Systems supporting argumentation exist for a long time and have appeared with many different forms, ranging from online forums, to mind maps and decision support systems. These systems are used daily by teams that collaborate in data intensive and cognitive complex settings, such as teams involved in DNA analysis and social media research. However, current argumentation support systems do not offer the required functionalities for the meaningful analysis and aggregation of the associated complex data, thus not properly serving requirements related to gaining of insights and decision making. Aiming to address these issues, this paper presents an approach that integrates argumentation support services with data mining and natural language processing services to augment collaboration and decision making in the above teams. The proposed approach builds on well-tried principles of hypertext systems and adopts a service-oriented architecture to enable the contextualization of the foreseen services, as well as their execution and integration of their results in the argumentative discourse, hence facilitating its monitoring and understanding.

Online labor markets have gained significant importance in recent years, drawing considerable attention in academia and practice.These platforms enable workers worldwide to sell their labor services to a global pool of clients.However, the challenge lies in motivating workers effectively to enhance their productivity.To address this issue, we employ the selfdetermination theory and present a model that elucidates motivation's impact on productivity across various payment schemes.Additionally, we leverage the psychological trait theory and its suggested taxonomy to explore how compensation policies in online labor markets affect incentives differently based on individual differences.Our experiment tests predictions from a formal labor supply and productivity model for workers with varying compensation levels.The results indicate that intrinsic workers exhibit higher productivity when bonus rewards are introduced.Furthermore, our study confirms the presence of heterogeneous personality effects, emphasizing that increased worker productivity is primarily associated with conscientiousness and agreeableness traits.These findings illuminate the intricate mechanisms governing worker motivation and engagement in paid crowdsourcing environments.They provide valuable theoretical and managerial insights for researchers and crowdsourcing practitioners aiming to enhance worker productivity in online tasks.

We investigate the impact of non-cognitive skills on the quality of task-specific outcomes by conducting a quasi-experiment on a well-known online crowdsourcing platform. We show that worker’s performance varies with personality traits, gender, human capital, crowdsourcing experience and work effort. Regarding the effects of non-cognitive skills, we find that worker’s performance in online microtasks is positively related to extraversion and agreeableness. The positive impact of extroverts is also revealed when performance is adjusted for task completion time. These findings provide implications regarding the integration of selection mechanisms in online labour matching platforms aiming in uncovering micro-workers’ soft skills to improve performance and consequently the allocation of resources in online microtasks.

This paper reports on an innovative approach that aims to reduce information management costs in dataintensive and cognitively-complex biomedical environments. Recognizing the importance of prominent high-performance computing paradigms and large data processing technologies as well as collaboration support systems to remedy data-intensive issues, it adopts a hybrid approach by building on the synergy of these technologies. The proposed approach provides innovative Web-based workbenches that integrate and orchestrate a set of interoperable services that reduce the data-intensiveness and complexity overload at critical decision points to a manageable level, thus permitting stakeholders to be more productive and concentrate on creative activities.

Biomedical research has recently seen a vast growth in publicly and instantly available information, which are often complementary or overlapping. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data-mining algorithm to gene-expression data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and biologically interpreting gene-expression micro array data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data.

Aiming to address requirements concerning integration of services in the context of "big data", this paper presents an innovative approach that (i) ensures a flexible, adaptable and scalable information and computation infrastructure, and (ii) exploits the competences of stakeholders and information workers to meaningfully confront information management issues such as information characterization, classification and interpretation, thus incorporating the underlying collective intelligence. Our approach pays much attention to the issues of usability and ease-of-use, not requiring any particular programming expertise from the end users. We report on a series of technical issues concerning the desired flexibility of the proposed integration framework and we provide related recommendations to developers of such solutions. Evaluation results are also discussed.

This paper presents an innovative solution to Social Media monitoring. The proposed approach builds on the synergy between machine and collective human intelligence to enhance the underlying sense-making and decision making processes. In the setting under consideration, our approach reduces the data-intensiveness and overall complexity of real-life collaboration and decision making to a manageable level, thus permitting stakeholders to be more productive and concentrate on creative activities. This is achieved through a meaningful integration of dedicated data mining and collaborative decision making services.

We present CoPe_it!, an innovative web-based tool that complies with collaborative practices to provide members of diverse communities with the appropriate means to manage individual and collective knowledge during a complex sense-making and/or decision-making session. We discuss the rationale behin

Hypermedia systems and more specifically open hypermedia systems (OHS) provide a rich set of implementations of different hypertext flavors such as navigational hypertext, spatial hypertext or taxonomic hypertext. Additionally, these systems offer component-based modular architectures and address interoperability between hypertext domains. Despite multiple efforts of integrating Web clients, a widespread adoption of OHS technology by Web developers has not taken place. In this paper it is argued that Web Services - which offer a component model for Web applications - can be integrated in OHSs. An architectural integration is proposed, a step-by-step process is outlined and an example of integration is provided. This very approach is aimed to benefit both worlds: the Web community with new rich hypermedia functionality that extends the current navigational hypermedia, and the OHS community by opening its tools and platforms to the many developer groups of the Web community.