文章基本信息

标题：Editorial
本地全文：下载
作者：João Varajão
期刊名称：International Journal of Information Systems and Project Management
印刷版ISSN：2182-7796
电子版ISSN：2182-7788
出版年度：2015
卷号：3
期号：1
出版社：SciKA
摘要：It is our great pleasure to bring you the first number of the third volume of IJISPM - International Journal of Information Systems and Project Management. The mission of the IJISPM is the dissemination of new scientific knowledge on information systems management and project management, encouraging further progress in theory and practice. In this issue readers will find important contributions on business process improvement, H ealth Information Technology acceptance, and on Building Information Modeling. The first article, "Business process improvement by means of Big Data based Decision Support Systems: a case study on Call Centers", is authored by Alejandro Vera-Baquero, Ricardo Colomo-Palacios, Owen Molloy and Mahmoud Elbattah. Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, organizations need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and value of "joined-up thinking" across supply chains and healthcare pathways, for example, this creates a demand for a new type of approach to Business Activity Monitoring and Management. This new approach requires Big Data solutions to cope with the volume and speed of transactions across global supply chains. The article describes a methodology and framework to leverage Big Data and Analytics to deliver a Decision Support framework to support Business Process Improvement, using near real-time process analytics in a decision-support environment. The system supports the capture and analysis of hierarchical process data, allowing analysis to take place at different organizational and process levels. Individual business units can perform their own process monitoring. An event-correlation mechanism is built into the system, allowing the monitoring of individual process instances or paths