315,191 research outputs found

    Developing an Extendable Process Engine using Cross-Platform Technologies

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    Despite the increasing digitization in everyday work and industry, data collection is still often based on paper-based questionnaires. One of the areas of application where the disadvantages come to bear are large-scale studies, such as clinical trials. In such studies, an enormous amount of paper and staff is needed for transcription, which leads to logistical problems as well as error susceptibility. The reasons why paper-based questionnaires are still used are often a lack of IT knowledge of the involved, difficult to use existing software, as well as high costs for the development of new customized software. The QuestionSys framework aims to solve these problems. It supports all steps of data collection from the creation of a questionnaire, through its execution on mobile devices, to the analysis of the collected data. In order to ensure a high degree of flexibility when creating questionnaires, questionnaires are mapped to process models which can then be executed by mobile devices. In the context of this thesis, a lightweight mobile process engine has been developed that allows to execute the process models of the QuestionSys framework. The focus was on process execution, support for several operating systems and easy extensibility. For this purpose, this thesis discusses related work, before the architecture of the engine is presented on the basis of defined requirements. In the following chapter, parts of the implementation are explained, which ultimately leads to an outlook

    Understanding the Error Behavior of Complex Critical Software Systems through Field Data

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    Software systems are the basis for human everyday activities, which are increasingly dependent on software. Software is an integral part of systems we interact with in our daily life raging form small systems for entertainment and domotics, to large systems and infrastructures that provide fundamental services such as telecommunication, transportation, and financial. In particular, software systems play a key role in the context of critical domains, supporting crucial activities. For example, ground and air transportation, power supply, nuclear plants, and medical applications strongly rely on software systems: failures affecting these systems can lead to severe consequences, which can be catastrophic in terms of business or, even worse, human losses. Therefore, given the growing dependence on software systems in life- and critical-applications, dependability, has become among one of the most relevant industry and research concerns in the last decades. Software faults have been recognized as one of the major cause for system failures since the hardware failure rate has been decreasing over the years. Time and cost constraints, along with technical limitations, often do not allow to fully validate the correctness of the software solely by means of testing; therefore, software might be released with residual faults that activate during operations. The activation of a fault generates errors which propagate through the components of the system, possibly leading to a failure. Therefore, in order to produce reliable software, it is important to understand how errors affect a software system. This is of paramount importance especially in the context of complex critical software systems, where the occurrence of a failure can lead to severe consequences. However, the analysis of the error behavior of this kind of system is not trivial. They are often distributed systems based on many interacting heterogeneous components and layers, including Off-The-Shelf (OTS), third party components and legacy systems. All these aspects, undermine the understanding of the error behavior of complex critical software system. A well established methodology to evaluate the dependability of operational systems and to identify their dependability bottlenecks is represented by field failure data analysis (FFDA), which is based on the monitoring and recording of errors and failures occurred during the operational phase of the system under real workload conditions, i.e., field data. Indeed, direct measurement and analysis of natural failures occurring under real workload conditions is among the most accurate ways to assess dependability characteristics. One of the main sources of field data, are monitoring techniques. The contribution of the thesis is to provide a methodology that allows understanding the error behavior of complex critical software systems by means of field data generated by the monitoring techniques already implemented in the target system. The use of available monitoring techniques allows to overcome the limitations imposed in the context of critical systems, avoiding severe changes in the system, and preserving its functionality and performance. The methodology is based on fault injection experiments that stimulate the target system with different error conditions. Injection experiments allow to accelerate the collection of error data naturally generated by the monitoring techniques already implemented in the system. The collected data are analyzed in order to characterize the behavior of the system under the occurred software errors. To this aim, the proposed methodology leverages a set of innovative means defined in this dissertation, i.e., (i) Error Propagation graphs, which allow to analyze the error propagation phenomena occurred in the target system and that can be inferred by the collected field data, and a set of metrics composed by (ii) Error Determination Degree, which allows gaining insights into the ability of error notifications of a monitoring technique to suggest either the fault that led to the error, or the failure the error led to in the system, (iii) Error Propagation Reportability, which allow understanding the ability of a monitoring technique at reporting the propagation of errors, and (iv) Data Dissimilarity, which allows gaining insights into the suitability of the data generated by the monitoring techniques for failure analysis. The methodology has been experimented on two instances of complex critical software systems in the field of Air Traffic Control (ATC), i.e., a communication middleware supporting data exchanging among ATC applications, and an arrival manager that is responsible for managing flight arrivals to a given airspace, within an industry-academia collaboration in the context of a national research project. Results show that field data generated by means of monitoring techniques already implemented in a complex critical software system can be leveraged to obtain insights about the error behavior exhibited by the target system, as well as about the potential beneficial locations for EDMs and ERMs. In addition, the proposed methodology also allowed to characterize the effectiveness of the monitoring techniques in terms of failure reporting, error propagation reportability, and data dissimilarity

    Automated Image Analysis of Offshore Infrastructure Marine Biofouling

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    Supplementary Materials: The following are available online at www.mdpi.com/2077-1312/6/1/2/s1 Acknowledgments: This project was funded by the Natural Environmental Research Council (NERC) project No.: NE/N019865/1. The authors would like to thank Melanie Netherway and Don Orr, from our project partner (company requested to remain anonymous) for the provision of survey footage and for supporting the project. In addition, many thanks to Oscar Beijbom, University California Berkley for providing guidance and support to the project. Additional thanks to Calum Reay, Bibby Offshore; George Gair, Subsea 7; and Alan Buchan, Wood Group Kenny for help with footage collection and for allowing us to host workshops with them and their teams, their feedback and insights were very much appreciated.Peer reviewedPublisher PD

    An Examination of Privacy Policies of US Government Senate Websites.

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    US Government websites are rapidly increasing the services they offer, but users express concerns about their personal privacy protection. To earn user's trust, these sites must show that personal data is protected, and the sites contain explicit privacy policies. This research studied privacy policy protection of 50 US Senate sites and found that few had comprehensive elements of privacy policies and a general lack of protection of personal data that could be obtain from the website. The study reviewed which specific privacy elements are most often mishandled, as well as suggestions for improving an overall online privacy practice

    Predicting and Evaluating Software Model Growth in the Automotive Industry

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    The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical. Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations. Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best

    A Survey on Economic-driven Evaluations of Information Technology

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    The economic-driven evaluation of information technology (IT) has become an important instrument in the management of IT projects. Numerous approaches have been developed to quantify the costs of an IT investment and its assumed profit, to evaluate its impact on business process performance, and to analyze the role of IT regarding the achievement of enterprise objectives. This paper discusses approaches for evaluating IT from an economic-driven perspective. Our comparison is based on a framework distinguishing between classification criteria and evaluation criteria. The former allow for the categorization of evaluation approaches based on their similarities and differences. The latter, by contrast, represent attributes that allow to evaluate the discussed approaches. Finally, we give an example of a typical economic-driven IT evaluation

    Legal Challenges and Market Rewards to the Use and Acceptance of Remote Sensing and Digital Information as Evidence

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    Bakgrund I den nutida forskningen Ă€r det essentiellt att företag tar hĂ€nsyn till medarbetarnas motivation sĂ„ att de gynnas av det arbetssĂ€tt som tillĂ€mpas. En arbetsmetod som blivit allt vanligare Ă€r konceptet Lean som ursprungligen kommer frĂ„n den japanska bilindustrin. Lean har idag utvecklats till ett allmĂ€ngiltigt koncept som tillĂ€mpas i flertalet branscher vĂ€rlden över. Trots att konceptet innebĂ€r flertalet positiva aspekter har det fĂ„tt utstĂ„ stark kritik nĂ€r det kommer till de mĂ€nskliga aspekterna och forskare har stĂ€llt sig frĂ„gan om Lean Ă€r "Mean". Kritiken hĂ€rleds frĂ€mst till medarbetares arbetsmiljö i form av stress och brist pĂ„ variation, sjĂ€lvbestĂ€mmande, hĂ€lsa och vĂ€lmĂ„ende. FĂ„ empiriska studier har dĂ€remot genomförts som undersöker konsekvenserna som Lean fĂ„r pĂ„ medarbetares upplevda motivation. Syfte VĂ„rt syfte Ă€r att undersöka och öka förstĂ„elsen för medarbetares upplevelser av motivationen i företag som tillĂ€mpar Lean. Vidare har studien för avsikt att utreda om det föreligger en paradox mellan Lean och vad som motiverar medarbetare pĂ„ en arbetsplats. Metod Studien har utgĂ„tt frĂ„n en kvalitativ metod via intervjuer. För att göra en djupare undersökning och analysera hur vĂ„rt fenomen, motivation, upplevs i en kontext med Lean tillĂ€mpade vi SmĂ„-N-studier. Vi har Ă€ven haft en iterativ forskningsansats som förenat den deduktiva och induktiva ansatsen dĂ€r studien pendlat mellan teorier och empiriska observationer fram tills det slutgiltiga resultatet. Slutsatser Utefter medarbetarnas upplevelser har vi identifierat att det inte föreligger nĂ„gon paradox mellan Lean och motivation eftersom övervĂ€gande antal medarbetare upplevde att de Ă€r motiverade Ă€ven om företaget tillĂ€mpar Lean. Dock har studien kunnat urskilja bĂ„de stödjande och motverkande faktorer nĂ€r det kommer till medarbetarnas upplevda arbetsförhĂ„llanden som i sin tur inverkar pĂ„ motivationen. De motverkande faktorerna menar vi frĂ€mst beror pĂ„ att arbetsförhĂ„llandena i somliga fall innehĂ„ller höga prestationskrav, mĂ„lstyrning samt standardiseringar. Vidare upplevs motivationen överlag som mer positiv nĂ€r företagen anvĂ€nder en mjukare form av Lean dĂ€r samtliga medlemmars intressen beaktas.Background In modern research, it is essential that companies consider employees’ motivation so that they benefit from the applied practices. A working method that has become increasingly common is the concept Lean, which has its origin in the Japanese automotive industry. Today, Lean has evolved into a universal concept that is applied in many industries worldwide. Although the concept involves numerous positive aspects it has endured strong criticism when it comes to the human aspects and researchers have raised the question if Lean is "Mean". Criticism is derived primarily to employees’ working conditions in terms of stress and lack, variation, autonomy, health and wellbeing. However, few empirical studies have been carried out that examines the impact that Lean has on employees’ experienced motivation. Aim The aim is to increase the understanding of employees’ experienced motivation in companies that practice Lean. Further on the study has the intention to investigate if there is a paradox between Lean and what motivates employees on work. Methodology The study has been conducted through a qualitative method by interviews and to be able to do a deeper examination and analyze how our phenomenon, motivation, is experienced in a Lean context we applied small-N-studies. Our strategy has been iterative, combining both a deductive and inductive approach, where the study has varied between theories and empirical observations until the final result. Conclusions We have identified that there is no paradox between Lean and motivation since the majority of employees’ experienced that they are motivated even though the company practice Lean. Nevertheless the study shows that there are both supportive and counteractive factors when it comes to the employees’ experienced working conditions. The counteractive factors consists foremost of high performance standards, goal steering and standardizations, and have in some cases a negative influence on the working conditions. Furthermore the experienced motivation is more positive overall when the companies use a softer form of Lean where all the members’ interests are taken into account
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