Data mart

About: Data mart is a research topic. Over the lifetime, 559 publications have been published within this topic receiving 8550 citations.

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.

Data Mining from Relationally Structured Data, Marts, and Warehouses

Abstract: One of the fundamental uses of information technology is data storage. The simplest way to store data is in the form of a flat file, text file, spreadsheet, or table. However, many businesses have more complex relational database systems based on data structures that reflect the relations between the key data entities and their relations, using primary indexes/keys, secondary indexes, normalized data structures, and so on. This chapter discusses the concepts of data mart and data warehouse and comments on how the informational data is separated from the operational data. Then the chapter follows an example of extracting data from an operational environment into a data mart and finally into a unique file that can be used as the starting point for data mining.

Business Intelligence Development Process: A Systematic Literature Review

Abstract: Business Intelligence (BI) is a term that gained popularity in recent years, with other concepts coming along, such as Data Warehouse, Data Mart, Data Deluge, Data Quality, ETL (Extract, Transform, Load), and Data Science, the modern umbrella term. The increase of the competitiveness and wildness among organizations in the current market and business intensifies the need of those organizations to have and hold key information about their issues. This gathering and analysis will keep the stakeholders always informed about their products. A systematic review, independently of the study area, uses a transparent and systematic process to define a research question, search for studies, access their quality and synthesize findings qualitatively or quantitatively. This reviews’ general objective is to analyze experience reports and scientific publications in the context of business intelligence development processes, with the purpose to characterize approaches as success cases, as well as the utilization of guides, knowledge bases and bodies of knowledge to support the further need to create new processes of development and/or to improve the existent processes. The result of this literature review has several implications for BI development researchers, practitioners, and tool developers, but the lack of studies in respected journals reveals that Business Intelligence is still used as needed in corporations, supporting the idea that a formal process needs to be created, to give a set of guidelines, also giving the opportunity to bring a new possibility in information technology studies.

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.