Related papers: Business Intelligence for Small and Middle-Sized E…
Purpose: The new competitive environment characterized by innovation and constant change is forcing a new organizational behavior. This requires a digital transformation of SMEs based on collective performance determinants. SMEs have…
Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary…
Distributed computing is increasingly being viewed as the next phase of Large Scale Distributed Systems (LSDSs). However, the vision of large scale resource sharing is not yet a reality in many areas - Grid computing is an evolving area of…
Nowadays, machine learning (ML) teams have multiple concurrent ML workflows for different applications. Each workflow typically involves many experiments, iterations, and collaborative activities and commonly takes months and sometimes…
Business requirements for rapid operational efficiency, customer responsiveness as well as rapid adaptability are actively driving the need for ever increasing communication and integration apabilities of software assets. In this context,…
Marketplace platforms routinely evaluate pricing and allocation policies using logged observational data, yet strong offline performance does not imply that a policy is safe to deploy. In real-time bidding (RTB) marketplaces, reserve-price…
Training deep learning (DL) models on petascale datasets is essential for achieving competitive and state-of-the-art performance in applications such as speech, video analytics, and object recognition. However, existing distributed…
Small and medium enterprises (SMEs) are increasingly vulnerable to cyber threats due to limited resources and cybersecurity expertise, in addition to an increasingly hostile cyber threat environment at national and international levels.…
Small and medium enterprises (SMEs) face growing cyber threats but often lack the resources and expertise needed to adopt Zero Trust Architecture (ZTA). This pilot study examines the drivers and barriers shaping SME perceptions of ZTA…
Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing…
Many research questions can be answered quickly and efficiently using data already collected for previous research. This practice is called secondary data analysis (SDA), and has gained popularity due to lower costs and improved research…
To enable data-driven decision-making across organizations, data professionals need to share insights with their colleagues in context-appropriate communication channels. Many of their colleagues rely on data but are not themselves…
Data warehousing and OLAP applications must nowadays handle complex data that are not only numerical or symbolic. The XML language is well-suited to logically and physically represent complex data. However, its usage induces new theoretical…
As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For…
Leveraging the potential power of even small handheld devices able to communicate wirelessly requires dedicated support. In particular, collaborative applications need sophisticated assistance in terms of querying and exchanging different…
Business Intelligence and Analytics (BI&A) is the process of extracting and predicting business-critical insights from data. Traditional BI focused on data collection, extraction, and organization to enable efficient query processing for…
We recently proposed a new cluster operating system stack, DBOS, centered on a DBMS. DBOS enables unique support for ML applications by encapsulating ML code within stored procedures, centralizing ancillary ML data, providing security built…
Microservice architectures have become a popular approach for designing scalable distributed applications. Despite their extensive use in industrial settings for over a decade, there is limited understanding of the data management…
Cloud computing is proved to be an effective computing technology for organisations through the advantages that it offers such as cost-effectiveness, IT technical agility and scalability, enhancing businesses processes, and increasing…
Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within…