Data mart

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

Big data augmentated business trend identification: the case of mobile commerce.

Abstract: Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

More Than Bar Codes: Integrating Global Standards-Based Bar Code Technology Into National Health Information Systems in Ethiopia and Pakistan to Increase End-to-End Supply Chain Visibility.

Abstract: The United Nations Population Fund (UNFPA) and the United States Agency for International Development (USAID) DELIVER PROJECT work together to strengthen public health commodity supply chains by standardizing bar coding under a single set of global standards. From 2015, UNFPA and USAID collaborated to pilot test how tracking and tracing of bar coded health products could be operationalized in the public health supply chains of Ethiopia and Pakistan and inform the ecosystem needed to begin full implementation. Pakistan had been using proprietary bar codes for inventory management of contraceptive supplies but transitioned to global standards-based bar codes during the pilot. The transition allowed Pakistan to leverage the original bar codes that were preprinted by global manufacturers as opposed to printing new bar codes at the central warehouse. However, barriers at lower service delivery levels prevented full realization of end-to-end data visibility. Key barriers at the district level were the lack of a digital inventory management system and absence of bar codes at the primary-level packaging level, such as single blister packs. The team in Ethiopia developed an open-sourced smartphone application that allowed the team to scan bar codes using the mobile phone's camera and to push the captured data to the country's data mart. Real-time tracking and tracing occurred from the central warehouse to the Addis Ababa distribution hub and to 2 health centers. These pilots demonstrated that standardized product identification and bar codes can significantly improve accuracy over manual stock counts while significantly streamlining the stock-taking process, resulting in efficiencies. The pilots also showed that bar coding technology by itself is not sufficient to ensure data visibility. Rather, by using global standards for identification and data capture of pharmaceuticals and medical devices, and integrating the data captured into national and global tracking systems, countries are able to lay the foundation for interoperability and ensure a harmonized language between global health stakeholders.

Data warehouse development and management: Practices of some large companies

Abstract: 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.

Computer-aided data-mart design

Abstract: With decision support systems, decisionmakers analyse data in data marts extracted from production bases. The data-mart sche ma design is generally performed by expert designers (administrator or com puter specialist). With data-driven, requirement-driven or hybrid-driven ap proaches, this designer builds a data-mart defining facts (analysis subject s) and analysis axes. This process, based on data sources and decision-makers requirements, often turns out to be approximate and complex. We propose to de sign a data-mart schema by the decision-maker himself, following a hybrid-d riven approach. Using an assistance process that visualises successively int rmediate schemas built from data sources, the decision-maker gradually builds h is multidimensional schema. He determines measures to be analysed, dimensions h ierarchies within dimensions. A CASE tool based on this concept has be en d veloped.

Automating Schema Integration Technique Case Study: Generating Data Warehouse Schema from Data Mart Schemas

Abstract: The schema integration technique offers the possibility to unify the representation of several schemas into one global schema. In this work, we present two contributions. The first one is about automating this technique to reduce human intervention. The second one is about applying this technique to generate data warehouse schema from data mart schemas. To response to our goals, we propose a new methodology that is composed by schema matching and schema mapping. The first technique compares the elements of the two schemas using a new semantic measure to generate the mapping rules. The second one transforms the mapping rules into queries and applies them to ensure the automatic merging of the schemas.