Abstract. Crowdsourcing has emerged as an important paradigm in human-problemsolvingtechniquesontheWeb.Oneapplicationofcrowdsourcing is to outsource certain tasks to the crowd that are difficult to implement as solutions based on software services only. Another benefit of crowdsourcing is the on-demand allocation of a flexible workforce. Businesses may outsource certain tasks to the crowd based on workload variations. The paper addresses the monitoring of crowd members ’ characteristics and the effective use of monitored data to improve the quality of work. Here we propose the extensions of standards such as Web Service Level Agreement (WSLA) to settle quality guarantees between crowd consumers and the crowdsourcing platform. Based on negotiated agreements, we provide a skill-based crowd scheduling algorithm. We evaluate our approach through simulations and show that our approach clearly outperforms a skill-agnostic scheduling approach.

Cross-enterprise collaboration has emerged as a key survival factor in today’s global markets. Semantic Web technologies are the basis to establish enterprise interoperability including data mediation support and automatic composition of services. Capabilities of services are semantically described andreasoningtechniquessupportthediscoveryandselection of services at run-time. These technologies are commonly based on precisely defined enterprise ontologies. In contrast to Semantic Web technologies that cover interactions between (technical) services, human collaborations emerge based on social preferences. Social networks have become a mass phenomenon. The fundamental aspects of these networks are to manage personal contacts and to share profile information with friends. These principles are increasingly harnessed in businesses and professional environments. In a manner similar to service-oriented systems, they enable flexible discovery and dynamic collaborations between participants. In this paper, we discuss the concept of social overlays for Web service based collaboration infrastructures. This mechanism enables information flows between actors in order to allow for flexible group formations in highly dynamic large-scale networks.

Abstract—Social networks have emerged from niche existence to a mass phenomenon. Nowadays, their fundamental concepts, such as managing personal contacts and sharing profile information, are increasingly harnessed for businesses in professional environments. Similar to service-oriented networks, they allow flexible discovery on demand and loose coupling of participants. Establishing social links facilitates cooperation and enables selective sharing of information. Intuitively, one shares more information with his connected neighbors and less or even none with unrelated individuals. Today, information is one of the most important and valuable goods in business networks. Being informed about ongoing collaborations and upcoming trends is a key success factor. Thus, in professional networks, participants aim at strategically establishing connections to enable reliable information flows. In this paper, we especially highlight an opportunistic model that let mediators connect actually unrelated actors in order to benefit from information mediation. We further discuss a framework that implements this model for service-oriented professional virtual communities. Keywords-social networks, information mediation, strategic link establishment, reciprocity, structural holes I.

Abstract. In recent years, the World Wide Web (WWW) has transformed to a gigantic social network where people interact and collaborate in diverse online communities. By using Web 2.0 tools, people contribute content and knowledge at a rapid pace. Knowledge-intensive social networks such as Q/A communities offer a great source of expertise for crowdsourcing applications. Companies desiring to outsource human tasks to the crowd, however, demand for certain guarantees such as quality that can be expected from returned tasks. We argue that the quality of crowd-sourced tasks greatly depends on incentives and the users’ dynamically evolving expertise and interests. Here we propose expertise mining techniques that are applied in online social communities. Our approach recommends users by considering contextual properties of Q/A communities such as participation degree and topic-sensitive expertise. Furthermore, we discuss prediction mechanisms to estimate answering dynamics considering a person’s interest and social preferences.

Abstract. Crowdsourcing has emerged as an important paradigm in human problem-solving techniques on the Web. One application of crowdsourcing is to outsource certain tasks to the crowd that are difficult to implement in software. Another potential benefit of crowdsourcing is the on-demand allocation of a flexible workforce. Businesses may outsource tasks to the crowd based on temporary workload variations. A major challenge in crowdsourcing is to guarantee high-quality processing of tasks. We present a novel crowdsourcing marketplace that matches tasks to suitable workers based on auctions. The key to ensuring high quality lies in skilled members whose capabilities can be estimated correctly. We present a novel auction mechanism for skill evolution that helps to correctly estimate workers and to evolve skills that are needed. Evaluations show that this leads to improved crowdsourcing. Keywords: Human-centric BPM, Crowdsourcing, Online communities, Task markets, Auctions, Skill evolution

Cross-enterprise collaboration has emerged as a key survival factor in today’s global markets. Semantic Web technologies are the basis to establish enterprise interoperability including data mediation support and automatic composition of services. Capabilities of services are semantically described and reasoning techniques support the discovery and selection of services at run-time. In contrast to Semantic Web technologies that cover interactions between (technical) services, human collaboration emerges based on social preferences. Social networks have become a mass phenomenon and are increasingly used in businesses and professional environments. In a manner similar to service-oriented systems, they enable flexible discovery and dynamic collaboration between participants. In this paper, we discuss the concept of social overlays for Web service based collaboration infrastructures. This mechanism enables information flows between actors in order to allow for flexible group formation in highly dynamic large-scale networks. We present the implementation of a trusted information sharing framework and demonstrate how people adaptively share information according to the strength of social relations using SOA concepts. We evaluate technical concepts with in-depth experiments.

Social computing is perceived mainly as a vehicle for establishing and maintaining private relationships and thus lacks mainstream adoption in enterprises. Collaborative computing, however, is firmly established, but no tight integration of the two approaches exists. Here, the authors look at how to integrate people, in the form of human-based computing, and software services into one composite system. Business process management (BPM) and workflow systems have had tremendous success in the past two decades with respect to both mindshare and deployment. We can safely consider service-oriented architecture (SOA) — BPM’s most recent manifestation — to be a “business-as-usual ” design practice. On the other hand, we’re observing enterprises exploring, if not even embracing, social computing

Web-based collaboration and virtual environments supported by various Web 2.0 concepts enable the application of numerous monitoring, mining and analysis tools to study human interactions and team formation processes. The composition of an effective team requires a balance between adequate skill fulfillment and sufficient team connectivity. The underlying interaction structure reflects social behavior and relations of individuals and determines to a large degree how well people can be expected to collaborate. In this paper we address an extended team formation problem that does not only require direct interactions to determine team connectivity but additionally uses implicit recommendations of collaboration partners to support even sparsely connected networks. We provide two heuristics based on Genetic Algorithms and Simulated Annealing for discovering efficient team configurations that yield the best trade-off between skill coverage and team connectivity. Our self-adjusting mechanism aims to discover the best combination of direct interactions and recommendations when deriving connectivity. We evaluate our approach based on multiple configurations of a simulated collaboration network that features close resemblance to real world expert networks. We demonstrate that our algorithm successfully identifies efficient team configurations even when removing up to 40 % of experts from various social network configurations.

Interactions spanning multiple organizations have become an important aspect in today’s collaboration landscape. Organizations create alliances to fulfill strategic objectives. The dynamic nature of collaborations increasingly demands for automated techniques and algorithms to support the creation of such alliances. Our approach bases on the recommendation of potential alliances by discovery of currently relevant competence sources and the support of semi-automatic formation. The environment is service-oriented comprising humans and software services with distinct capabilities. To mediate between previously separated groups and organizations, we introduce the broker concept that bridges disconnected networks. We present a dynamic broker discovery approach based on interaction mining techniques and trust metrics. We evaluate our approach by using simulations in real Web services’ testbeds.

Interactions spanning multiple organizations have become an important aspect in today’s collaboration landscape. Organizations create alliances to fulfill strategic objectives. The dynamic nature of collaborations increasingly demands for automated techniques and algorithms to support the creation of such alliances. Our approach bases on the recommendation of potential alliances by discovery of currently relevant competence sources and the support of semi-automatic formation. The environment is service-oriented comprising humans and software services with distinct capabilities. To mediate between previously separated groups and organizations, we introduce the broker concept that bridges disconnected networks. We present a dynamic broker discovery approach based on interaction mining techniques and trust metrics. We evaluate our approach by using simulations in real Web services’ testbeds.