Monitoring Platform Emergence: Guidelines from Software Networks

In this paper we explore how platforms emerge and evolve due to independent actions by companies providing them or launching products on them. We use the software industry as the setting for our study. We analyze the pattern of evolution for Windows, Unix, and Linux over 14 years. Based on this, we derive some lessons for companies aspiring to compete in settings where platforms and complementors play a major role. We support our analysis using visualizations. PLEASE NOTE: This is a very large article, over 1 MB in size

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

Decision support in E-government – a pervasive business intelligence approach case study in a local government

Business Intelligence (BI) systems are being increasingly used by organizations and considered as an advantage, which goal is to offer access to information in a timely manner to support the decision-making process. Simultaneously, the local government has put forward quality assurance systems, with the goal of improving efficiency of internal processes, who require timely and quality information to function. However it should be noted that this is an area of activity with peculiar characteristics that must be taken into account. This paper presents a real case study and the development of a pervasive BI functional solution implemented in local government, providing support and improving the quality of processes. The developed solution brings some important contributions and represents some advances in the e-Government context applied to local governments. This solution is able to accommodate a wide range of users, with the information available anytime and anywhere, capable of issuing alerts and being ubiquitous, scalable and have real-time data availability.(undefined

Enabling collaborative GeoVisual analytics: Systems, techniques, and research challenges

Collaboration across disciplines is recognized as one of the great challenges for research in visual analysis of geographic information (GeoVisual Analytics, GVA). Considering the increasing availability of geodata and the complexity of analytical problems, the need to advance the support for collaborative work is becoming more pressing and prominent. This article contributes to this objective by reviewing the state‐of‐the‐art of the support for collaborative work in GVA systems and by identifying research challenges and proposing strategies to address them. We conducted a systematic review, resulting in the identification of 13 collaborative systems, 6 distinct collaborative techniques, and 3 research challenges. We conclude that GVA is moving toward more effective support of multidisciplinary and cross‐domain collaborative analysis. However, to materialize this potential, research is needed to improve the support for hybrid collaborative scenarios, cross‐device collaboration, and support for time‐critical and long‐term analysis