information that is useful for the Top Management to support the making decision.

An Evaluation of How Big-Data and Data Warehouses Improve Business Intelligence Decision Making.

Abstract: Analyze and understand how to combine data warehouse with business intelligence tools, and other useful information or tools to visualize KPIs are critical factors in achieving the goal of raising competencies and business results of an organization. This article reviews data warehouse concepts and their appropriate use in business intelligence projects with a focus on large amounts of information. Nowadays, data volume is more significant and critical, and proper data analysis is essential for a successful project. From importing data to displaying results, there are crucial tasks such as extracting information, transforming it analyzing, and storing data for later querying. This work contributes with the proposition of a Big Data Business Intelligence architecture for an efficiently BI platform and the explanation of each step in creating a Data Warehouse and how data transformation is designed to provide useful and valuable information. To make valuable information useful, Business Intelligence tools are presented and evaluates, contributing to the continuous improvement of business results.

Machine Learning for Older Jobseeker and Employment Matching

Abstract: The challenge of the growth of the aging society is causing a critical economic burden that is affecting the gross domestic product of many countries. Normally, recruiters for information technology employment face the problem of matching appropriate candidates with available positions as they may have insufficient knowledge to determine whether candidate profiles are suitable. The requirements and demand for jobs may not be enough to advance the situation while matching skills and competence with job descriptions often preclude older candidates. This research is supported by the Thai Gerontology Research and Development Institute (TGRI). Herein, we present a solution involving data mart modeling and machine learning. In the data mart modeling, an extract, transform, and load (ETL) process that pulls data into a data mart (star schema) and then applies distance measures to calculate matching. Historical records of both the companies and older job seekers are kept in the data mart to understand behavior and keep track of data. Moreover, we collect disease information and map to ICD-10 code. This can help employers to find appropriate job seekers related to their skill sets and limitation of disease. We treat their skills as terms and employment positions as documents and then calculate the ranking of each job profile. The results show that the outcomes garnered from the distance measures are consistent and creditable.

Integration of textual VoC into a CX data model for business intelligence use in B2C

Abstract: Customer experience (CX) focuses on customer feedback. CX is a holistic construct which contains different perceptual elements such as satisfaction and loyalty, but also emotions or personality. Customers share their opinions, which contain these elements also in textual expressions through different channels, known in research as Voice of Customer (VoC). Currently, VoC is collected mainly in customer surveys and manually evaluated, or through simple quantitative measurement from data scattered in various systems at the end of a customer journey. To bridge this gap, we designed a multidimensional CX data model for integrated storage of all customers’ data from structured and textual sources. A consolidated CX measurement to monitor elements of CX during the entire customer journey from the customer perspective is proposed to serve as business intelligence. The artefact offers a selfcontained expandable data mart affordable to implement in small and medium B2C enterprises. Companies can now manage customer relationships and future performance more automatically and effectively thanks to integrated information mined from texts, combined with other data from internal systems and shared across the company in unified reporting.

Big data center operation monitoring system

Abstract: The invention relates to an operation monitoring system, particularly relates to a big data center operation monitoring system that comprises a source system for collecting data resources, and a full-service data center that is used for processing and storing data resources acquired by the source system; the full-service data center comprises a data buffer area, a detail data layer and a data martlayer. A data mart server is arranged in the data mart layer; the data mart server is connected with a data analysis module for analyzing the processed data; the data mart server is connected with achart conversion module used for converting an analysis result of the data analysis module into a chart, and the data mart server is connected with a data mining module used for carrying out deep mining on the processed data; according to the technical scheme provided by the invention, the defects of lack of a unified data center operation monitoring system and incapability of efficiently monitoring and analyzing power grid data in the prior art can be effectively overcome.

Time series model for sales predictions in the wholesale industry

Abstract: The prediction process in sales is a basis for a successful ongoing planning process for any organization. Wholesale companies, being B2B oriented, have to plan their organisational environment carefully to optimize the costs and maximize revenue. As the sales process is intersected with logistics, having precise sales predictions optimizes both sales and logistics processes. In order to track the sales towards a customer, we propose a data mart built on the top of the data warehouse to be used with daily loads of outgoing invoices and uninvoiced shipments data.Predictions are based on ARIMA model, one of the most popular forecasting models for the time series. The data is aggregated on a weekly level, as it was proven to be the most useful in this process. For the prediction purposes, we are focusing only on the outgoing invoices. From the business perspective, each product is tracked with data about the sales market, customer, quantity, and the date. In the article, the process of data preparation will also be included as it is the crucial step for successful prediction.

Energy data warehouse system construction method and device

Abstract: The invention is suitable for the technical field of energy data processing, and provides an energy data warehouse system construction method and device, and the method comprises the steps: carrying out the first data processing of the energy data of a data source, obtaining a detail data table corresponding to the energy data, and constructing an operation data layer; performing second data processing on the detail data table according to the energy equipment type to obtain a basic data table so as to construct a basic data layer; performing third data processing on the basic data table according to business analysis requirements to obtain a data warehouse theme so as to construct a general data layer; and performing fourth data processing on the data warehouse theme according to the service unit, and obtaining a data mart corresponding to the service unit to construct an application data layer. By constructing the energy data warehouse system for the energy industry, the problem thata general data warehouse cannot be applied to the energy field is effectively solved, formation of a unified and standard data system is facilitated, and the speed of energy equipment data processingand data analysis in the energy industry is increased.

Data mart construction for semi- structured data source using support vector machine

Abstract: The departments of Agriculture are in need of tracking and updating all the details of food and the agricultural industry This data analysis is mandatory to have the latest updated information for the authorities who decide on the market value and the prediction of various products Having complex, high dimensional information and therefore a progressively perplexing preparing chain as a link of various calculations, up to now it was about difficult to discover what occurred in the classification procedure and which parts of the first information were utilized The proposed strategy utilizes the Artificial Intelligence calculation using Support Vector Machine for the grouping and development of information bazaar It empowers a representation of the total handling chain in the information space and the preprocessing to convert into semi structured data The classifier used defined the data with distinct facts and dimensions to enhance the prediction of data analysis in the food and agricultural sector

Method and system for automated software code generation for a corporate data warehouse

Abstract: This technical solution relates generally to the field of computer technology, and more particularly to systems and methods for the automated generation of software code for a corporate data warehouse. A method for the automated generation of software code for a corporate data warehouse includes obtaining metadata describing a setting for data transformation mechanisms for loading data to a detailed layer level and calculating the data marts of a warehouse; generating at least one template for updating the data of the detailed layer and a data mart of the data warehouse; generating software code for loading data to the detailed layer of the data warehouse and calculating data marts on the basis of the metadata obtained and the data update template generated; installing the software code generated in the previous step in the data warehouse environment for performing loading; reusing the detailed layer and data mart update metadata. The technical result is an increase in the stability of detailed layer and data mart algorithms and a decrease in the number of incidents in the data warehouse.

Scheduling software jobs having dependencies

Abstract: This disclosure includes a scheduling platform for self-service automation of workflows, such as automating data mart creation or generating reports. The platform includes a scheduling model that includes configuring pre- and post-conditions for a job that defines when the task runs and its effects on other jobs when it finishes.

Reviewing The Effect of Business Intelligence on Decision Support Process: An Application on The Finance Sector

Abstract: Nowadays, data warehouse (DWH) and the business intelligence enterprise solutions frequently used by companies blend the services of reporting, analysis and data mining by rich visual components and provide easy to interpret and meaningful information for decision makers. This study aims to summarize the bank profit loss and Balance in the corporate data warehouse model using the bottom up methodology at enterprise level. Building a data mart using the bottom up methodology allows; high flexibility and user friendliness, because it is based on the individual business department (finance) information needs. The other reason this methodology which was preferred, is that the fundamental concept of dimensional modelling, is the star schema and it also supported by data modelling architecture of Oracle OBIEE 11g .One of the main pillars of a bank's pricing policy is to control the profit and loss of branches. At the end of application of this concept’s study, Corporate memory became more mature and dependency on people was removed in terms of reporting. In addition communication and sharing of information within the finance department increased, personal Productivity increased and cost advantage was ensured and the widespread use of structural data, the users' confidence on business intelligence solutions increased by new data mart.

Electronic demography decision making system

Abstract: The article suggests an approach to the development of an electronic demographic decision support system using data warehouse and interactive analytical processing OLAP. This makes it possible to conduct research on demographic processes at a high level and to support decision makers in the field of demography. Due to the presence of many types of demography and a large number of indicators, proposed in the article, a Data Mart Bus Architecture with Linked Dimensional Data Marts is proposed as a Data Warehouse architecture. The article also shows the practical application of this approach using two Data Marts as an example. Based on these Data Marts, OLAP-cubes are built. OLAP operations provide the ability to view cubes in various slices, as well as provide aggregate data. Problems in programming 2020; 2-3: 228-236

View-based data mart management system

Abstract: A data analysis platform may be based on database views. A build module may receive, from a source code repository, information about a modified definition of a view. The build module may identify schema objects on which the view depends and form instructions for creating the view and the schema objects. The instructions may be executed to form the updated version of the view in a schema space separate from a production schema space. A deployment pipeline may coordinate replacing the production version of the view with the new version in response to validating the new version of the view.

Improving Service Quality Using Consumers’ Complaints Data Mart which Effect on Financial Customer Satisfaction

Abstract: One of the best ways to enhance the performance of all companies and manage Customer Satisfaction is to get the consumers' complaints and analyze them in order to fix them. These complaints represent the consumers' behavior to the companies and how these company's response to them. Besides, customers' satisfaction is the main goal of all companies and this goal cannot achieve if they do not handle the customers' complaints. The paper represents a framework of complaint data mart construction where the source data are thousands of complaints about services and financial products of companies. The data mart represents the first step to implement an enterprise data warehouse (DW) to support strategic decisions. Reports are constructed to help analysts and decision-makers to support their decisions related to consumers' complaints and how to improve service quality. Two different categories of on-line analytical processing (OLAP) reports are used, offline and web OLAP reports. The two types of reports provide a deep view of the data and present the analysts with flexible charts that can be used in supporting strategic decisions. SQL Server Management Studio (SSMS), SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS) 2014 beside SQL Server Data Tools (SSDT) 2013 is used to build the data mart staging table, schema, cube, and OLAP reports. MS Excel Pivot table 2010 is used also to import the cube and build offline reports and implementing OLAP processes. This data mart can be utilized by consumers themselves besides decision-makers and analysts. The data mart can measure how the companies fix complaints issues and prevent them from occurring again and identify the factors that influence financial customers' satisfaction.

The Role of the Data Warehouse in the Archive

Abstract: This article sets in context Data Warehouses (DWs) and Online Analytical Processing (OLAP) against the backdrop of databases and Big Data and shows how data warehouses and OLAP were incorporated in...