262 research outputs found

    Business-driven IT Management

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    Business-driven IT management (BDIM) aims at ensuring successful alignment of business and IT through thorough understanding of the impact of IT on business results, and vice versa. In this dissertation, we review the state of the art of BDIM research and we position our intended contribution within the BDIM research space along the dimensions of decision support (as opposed of automation) and its application to IT service management processes. Within these research dimensions, we advance the state of the art by 1) contributing a decision theoretical framework for BDIM and 2) presenting two novel BDIM solutions in the IT service management space. First we present a simpler BDIM solution for prioritizing incidents, which can be used as a template for creating BDIM solutions in other IT service management processes. Then, we present a more comprehensive solution for optimizing the business-related performance of an IT support organization in dealing with incidents. Our decision theoretical framework and models for BDIM bring the concepts of business impact and risk to the fore, and are able to cope with both monetizable and intangible aspects of business impact. We start from a constructive and quantitative re-definition of some terms that are widely used in IT service management but for which was never given a rigorous decision: business impact, cost, benefit, risk and urgency. On top of that, we build a coherent methodology for linking IT-level metrics with business level metrics and make progress toward solving the business-IT alignment problem. Our methodology uses a constructive and quantitative definition of alignment with business objectives, taken as the likelihood – to the best of one’s knowledge – that such objectives will be met. That is used as the basis for building an engine for business impact calculation that is in fact an alignment computation engine. We show a sample BDIM solution for incident prioritization that is built using the decision theoretical framework, the methodology and the tools developed. We show how the sample BDIM solution could be used as a blueprint to build BDIM solutions for decision support in other IT service management processes, such as change management for example. However, the full power of BDIM can be best understood by studying the second fully fledged BDIM application that we present in this thesis. While incident management is used as a scenario for this second application as well, the main contribution that it brings about is really to provide a solution for business-driven organizational redesign to optimize the performance of an IT support organization. The solution is quite rich, and features components that orchestrate together advanced techniques in visualization, simulation, data mining and operations research. We show that the techniques we use - in particular the simulation of an IT organization enacting the incident management process – bring considerable benefits both when the performance is measured in terms of traditional IT metrics (mean time to resolution of incidents), and even more so when business impact metrics are brought into the picture, thereby providing a justification for investing time and effort in creating BDIM solutions. In terms of impact, the work presented in this thesis produced about twenty conference and journal publications, and resulted so far in three patent applications. Moreover this work has greatly influenced the design and implementation of Business Impact Optimization module of HP DecisionCenter™: a leading commercial software product for IT optimization, whose core has been re-designed to work as described here

    Maps of Lessons Learnt in Requirements Engineering

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    Both researchers and practitioners have emphasized the importance of learning from past experiences and its consequential impact on project time, cost, and quality. However, from the survey we conducted of requirements engineering (RE) practitioners, over 70\% of the respondents stated that they seldom use RE lessons in the RE process, though 85\% of these would use such lessons if readily available. Our observation, however, is that RE lessons are scattered, mainly implicitly, in the literature and practice, which obviously, does not help the situation. We, therefore, present ``maps” of RE lessons which would highlight weak (dark) and strong (bright) areas of RE (and hence RE theories). Such maps would thus be: (a) a driver for research to ``light up” the darker areas of RE and (b) a guide for practice to benefit from the brighter areas. To achieve this goal, we populated the maps with over 200 RE lessons elicited from literature and practice using a systematic literature review and survey. The results show that approximately 80\% of the elicited lessons are implicit and that approximately 70\% of the lessons deal with the elicitation, analysis, and specification RE phases only. The RE Lesson Maps, elicited lessons, and the results from populating the maps provide novel scientific groundings for lessons learnt in RE as this topic has not yet been systematically studied in the field

    Business-driven IT Management

    Get PDF
    Business-driven IT management (BDIM) aims at ensuring successful alignment of business and IT through thorough understanding of the impact of IT on business results, and vice versa. In this dissertation, we review the state of the art of BDIM research and we position our intended contribution within the BDIM research space along the dimensions of decision support (as opposed of automation) and its application to IT service management processes. Within these research dimensions, we advance the state of the art by 1) contributing a decision theoretical framework for BDIM and 2) presenting two novel BDIM solutions in the IT service management space. First we present a simpler BDIM solution for prioritizing incidents, which can be used as a template for creating BDIM solutions in other IT service management processes. Then, we present a more comprehensive solution for optimizing the business-related performance of an IT support organization in dealing with incidents. Our decision theoretical framework and models for BDIM bring the concepts of business impact and risk to the fore, and are able to cope with both monetizable and intangible aspects of business impact. We start from a constructive and quantitative re-definition of some terms that are widely used in IT service management but for which was never given a rigorous decision: business impact, cost, benefit, risk and urgency. On top of that, we build a coherent methodology for linking IT-level metrics with business level metrics and make progress toward solving the business-IT alignment problem. Our methodology uses a constructive and quantitative definition of alignment with business objectives, taken as the likelihood – to the best of one’s knowledge – that such objectives will be met. That is used as the basis for building an engine for business impact calculation that is in fact an alignment computation engine. We show a sample BDIM solution for incident prioritization that is built using the decision theoretical framework, the methodology and the tools developed. We show how the sample BDIM solution could be used as a blueprint to build BDIM solutions for decision support in other IT service management processes, such as change management for example. However, the full power of BDIM can be best understood by studying the second fully fledged BDIM application that we present in this thesis. While incident management is used as a scenario for this second application as well, the main contribution that it brings about is really to provide a solution for business-driven organizational redesign to optimize the performance of an IT support organization. The solution is quite rich, and features components that orchestrate together advanced techniques in visualization, simulation, data mining and operations research. We show that the techniques we use - in particular the simulation of an IT organization enacting the incident management process – bring considerable benefits both when the performance is measured in terms of traditional IT metrics (mean time to resolution of incidents), and even more so when business impact metrics are brought into the picture, thereby providing a justification for investing time and effort in creating BDIM solutions. In terms of impact, the work presented in this thesis produced about twenty conference and journal publications, and resulted so far in three patent applications. Moreover this work has greatly influenced the design and implementation of Business Impact Optimization module of HP DecisionCenter™: a leading commercial software product for IT optimization, whose core has been re-designed to work as described here

    Personalized ECA Tutoring with Self-Adjusted POMDP Policies and User Clustering

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    An Embodied Conversational Agent (ECA) is an intelligent agent that enables real-time human/computer interaction in natural language. For its rich style of communication, ECA is particularly popular and useful in applications such as education, e-commerce, healthcare, finance, marketing, and business, where a human-like conversation is more attractive to users than traditional keyboard-based interaction. The interest in using ECA in e-learning has become even stronger since the COVID-19 outbreak, and a preliminary investigation has been started by our research group to extend collaborative learning in a virtual environment with personalized ECA tutoring. This thesis document first highlights the prior work of personalized tutoring with ECA, including wavelet transformation for user clustering and face-to-face interaction for quiz-style e-learning. An enhanced approach is then developed to enable self-adjustment of POMDP policies for dialogue management and to allow a more natural way of question/answer style of personalized tutoring with a generic, flexible tutoring ontology. In addition, the proposed approach uses machine learning techniques to adjust knowledge levels of user clustering and evaluates its effectiveness by conducting experiments with real datasets. This research work is projected to further improve online learning with ECA serving as a personal tutor

    Combining SOA and BPM Technologies for Cross-System Process Automation

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    This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

    Three Studies on Multi-attribute Market Mechanisms in E-procurement

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    Successful e-procurement depends on selecting the appropriate mechanisms that comprise rules governing and facilitating transaction process. Existing mechanisms have theoretical or practical limitations such as limited number of attributes, disclosure of buyer’s preferences and costly processes. The present research addresses these issues through three studies. Study 1 presents two feasible mechanisms for multi-attribute multi-supplier transactions. They allow buyers to control preference representation and information revelation, assuring that suppliers obtain sufficient information in making effective proposals while protecting confidential information. Following the design-science approach, the mechanisms are implemented to support multi-attribute reverse auctions and multi-bilateral negotiations. Study 2 examines the revelation of information in multi-attribute reverse auctions. Three revelation rules are formulated with admissible bids, winning bids and all bidders’ bids. Their effects on the process, outcomes and bidders’ assessment are tested in two experiments. The results show significant improvement in process efficiency when more information is revealed. The suppliers reached better outcomes with either admissible bids only or all bidders’ bids, while the buyers gained more when revealing the winning bids only. Bidders were more satisfied with the outcomes and system when more information was provided. Study 3 compares multi-attribute reverse auctions and multi-bilateral negotiations in both laboratory and online experiments. The results show that auctions are more efficient than negotiations in terms of the process. Auctions led to greater gains for the buyers, whereas more balanced contracts were reached in negotiations. Suppliers’ assessment was affected by their outcomes, and the winning suppliers were more satisfied with the process, outcomes and system. The buyer’s role was also examined. Different types of information conveyed from buyer influence suppliers’ behavior in making bids/offers and concessions, which in turn affected buyer’s gains. This research provides implications to future studies and practices in e-procurement, in particular, the formulation of a procedure of two multi-attribute mechanisms and the formulation of general guidelines for strategic use of different mechanisms in various e-procurement contexts

    Advances in Information Security and Privacy

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    With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

    Review on recent advances in information mining from big consumer opinion data for product design

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    In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

    The 11th Conference of PhD Students in Computer Science

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