No abstract

The emergence of Internet as the hub of e-business has made business process integration another important frontier of information management and technology. As a result, the management of inter-organizational business processes is now a major concern of corporate managers. We envision that the new and complex ways of interactions among business partners in a supply web will need to be managed effectively as the next generation of the Internet makes cross company workflow a reality. Consequently, business managers are confronted with new issues and new decisions in the strategic and operational aspects of process integration. To facilitate the understanding of these issues, we introduce a new paradigm of business computing referred to as E-business Process Interleaving, which emphasizes the complex and dynamic integration of e-business processes across company boundaries. In this paper, we outline various impacts of this new phenomenon on information management and technology and investigate the enabling technologies of e-business process interleaving. We believe that our work can help the development of the next generation of corporate information infrastructure that enables greater degree of e-business process integration. Information Systems and e-Business Management Ó Springer-Verlag 2003 332 A. Segev et al. e-Business process interleaving: Managerial and technological implications 335 336 A. Segev et al.

Time-series forecasting has been an important research domain for so many years. Its applications include ECG predictions, sales forecasting, weather conditions, even COVID-19 spread predictions. These applications have motivated many researchers to figure out an optimal forecasting approach, but the modeling approach also changes as the application domain changes. This work has focused on reviewing different forecasting approaches for telemetry data predictions collected at data centers. Forecasting of telemetry data is a critical feature of network and data center management products. However, there are multiple options of forecasting approaches that range from a simple linear statistical model to high capacity deep learning architectures. In this paper, we attempted to summarize and evaluate the performance of well known time series forecasting techniques. We hope that this evaluation provides a comprehensive summary to innovate in forecasting approaches for telemetry data.

No abstract