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The Role of IoT in the Design of a Security System
Published in Uzzal Sharma, Parmanand Astya, Anupam Baliyan, Salah-ddine Krit, Vishal Jain, Mohammad Zubair Khan, Advancing Computational Intelligence Techniques for Security Systems Design, 2023
Enterprise information systems (EISs) support all data gathering, business intelligence, connectivity, and associated decision-making processes. As a result, the network infrastructure for data gathering and exchange has a significant impact on the performance of an EIS. The goal of this study [13] is to look into the influence of security on enterprise resource planning in the IoT paradigm. The IoT's groundbreaking potential opens up a plethora of new marketing strategies for providing quality across sectors, goods, and service offerings. Ensuring IoT technology is dependable and safe, on the other hand, is critical to achieving the full potential of this game-changing notion. This paper [14] outlines a method for detecting IoT loopholes in businesses. The IoT is becoming imperative in businesses. IoT safety differs from typical PC information security due to the size of the IoT, and convenience and uniformity of such smart devices, and the ability to identify assaults using sensor data. These characteristics of the Internet of Things, along with the huge computational power of upcoming hardware devices, may be leveraged to create a security assessment tool tailored to IoT security. Because ERPs are linked to the internet and intranet, ERP security has become a problem for major businesses. The problem [15] has worsened since the growth of IoT that allows businesses to control different elements of their operations by connecting several devices to a network. Switching to linked IP systems not only enables automation, but it also reduces the cost of operation and complexity of existing systems. ERP systems are copyrighted software designed to be utilized within the four walls of an organization, making them more vulnerable to hackers.
AI-enabled Enterprise Information Systems for Manufacturing
Published in Enterprise Information Systems, 2022
Milan Zdravković, Hervé Panetto, Georg Weichhart
The so-called Enterprise Information Systems (EIS) – the parameterisable software applications dealing with the partial or the complete management of enterprise resources, processes or information – aim to solve common problems, such as lack of process automation, flexibility, responsiveness, scalability, traceability, coherency and integration in as less as possible intrusive ways. However, they often fail to address those issues consistently and the typical reason for that is the complexity of the process of EIS development and/or implementation in which different and diverse stakeholders fail to effectively communicate around the business objectives and key factors for their achievement. Complexity is multiplied with the factor of the long duration of the software development life-cycle in which those objectives could easily become legacy. A solution to this problem is the Model-Driven Engineering (MDE) which facilitates near real-time system customisation and efficient response to a change. MDE relies on the human perceptions of the realities of the enterprise, which are sometimes incomplete or inaccurate. Data-driven approaches address this challenge by embedding machine-learned understanding of the enterprise realities represented by raw data, into the respective models.
Schema on read modeling approach as a basis of big data analytics integration in EIS
Published in Enterprise Information Systems, 2018
Slađana Janković, Snežana Mladenović, Dušan Mladenović, Slavko Vesković, Draženko Glavić
An Enterprise Information System (EIS) is an integrated information system with the basic task of providing the management with the necessary information. This research addresses two major challenges encountered by modern EISs in the sphere of data management in order to be qualified as ‘integrated’ as per the above definition. The promotion of business operation of organizations nearly always involves the introduction of new sources of corporate data. If new data sets fall into the category of Big Data, they require the application of Big Data storage, processing and analysis methods. To use new corporate Big Data sets in a business context, they have to be integrated with the existing corporate data sets, after which the integrated data should be subjected to Big Data analysis. The integration of the existing and new corporate data sets to create the subject of the future Big Data analysis is the first challenge to which this research will try to respond. The second challenge and the subject of this research is the integration of the results of Big Data analysis with EIS. This task has to be solved regardless of whether corporate or external data are the subject of Big Data analysis. External data, such as social media and web data, are increasingly used as the subject of Big Data analyses in order to examine user satisfaction, habits and needs etc.
Intellectual structure of cybersecurity research in enterprise information systems
Published in Enterprise Information Systems, 2023
Nitin Singh, Venkataraghavan Krishnaswamy, Justin Zuopeng Zhang
Enterprise information systems (EIS) are socio-technical systems comprising people, processes, physical systems, and software. As articulated by Romero and Vernadat (2016), research on EIS pertains to enterprise applications (such as enterprise resource planning, customer relationship management, product lifecycle management, business intelligence & analytics, etc.), design and engineering (business process management, object-oriented approach, service-oriented architecture, unified modelling language, web-services, etc.), enterprise architecture (popular frameworks such as Zachman framework, the open architecture framework, etc.) and enterprise application integration and networks.