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Data lakes are becoming increasingly prevalent for big data management and data analytics. In contrast to traditional 'schema-on-write' approaches such as data warehouses, data lakes are repositories storing raw data in its original formats…
Over the past two decades, we have witnessed an exponential increase of data production in the world. So-called big data generally come from transactional systems, and even more so from the Internet of Things and social media. They are…
Large organizations today are being served by different types of data processing and informations systems, ranging from the operational (OLTP) systems, data warehouse systems, to data mining and business intelligence applications. It is…
Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives…
This paper introduces Data Stations, a new data architecture that we are designing to tackle some of the most challenging data problems that we face today: access to sensitive data; data discovery and integration; and governance and…
Strategic planning in a corporate environment is often based on experience and intuition, although internal data is usually available and can be a valuable source of information. Predicting merger & acquisition (M&A) events is at the heart…
The data warehousing is becoming increasingly important in terms of strategic decision making through their capacity to integrate heterogeneous data from multiple information sources in a common storage space, for querying and analysis. So…
The concept of Industry 4.0 brings a disruption into the processing industry. It is characterised by a high degree of intercommunication, embedded computation, resulting in a decentralised and distributed handling of data. Additionally,…
A method for representing the digest information of each dataset is proposed, oriented to the aid of innovative thoughts and the communication of data users who attempt to create valuable products, services, and business models using or…
In recent decades, it has become a significant tendency for industrial manufacturers to adopt decentralization as a new manufacturing paradigm. This enables more efficient operations and facilitates the shift from mass to customized…
Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data…
While the Internet was conceived as a decentralized network, the most widely used web applications today tend toward centralization. Control increasingly rests with centralized service providers who, as a consequence, have also amassed…
The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services,…
In today's digital age, the imperative to protect data privacy and security is a paramount concern, especially for business-to-business (B2B) enterprises that handle sensitive information. These enterprises are increasingly constructing…
Microservice architectures are a popular choice for deploying large-scale data-intensive applications. This architectural style allows microservice practitioners to achieve requirements related to loose coupling, fault contention, workload…
This study investigates the impact of software design model capabilities and data structure algorithm abilities on microservices architecture design within enterprises. Utilizing a qualitative methodology, the research involved in-depth…
The emerging paradigm of data economy can constitute an unmissable and attractive opportunity for companies that aim to consider their data as valuable assets. To fully leverage this opportunity, data owners need to have specific and…
Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even…
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…
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…