Strategic IT alignment Projects. Towards Good Governance Maciá Pérez, Francisco; Berna Martinez, José Vicente; Lorenzo Fonseca, Iren
Computer standards and interfaces,
June 2021, 2021-06-00, 20210601, Letnik:
76
Journal Article
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•We propose a model for implementing and operating a portfolio of strategic IT projects•This proposal is based on Good Governance principles•The model achieve strategic alignment of IT projects with ...the organisation's business•The model has already been implemented in fourteen public universities•The model improves state of maturity of Good Governance.
The senior management of organisations frequently perceive IT Projects as merely technological in nature. They fail to realise that in reality, the mission of IT is to provide technology-based support to business processes that can be key to the organisation. This lack of understanding means that these IT projects are not aligned with the business objectives and that investments in resources and personnel are not adequately prioritised. This can lead to an opportunity loss: a mere computerising of the business is sought, and processes that could turn out to be transformative, generating added value, driving a true digital transformation of the business are overlooked. This article proposes a model for implementing and operating a portfolio of strategic IT projects. Based on Good Governance principles, these latter projects move strategic decision-making up to an organisation's senior management, succeeding in gradually implicating these managers into the IT strategy. But above all, the model succeeds in achieving the targeted strategic alignment of IT projects with the organisation's business objectives and interests. The model has already been implemented in fourteen medium and large size public universities. The follow-up through interviews of the nine longest-standing experiences—some are nearly a decade old—revealed that the portfolio implementation strategy had helped to markedly improve the following elements: the institutions’ state of maturity of Good Governance; senior management's involvement in IT projects; and the identification of the most interesting IT projects for the business. To conclude, based on our experience, we can affirm that the strategic IT alignment projects is an effective IT Governance tool and, by extension, an example of Good Governance practice.
The smart city concept has been gaining momentum in the scientific community because of its potentially huge impact on citizens’ quality of life. However, expectations have not yet been met in ...practice. This is firstly due to the sheer breadth of such projects and secondly to the lack of methodologies available to guide the development of flexible and sustainable platforms over time. In this work, we propose to address these issues by using a university campus as a less complex mock-up version of a city. Despite differences between them, we find services that are common to both, and a medium-sized city’s population is comparable to that of a university community. We propose an IT conceptual framework to model and implement smart university projects, which supports the design of a platform that is both in line with the strategic plans of universities and is flexible, sustainable, stable, and sufficiently modular to support the addition of different value-added services over the years. Our framework is based on a service provision model materialised in an IT architecture and managed following a methodology to integrate IT components that ensure the insertion of new, smart initiatives of value to the community, aligned with the university’s needs, via a value-added service planning process. The results are presented in the University of Alicante case study and the SmartUA project.
Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the ...utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.
•We propose a formal expansion to the theory of rough sets.•We propose an efficient algorithm for the detection of outliers.•We have implemented the algorithm and verified the theoretical results.
Analysing message streams in a dynamic environment is challenging. Various methods and metrics are used to evaluate message classification solutions, but often fail to realistically simulate the ...actual environment. As a result, the evaluation can produce overly optimistic results, rendering current solution evaluations inadequate for real-world environments. This paper proposes a framework based on the simulation of real-world message streams to evaluate classification solutions. The framework consists of four modules: message stream simulation, processing, classification and evaluation. The simulation module uses techniques and queueing theory to replicate a real-world message stream. The processing module refines the input messages for optimal classification. The classification module categorises the generated message stream using existing solutions. The evaluation module evaluates the performance of the classification solutions by measuring accuracy, precision and recall. The framework can model different behaviours from different sources, such as different spammers with different attack strategies, press media or social network sources. Each profile generates a message stream that is combined into the main stream for greater realism. A spam detection case study is developed that demonstrates the implementation of the proposed framework and identifies latency and message body obfuscation as critical classification quality parameters.
•We propose an architectural model of the neuroregulator system for human implantable hardware devices.•We developed a FPGA prototype hardware of its application.•We validated the proposal using a ...hybrid software-hardware LUT simulator.
The human neuroregulator system is a complex nervous system composed of a heterogeneous group of nerve centres distributed along the spinal cord. These centres act autonomously, communicate through nerve interconnections, and govern and regulate the behaviour of human beings’ organs and systems. For over twenty years, our research group has been studying the neuroregulatory system of the lower urinary tract (LUT), which controls the organs and systems involved in the urination process. Based on the study of the behaviour and composition of the LUT, we have succeeded in isolating the centres involved in its functioning. The goal has been to understand the individual role played by each centre in order to create a general model of the neuroregulator system capable of operating at the level of the nerve centre. The model has been created and formalised based on Multi-Agent Systems (MAS) theory: each agent thus models the behaviour of a nerve centre. This latter proposal is a step forward regarding current black box models. Its fine granularity opens up the possibility of acting at the level of the centre, of particular interest to treat dysfunctions. The present study enriches this theoretical model with an architectural model that makes it suitable to implement in hardware. Based on this new model, we propose a System on Chip (SoC) design of a specific processor capable of performing a nerve centre's functions. Although this processor can be entirely configured and programmed to adjust to the functioning of the different centres, the present work aimed at facilitating the understanding and validation of the proposal. We thus focused on the Cortical-Diencephalic (CD) centre, responsible for voluntary micturition. The research adopted an original approach with the aim of creating a configurable chip, capable of developing any neuroregulatory function, implantable in the body and being able to function in a coordinated way with the biological neuroregulator system.
Network Intrusion Detection System Embedded on a Smart Sensor Maciá-Pérez, Francisco; Mora-Gimeno, F; Marcos-Jorquera, D ...
IEEE transactions on industrial electronics (1982),
2011-March, 2011-03-00, 20110301, Letnik:
58, Številka:
3
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This paper proposes a Network Intrusion Detection System (NIDS) embedded in a smart-sensor-inspired device under a service-oriented architecture (SOA) approach which is able to operate independently ...as an anomaly-based NIDS, or integrated transparently in a Distributed Intrusion Detection System (DIDS). The proposal is innovative because it combines the advantages of the smart sensor approach and the subsequent offering of the NIDS functionality as a service with the SOA use to achieve their integration with other DIDS components. The main goal of this paper is to reduce the huge volume of management tasks inherent to this type of network services, as well as facilitating the design of DIDS whose managing complexity could be restricted within well-defined margins. This paper also addresses the construction of a physical sensor prototype. This prototype was used to carry out the tests that has demonstrated the proposal's validity, providing detection and performance ratios similar to those of existing intrusion detection systems (IDS), but with the advantage of a zero-maintenance approach.
This article presents the design of a field programmable gate array (FPGA)-based prototype of a system on chip (SoC) capable of behaving as one of the nerve centres comprising the neuroregulatory ...system in humans: the cortical-diencephalic nerve centre. The neuroregulatory system is a complex nerve system consisting of a heterogeneous group of nerve centres. These centres are distributed throughout the length of the spinal cord, are autonomous, communicate via interneurons, and govern and regulate the behaviour of multiple organs and systems in the human body. As a result of years of study of the functioning and composition of the neuroregulatory system of the lower urinary tract (LUT), the centres that regulate this system have been isolated. The objective of this study is to understand the individual functioning of each centre in order to create a general model of the neuroregulatory system that is capable of operating at the level of the actual nerve centre. This model represents an advancement of the current black box models that do not allow for isolated or independent treatment of system dysfunction. In this study, we re-visit our research into the viability of the hardware design of one of these centres—the cortical-diencephalic centre. We describe this hardware because functioning of the centre is completely configurable and programmable, which validates the design for other centres that comprise the neuroregulatory system. In this document, we succinctly present the formal model of the centre, propose a hardware design and an FPGA-based prototype, construct a testing and simulation environment to evaluate it and, lastly, analyse and contrast the results using data obtained from real patients, verifying that the functional behaviour fits that observed in humans.
•We propose a configurable and programmable hardware design of the Cortical-Diencephalic centre.•We have implemented a prototype of the hardware design on FPGA system.•We have constructed a testing and simulation environment.•We analysed the results and verified that the verifying that the functional behaviour fits that observed in humans.
Now-a-days, email is often one of the most widely used means of communication despite the rise of other communication methods such as instant messaging or communication via social networks. The need ...to automate the email stream management increases for reasons such as multi-folder categorization, and spam email classification. There are solutions based on email content, capable of contemplating elements such as the text subjective nature, adverse effects of concept drift, among others. This paper presents an email stream classifier with a goal-oriented approach to client and server environment. The i* language was the basis for designing the proposed email stream classifier. The email environment was represented with the early requirements model and the proposed classifier with the late requirements model. The classifier was implemented following a multi-agent system approach supported by JADE agent platform and Implementation_JADE pattern. The behavior of agents was taking from an existing classifier. The multi-agent classifier was evaluated using functional, efficacy and performance tests, which compared the existing classifier with the multi-agent approach. The results obtained were satisfactory in all the tests. The performance of multi-agent approach was better than the existing classifier due to the use of multi-threads.
In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding ...relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. This study presents a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm, whose usefulness is contextualised by comparison to different algorithms analysed in the literature.
The inclusion of IoT in digital platforms is very common nowadays due to the ease of deployment, low power consumption and low cost. It is also common to use heterogeneous IoT devices of ad-hoc or ...commercial development, using private or third-party network infrastructures. This scenario makes it difficult to detect invalid packets from malfunctioning devices, from sensors to application servers. These invalid packets generate low quality or erroneous data, which negatively influence the services that use them. For this reason, we need to create procedures and mechanisms to ensure the quality of the data obtained from IoT infrastructures, regardless of the type of infrastructure and the control we have over them, so that the systems that use this data can be reliable. In this work we propose the development of an Anomaly Detection System for IoT infrastructures based on Machine Learning using unsupervised learning. We validate the proposal by implementing it on the IoT infrastructure of the University of Alicante, which has a multiple sensing system and uses third-party services, over a campus of one million square meters. The contribution of this work has been the generation of an anomaly detection system capable of revealing incidents in IoT infrastructures, without knowing details about the infrastructures or devices, through the analysis of data in real time. This proposal allows to discard from the IoT data flow all those packets that are suspected to be anomalous to ensure a high quality of information to the tools that consume IoT data.
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