Utilising Provenance to Enhance Social Computation

Postprin

CrowdCE: A Collaboration Model for Crowdsourcing Software with Computing Elements

Today’s crowd computing models are mainly used for handling independent tasks with simplistic collaboration and coordination through business workflows. However, the software development processes are complex, intellectually and organizationally challenging business models. We present a model for software development that addresses key challenges. It is designed for the crowd in the development of a social application. Our model presents an approach to structurally decompose the overall computing element into atomic machine-based computing elements and human-based computing elements such that the elements can complement each other independently and socially by the crowd. We evaluate our approach by developing a business application through crowd work. We compare our model with the traditional software development models. The primary result was completed well for empowering the crowd

Modeling, enacting, and integrating custom crowdsourcing processes

Crowdsourcing (CS) is the outsourcing of a unit of work to a crowd of people via an open call for contributions. Thanks to the availability of online CS platforms, such as Amazon Mechanical Turk or CrowdFlower, the practice has experienced a tremendous growth over the past few years and demonstrated its viability in a variety of fields, such as data collection and analysis or human computation. Yet it is also increasingly struggling with the inherent limitations of these platforms: each platform has its own logic of how to crowdsource work (e.g., marketplace or contest), there is only very little support for structured work (work that requires the coordination of multiple tasks), and it is hard to integrate crowdsourced tasks into stateof-the-art business process management (BPM) or information systems. We attack these three shortcomings by (1) developing a flexible CS platform (we call it Crowd Computer, or CC) that allows one to program custom CS logics for individual and structured tasks, (2) devising a BPMN-based modeling language that allows one to program CC intuitively, (3) equipping the language with a dedicated visual editor, and (4) implementing CC on top of standard BPM technology that can easily be integrated into existing software and processes. We demonstrate the effectiveness of the approach with a case study on the crowd-based mining of mashup model patterns

Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation

A human computation system can be viewed as a distributed system in which the processors are humans, called workers. Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily. Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform. A human computation application comprises a group of tasks, each of them can be performed by one worker. Tasks might have dependencies among each other. In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view. Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects. By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications. In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance. We discuss how they are related to distributed systems and other areas of knowledge.Comment: 3 figures, 1 tabl

Investigating the virtual representation of human resources

Despite all the advancements of software technologies to increase the productivity of companies, their capabilities to find solutions for certain problem domains are still limited. For the purpose of collaboratively addressing problems, which cannot be solved by algorithms alone, humans as computational units that are connected in a network of hardware and software resources, are therefore becoming increasingly popular. In this diploma thesis we investigate virtual representations of human resources by analyzing properties of scientific work in the areas of human computation and by examining available sources of information, especially social networks like Facebook, Google+, LinkedIn, XING and GitHub. To comprise both the academic requirements and the offered data from the market products, which according to our comparison substantially differ, we present our concept of a virtual human resource representation. It provides thirteen categories of more than 150 definable attributes to create a basis for the representation of human resources in virtual environments that support collaborative work and business-related processes. Furthermore we show how to access human information using the example of Google+ and how to save this information as a virtual human web ontology instance to be potentially used in web based environments.Trotz aller Fortschritte in den Gebieten der Softwaretechnologie um die Produktivität von Firmen zu steigern, sind diese immer noch begrenzt, um Lösungen für gewisse Problemstellungen zu finden. Für den Zweck kollaborativ Proleme anzugehen, die durch Algorithmen alleine nicht gelöst werden können, werden Menschen, die als Recheneinheiten mit anderen Softwareund Hardware-Ressourcen verbunden sind, zunehmend populärer. In dieser Diplomarbeit untersuchen wir virtuelle Darstellungen von menschlichen Ressourcen durch die Analyse von Eigenschaften aus wissenschaftlichen Arbeiten in den Bereichen der Menschen-basierten Datenverarbeitung und durch die Prüfung verfügbarer Informationsquellen, insbesondere sozialer Netzwerke, wie Facebook, Google+, LinkedIn, XING und GitHub. Um sowohl die akademischen Anforderungen, als auch die angebotenen Daten aus den sozialen Netzwerken zu erfassen, die sich entsprechend unserem Vergleich wesentlich unterscheiden, präsentieren wir unser Konzept der virtuellen Repräsentation einer menschlichen Ressource. Sie bietet dreizehn Kategorien mit mehr als 150 definierbaren Eigenschaften an, um eine Grundlage für die Darstellung von menschlichen Ressourcen in virtuellen Umgebungen zu bilden, welche gemeinsames Arbeiten und unternehmensbezogene Prozesse unterstützen. Außerdem zeigen wir, wie man auf diese menschlichen Daten am Beispiel von Google+ zugreifen kann und wie diese Informationen als virtuelle menschliche Web-Ontologie-Instanzen gespeichert werden können, um möglicherweise in webbasierten Umgebungen eingesetzt zu werden

Models and systems for managing sensor and crowd-oriented processes

Business process modeling refers to the design of business process models, using business processes languages, to orchestrate the work executed by employees, their interaction with external entities, and work items that are necessary to achieve a predefined goal. Model-driven development allows people, generally called modelers, to design also sophisticated application logic using high-level abstractions. Process modeling is typically connected with business, hence, existing process languages focus principally on the support and orchestration of activities executed by employees, or by external entities like web services. However, there is a wide range of other application logics that are process-driven and that can benefit from high-level abstractions to model low-level details. Our initial research focuses on distributed UIs, which are a distributed type of actors, and then particularly concentrated on Wireless Sensor Networks (WSNs) and crowdsourcing, which are distributed and also autonomous types of actors (they can execute a part of an application logic in an autonomous and isolated fashion). Developing applications in these areas requires a deep knowledge of the field and a non-trivial programming effort; domain experts have to code an orchestrate the logic executed by these actors. Since these applications are highly process-driven, domain experts could take advantage of high-level, process-oriented modeling conventions to design the internal logic of these kinds of applications. However, the intrinsic complexity of these domains and the current state of the art of modeling paradigms make the design and execution of processes for these new actors challenging. In this dissertation we analyze, design, and present modeling formalism and systems for managing processes in these contexts. We tackle the challenges of the three areas with an approach that analyzes and extends existing process modeling languages, to enable the design of the processes, and with an architecture, similar for the three focuses, to support the development and execution of processes. Starting from our initial work on the orchestration of distributed UIs, for which we present a modeling language with a set of modeling constructs specific for the UIs, we then present our contribution to WSNs and crowdsourcing domains, which are: a modeling convention for the development of WSN applications, with high-level modeling constructs that abstract the low-level details of the networks; and a modeling paradigm to design processes that are partially executed by a crowd of people. These languages are all equipped with prototypes that contain a modeling tool to design processes and a runtime environment to support the execution. The impact of this work is not only to the domains we focused on but also to the business process domain as we demonstrate how a process modeling is a flexible and suitable formalism to design processes with very diverging, domain-specific requirements

Platform Design for Crowdsourcing and Future of Work

International audienceOnline job platforms have proliferated in the last few years. We anticipate a future where there exists thousands of such platforms covering wide swathes of tasks. These include crowdsourcing platforms such as Amazon Mechanical Turk (AMT), CrowdWorks, Figure Eight; specialized services such as ridehailing; matching markets such as TaskRabbit that matches workers with local demand and so on. It is widely anticipated that a vast majority of human workforce will be employed in these platforms. In this article, we initiate discussions about the under studied aspect of platform design-how to design platforms that maximize the satisfaction of various stakeholders. We also contribute a novel taxonomy for platform ecosystems that categorizes existing and emerging platforms. Finally, we discuss the need for interoperability between these platforms so that workers and requesters are not tied to a single platform

10301 Executive Summary and Abstracts Collection -- Service Value Networks

From 25.07.2010 to 30.07.2010, the Perspectives Workshop 10301 ``Perspectives Workshop: Service Value Networks \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar - all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.Comment: 40 pages main paper, 5 pages appendi