State-of-the-art practices have recognized the utility of leveraging human intervention as a crucial aspect of modern computing systems. The emerging crowdsourcing paradigm is based on harnessing human intelligence, effort and rational behaviors to augment computation and analysis. In addition to the crowdsourcing paradigm, new techniques have emerged that incorporate machine and human computational resources together forming a hybrid intelligence when addressing complex problems and tasks. This combined technique is particularly impactful if human and machine contributions can scale automatically in response to their respective efficiency and effectiveness when addressing subsets of a bigger problem - an approach that we have named mixed elastic systems. In this survey, we highlight state-of-the-art projects that investigate crowdsourcing, hybrid intelligence systems and mixed elastic systems. We also present a taxonomy and classification of the broader domain of human-enhanced computing systems as it assimilates crowdsourcing, hybrid intelligence, and mixed elastic systems.

Businesses and government agencies are continuously generating and collecting huge amounts of data and building related Big Data applications. Big Data applications involve the collaborative integration of APIs from different providers. A challenge in this domain is to guarantee the conformance of t

Crowd computing leverages human input in order to execute tasks that are computationally expensive, due to complexity and/or scale. Combined with automation, crowd computing can help solve problems efficiently and effectively. In this work, we introduce an elasticity framework that adaptively optimi

A major tenet of the service-oriented computing paradigm is the just-in-time approach to composing web services. While the majority of related research projects use nonfunctional evaluators to choose the best, cross-organizational composition scheme from many options, few projects leverage the real, domain-specific functional data. In this paper we describe a data-centric modelling approach that leverages the real operational data of any web service workflow. This model is a core aspect for the control of adaptive collaboration software that integrates web services that span distributed enterprises. Using Bayesian inference, we assess the message flow of long-running service workflows, and, in this paper, we introduce an operational model that represents the service workflow behavior. We believe that our operational model can be used for off-line or real-time adaptation of operational service workflows.

The "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track currently enjoys its 12th year. ACEC focuses on the dynamic collaboration that occurs between networked systems as realized by agent-based or adaptive solutions or services. The purpose of this track is to bring together research in the fields of software agents and adaptive computing, more broadly, as they relate to the challenges associated with efficient, effective collaboration. This report briefly discusses the content of the papers presented in the track by respective authors and collective knowledge derived from the underlying state-of-the-art.

Web services allow organizations to capture their human and software-based capabilities as modular software components that are called remotely over a network. In such service-oriented settings, it is important to establish an agreement that sets the obligations of the service provider and the expectations of the service consumer. Since traditional approaches such as Service Level Agreements (SLAs) are loosely defined with respect to data integrity aspects, in previous work, we proposed a formal model for specifying data-centric Web services. The goal is to formally and unambiguously specify the service behavior in terms of its underlying data model and data interactions. In this paper, we present our model and our effort in specifying and verifying data-centric Web services using three state-of-the-art specification languages: JML, Dafny and RESOLVE. Our goal is to study the feasibility of our proposed model and also to pinpoint the challenges and limitations of current specification and verification tools.

Research in transactions planning has recognized the evolvement of Web Services as an industry standard to implement transactional business processes. We proposed a data modeling and contracting framework for Web services and in this paper we are exploring how our framework can help formally verify data integrity properties in an ad-hoc transaction.

Service-oriented architecture (SOA) promotes a paradigm where ad-hoc applications are built by dynamically linking service-based software capabilities. Service providers follow specification standards to advertise their services' capabilities and to enable loosely coupled integration between their services and other businesses over the Web. A major challenge in this domain is interpreting the data that must be marshaled between consumer and producer systems. We propose a framework to support formal modeling and contracts for data-centric Web services. We demonstrate how this framework can be used to verify correctness properties for composition of services.

Data-centric Web services are services whose behavior is determined by their interactions with a repository of stored data. The lack of data specification in current Web service standards potentially leads to erroneous use of these services by their consumers. In this work, we propose using formal data contracts to decrease ambiguity about a service behavior, to fully verify a composition of services, and to guarantee data integrity within a composition of services.