相关论文: Agile data management in NAV: A Case Study

The rise of big data has revolutionized data exploitation practices and led to the emergence of new concepts. Among them, data lakes have emerged as large heterogeneous data repositories that can be analyzed by various methods. An efficient…

Applications requiring real-time processing of large volumes of data have been the main driver for rethinking the traditional cloud, giving rise to novel cloud models. Distributed cloud (DC) is a model that allows users to dynamically…

Global digitalization has given birth to the explosion of digital services in approximately every sector of contemporary life. Applications of artificial intelligence, blockchain technologies, and internet of things are promising to…

Background: Agile methods are no longer restricted to small projects and co-located teams. The last decade has seen the spread of agile into large scale, distributed and regulated domains. Many case studies show successful agile adoption in…

Cloud-enabled large-scale distributed systems orchestrate resources and services from various providers in order to deliver high-quality software solutions to the end users. The space and structure created by such technological advancements…

With the proliferation of the data warehouses as supportive decision making tools, organizations are increasingly looking forward for a complete data warehouse success model that would manage the enormous amounts of growing data. It is…

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…

With the rapid transformation of computer hardware and algorithms, mobile networking has evolved from low data carrying capacity and high latency to better-optimized networks, either by enhancing the digital network or using different…

Distributed software systems that are designed to run over workstation machines within organisations are termed workstation-based. Workstation-based systems are characterised by dynamically changing sets of machines that are used primarily…

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…

Networks connecting distributed cloud services through multiple data centers are called cloud networks. These types of networks play a crucial role in cloud computing and a holistic performance evaluation is essential before planning a…

Distribution of software development is becoming more and more common in order to save the production cost and reduce the time to market. Large geographical distance, different time zones and cultural differences in distributed software…

Large-scale system development companies are increasingly adopting agile methods. While this adoption may improve lead-times, such companies need to balance two trade-offs: (i) the need to have a uniform, consistent development method on…

[Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges…

The modern datacenter's computing capabilities have far outstripped the applications running within and have become a hidden cost of doing business due to how software is architected and deployed. Resources are over-allocated to monolithic…

The personalized health care service utilizes the relational patient data and big data analytics to tailor the medication recommendations. However, most of the health care data are in unstructured form and it consumes a lot of time and…

Data Lake (DL) is a Big Data analysis solution which ingests raw data in their native format and allows users to process these data upon usage. Data ingestion is not a simple copy and paste of data, it is a complicated and important phase…

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…

In this paper, we propose a data collaboration analysis method for distributed datasets. The proposed method is a centralized machine learning while training datasets and models remain distributed over some institutions. Recently, data…

Science has a data management problem, as well as a project management problem. While industrial-grade data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows,…