Related papers: Structuring Business Metadata in Data Warehouse Sy…
The success of any business depends on how well it is able to satisfy customer demand, while remaining financially viable. Globalisation and the growth of e-commerce have resulted in retail businesses having to manage increasing numbers of…
The web of data has brought forth the need to preserve and sustain evolving information within linked datasets; however, a basic requirement of data preservation is the maintenance of the datasets' structural characteristics as well. As…
An essential capability of the Virtual Observatory is a means for describing what data and computational facilities are available where, and once identified, how to use them. The data themselves have associated metadata (e.g., FITS…
To succeed in a Big Data strategy, you have to arm yourself with a wide range of data skills and best practices. This strategy can result in an impressive asset that can streamline operational costs, reduce time to market, and enable the…
The digital transformation of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven…
Recently, data exchange platforms have emerged in the digital economy to enable better resource allocation in a data-driven society, which requires cross-organizational data collaborations. Understanding the characteristics of the data on…
This study investigates the simultaneous use of multiple metadata schemas at research data repositories. The analysis covers how eight disciplinary research data repositories from the geosciences and social sciences use disciplinary…
Service sharing is a prominent operating model to support business. Many large inter-organizational networks have implemented some form of value added integrated services in order to reach efficiency and to reduce costs sustainably.…
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…
Today, data guides the decision-making process of most companies. Effectively analyzing and manipulating data at scale to extract and exploit relevant knowledge is a challenging task, due to data characteristics such as its size, the rate…
With the growing use of new technologies, healthcare is nowadays undergoing significant changes. Information-based medicine has to exploit medical decision-support systems and requires the analysis of various, heterogeneous data, such as…
The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to identify customers who potentially become unsatisfied and…
Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have…
This chapter presents software architectures of the big data processing platforms. It will provide an in-depth knowledge on resource management techniques involved while deploying big data processing systems on cloud environment. It starts…
Modern enterprise platforms increasingly depend on distributed microservices, analytical data platforms, and external APIs to construct composite responses for applications. Orchestrating data retrieval across these heterogeneous systems is…
In 2010, the concept of data lake emerged as an alternative to data warehouses for big data management. Data lakes follow a schema-on-read approach to provide rich and flexible analyses. However, although trendy in both the industry and…
In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose…
While manufacturers have been generating highly distributed data from various systems, devices and applications, a number of challenges in both data management and data analysis require new approaches to support the big data era. These…
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…
Nowadays, information management systems deal with data originating from different sources including relational databases, NoSQL data stores, and Web data formats, varying not only in terms of data formats, but also in the underlying data…