Biblio

An information system based on real-time file integrations has an important role in today's organizations' work process management. By connecting to the network, file flow and integration between corporate systems have gained a great significance. In addition, network and security issues have emerged depending on the file structure and transfer processes. Thus, there has become a need for an effective and self-learning anomaly detection module for file transfer processes in order to provide the persistence of integration channels, accountability of transfer logs and data integrity. This paper proposes a novel anomaly detection approach that focuses on file size and integration duration of file transfers between enterprise systems. For this purpose, size and time anomalies on transferring files will be detected by a machine learning-based structure. Later, an alarm system is going to be developed in order to inform the authenticated individuals about the anomalies.

Transportation costs for road transport companies may be intensified by rising fuel prices, levies, traffic congestion, etc. Of particular concern to the Mpact group of companies is the long waiting times in the queues at loading and offloading points at three processing mills in the KZN (KwaZulu-Natal) province in South Africa. Following a survey among the drivers who regularly deliver at these sites, recommendations for alleviating the lengthy waiting times are put forward. On the strength of one of these recommendations, namely the innovative use of ICTs, suggestions on how cloud-based technologies may be embraced by the company are explored. In the process, the value added by a cloud-based supply chain, enterprise systems, CRM (Customer Relationship Management) and knowledge management is examined.

Although wireless communication is integral to our daily lives, there are numerous crucial questions related to coverage, energy consumption, reliability, and security when it comes to industrial deployment. The authors provide an overview of wireless machine-to-machine (M2M) technologies in the context of a smart factory.