Related papers: Data Mesh: a Systematic Gray Literature Review
Data mesh is an emerging decentralized approach to managing and generating value from analytical enterprise data at scale. It shifts the ownership of the data to the business domains closest to the data, promotes sharing and managing data…
With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics…
Data mesh is a socio-technical approach to decentralized analytics data management. To manage this decentralization efficiently, data mesh relies on automation provided by a self-service data infrastructure platform. A key aspect of this…
The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centralized architecture is limited by the proliferation of data…
The Medical Subject Headings (MeSH), one of the main knowledge organization systems in the biomedical domain, continuously evolves to reflect the latest scientific discoveries in health and life sciences. Previous research has focused on…
Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as…
Infrastructure construction, often dubbed an "industry of industries," is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better…
Context: Agility at the business level requires Information Technology (IT) environment flexible and customizable, as well as effective and responsive governance in order to deliver value faster, better, and cheaper to the business.…
The data mesh is a novel data management concept that emphasises the importance of a domain before technology. The concept is still in the early stages of development and many efforts to implement and use it are expected to have negative…
Over the past few years, a growing number of data platforms have emerged, including data commons, data repositories, and databases containing biomedical, environmental, social determinants of health and other data relevant to improving…
Data warehouse, data lake, data lakehouse, data mesh ... many new names for analytical data architectures are currently circulating in the scene. But are the various approaches really so different? This article attempts a structured…
Scientific data management is at a critical juncture, driven by exponential data growth, increasing cross-domain dependencies, and a severe reproducibility crisis in modern research. Traditional centralized data management approaches are…
Systematic literature studies have received much attention in empirical software engineering in recent years. They have become a powerful tool to collect and structure reported knowledge in a systematic and reproducible way. We distinguish…
This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SBSE). While MSR formulates software engineering problems as…
Enterprise data platforms face an enduring tension between domain self-service and holistic governance. The data mesh paradigm proposed decentralized domain ownership as a remedy, but pure implementations frequently underdeliver: teams…
Context: Championed by IBM's vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences and workshops have been established and have…
Data democratization is an ongoing process that broadens access to data and facilitates employees to find, access, self-analyze, and share data without additional support. This data access management process enables organizations to make…
Data is the key to success for any Data-Driven Organization, and managing it is considered the most challenging task. Data Architecture (DA) focuses on describing, collecting, storing, processing, and analyzing the data to meet business…
When organizations decentralize data product ownership, as in the data mesh paradigm, each domain team optimizes for its immediate analytical needs, underinvesting in the cross-domain generality that enables organization-wide reuse. We…
In the context of Industry 4.0, the manufacturing sector is increasingly facing the challenge of data usability, which is becoming a widespread phenomenon and a new contemporary concern. In response, Data Governance (DG) emerges as a viable…