Improving healthcare through human city interaction

The study of information technology has given insufficient focus to a) the structural factors and b) the community perspective. As information systems become increasingly integrated with human systems these wider influences are more important than ever. Human city interaction concepts including their interplay with cyber-physical systems and social computing are appropriated to healthcare. Through Structuration Theory, insights are given into how healthcare through the human city interaction lens can most effectively be improved

Entering an ecosystem: The hybrid OSS landscape from a developer perspective

Hybrid Open Source Software projects are virtual organizations that express characteristics of both static and dynamic behavior. They are choreographed through complex organizational structures that mix centralized governance with distributed community drivenness. While many communities use standard software tools to support their development processes, each community has its own ways of working and invisible power structures that influence how contributions are submitted, how they are verified and how decisions about the long-term direction of the software product are made. Navigating this environment is especially challenging for new developers who need to prove their abilities to gain rights to make contributions. This paper provides a viewpoint on the factors that influence a new developer's perception of the hybrid OSS developer community landscape. We apply an established developmental theory to build an initial model for the developer's context and discuss the model's validation, providing its practical and theoretical implications for building and managing on-line developer communities.Peer reviewe

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Information Quality in Social Networks: Predicting Spammy Naming Patterns for Retrieving Twitter Spam Accounts

The popularity of social networks is mainly conditioned by the integrity and the quality of contents generated by users as well as the maintenance of users’ privacy. More precisely, Twitter data (e.g. tweets) are valuable for a tremendous range of applications such as search engines and recommendation systems in which working on a high quality information is a compulsory step. However, the existence of ill-intentioned users in Twitter imposes challenges to maintain an acceptable level of data quality. Spammers are a concrete example of ill-intentioned users. Indeed, they have misused all services provided by Twitter to post spam content which consequently leads to serious problems such as polluting search results. As a natural reaction, various detection methods have been designed which inspect individual tweets or accounts for the existence of spam. In the context of large collections of Twitter users, applying these conventional methods is time consuming requiring months to filter o ut spam accounts in such collections. Moreover, Twitter community cannot apply them either randomly or sequentially on each user registered because of the dynamicity of Twitter network. Consequently, these limitations raise the need to make the detection process more systematic and faster. Complementary to the conventional detection methods, our proposal takes the collective perspective of users (or accounts) to provide a searchable information to retrieve accounts having high potential for being spam ones. We provide a design of an unsupervised automatic method to predict spammy naming patterns, as searchable information, used in naming spam accounts. Our experimental evaluation demonstrates the efficiency of predicting spammy naming patterns to retrieve spam accounts in terms of precision, recall, and normalized discounted cumulative gain at different rank

Combining Process Guidance and Industrial Feedback for Successfully Deploying Big Data Projects

Companies are faced with the challenge of handling increasing amounts of digital data to run or improve their business. Although a large set of technical solutions are available to manage such Big Data, many companies lack the maturity to manage that kind of projects, which results in a high failure rate. This paper aims at providing better process guidance for a successful deployment of Big Data projects. Our approach is based on the combination of a set of methodological bricks documented in the literature from early data mining projects to nowadays. It is complemented by learned lessons from pilots conducted in different areas (IT, health, space, food industry) with a focus on two pilots giving a concrete vision of how to drive the implementation with emphasis on the identification of values, the definition of a relevant strategy, the use of an Agile follow-up and a progressive rise in maturity

Towards Business-to-IT Alignment in the Cloud

Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes

Measures related to social and human factors that influence productivity in software development teams

Software companies need to measure their productivity. Measures are useful indicators to evaluate processes, projects, products, and people who are part of software development teams. The results of these measurements are used to make decisions, manage projects, and improve software development and project management processes. This research is based on selecting a set of measures related to social and human factors (SHF) that influence productivity in software development teams and therefore in project management. This research was performed in three steps. In the first step, there was performed a tertiary literature review aimed to identify measures related to productivity. Then, the identified measures were submitted for its evaluation to project management experts and finally, the measures selected by the experts were mapped to the SHF. A set of 13 measures was identified and defined as a key input for designing improvement strategies. The measures have been compared to SHF to evaluate the development team\u27s performance from a more human context and to establish indicators in productivity improvement strategies of software projects. Although the number of productivity measures related to SHF is limited, it was possible to identify the measures used in both traditional and agile contexts

A test case generation framework based on UML statechart diagram

Early software fault detection offers more flexibility to correct errors in the early development stages. Unfortunately, existing studies in this domain are not sufficiently comprehensive in describing the major processes of the automated test case generation. Furthermore, the algorithms used for test case generation are not provided or well described. Current studies also hardly address loops and parallel paths issues, and achieved low coverage criteria. Therefore, this study proposes a test case generation framework that generates minimized and prioritized test cases from UML statechart diagram with higher coverage criteria. This study, conducted a review of the previous research to identify the issues and gaps related to test case generation, model-based testing, and coverage criteria. The proposed framework was designed from the gathered information based on the reviews and consists of eight components that represent a comprehensive test case generation processes. They are relation table, relation graph, consistency checking, test path minimization, test path prioritization, path pruning, test path generation, and test case generation. In addition, a prototype to implement the framework was developed. The evaluation of the framework was conducted in three phases: prototyping, comparison with previous studies, and expert review. The results reveal that the most suitable coverage criteria for UML statechart diagram are all-states coverage, all-transitions coverage, alltransition-pairs coverage, and all-loop-free-paths coverage. Furthermore, this study achieves higher coverage criteria in all coverage criteria, except for all-state coverage, when compared with the previous studies. The results of the experts’ review show that the framework is practical, easy to implement due to it is suitability to generate the test cases. The proposed algorithms provide correct results, and the prototype is able to generate test case effectively. Generally, the proposed system is well accepted by experts owing to its usefulness, usability, and accuracy. This study contributes to both theory and practice by providing an early alternative test case generation framework that achieves high coverage and can effectively generate test cases from UML statechart diagrams. This research adds new knowledge to the software testing field, especially for testing processes in the model-based techniques, testing activity, and testing tool support