Proceedings of the 4th Workshop on Interacting with Smart Objects 2015

These are the Proceedings of the 4th IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Enhancing Automation and Interoperability in Enterprise Crowdsourcing Environments

The last couple of years have seen a fascinating evolution. While the early Web predominantly focused on human consumption of Web content, the widespread dissemination of social software and Web 2.0 technologies enabled new forms of collaborative content creation and problem solving. These new forms often utilize the principles of collective intelligence, a phenomenon that emerges from a group of people who either cooperate or compete with each other to create a result that is better or more intelligent than any individual result (Leimeister, 2010; Malone, Laubacher, & Dellarocas, 2010). Crowdsourcing has recently gained attention as one of the mechanisms that taps into the power of web-enabled collective intelligence (Howe, 2008). Brabham (2013) defines it as “an online, distributed problem-solving and production model that leverages the collective intelligence of online communities to serve specific organizational goals” (p. xix). Well-known examples of crowdsourcing platforms are Wikipedia, Amazon Mechanical Turk, or InnoCentive. Since the emergence of the term crowdsourcing in 2006, one popular misconception is that crowdsourcing relies largely on an amateur crowd rather than a pool of professional skilled workers (Brabham, 2013). As this might be true for low cognitive tasks, such as tagging a picture or rating a product, it is often not true for complex problem-solving and creative tasks, such as developing a new computer algorithm or creating an impressive product design. This raises the question of how to efficiently allocate an enterprise crowdsourcing task to appropriate members of the crowd. The sheer number of crowdsourcing tasks available at crowdsourcing intermediaries makes it especially challenging for workers to identify a task that matches their skills, experiences, and knowledge (Schall, 2012, p. 2). An explanation why the identification of appropriate expert knowledge plays a major role in crowdsourcing is partly given in Condorcet’s jury theorem (Sunstein, 2008, p. 25). The theorem states that if the average participant in a binary decision process is more likely to be correct than incorrect, then as the number of participants increases, the higher the probability is that the aggregate arrives at the right answer. When assuming that a suitable participant for a task is more likely to give a correct answer or solution than an improper one, efficient task recommendation becomes crucial to improve the aggregated results in crowdsourcing processes. Although some assumptions of the theorem, such as independent votes, binary decisions, and homogenous groups, are often unrealistic in practice, it illustrates the importance of an optimized task allocation and group formation that consider the task requirements and workers’ characteristics. Ontologies are widely applied to support semantic search and recommendation mechanisms (Middleton, De Roure, & Shadbolt, 2009). However, little research has investigated the potentials and the design of an ontology for the domain of enterprise crowdsourcing. The author of this thesis argues in favor of enhancing the automation and interoperability of an enterprise crowdsourcing environment with the introduction of a semantic vocabulary in form of an expressive but easy-to-use ontology. The deployment of a semantic vocabulary for enterprise crowdsourcing is likely to provide several technical and economic benefits for an enterprise. These benefits were the main drivers in efforts made during the research project of this thesis: 1. Task allocation: With the utilization of the semantics, requesters are able to form smaller task-specific crowds that perform tasks at lower costs and in less time than larger crowds. A standardized and controlled vocabulary allows requesters to communicate specific details about a crowdsourcing activity within a web page along with other existing displayed information. This has advantages for both contributors and requesters. On the one hand, contributors can easily and precisely search for tasks that correspond to their interests, experiences, skills, knowledge, and availability. On the other hand, crowdsourcing systems and intermediaries can proactively recommend crowdsourcing tasks to potential contributors (e.g., based on their social network profiles). 2. Quality control: Capturing and storing crowdsourcing data increases the overall transparency of the entire crowdsourcing activity and thus allows for a more sophisticated quality control. Requesters are able to check the consistency and receive appropriate support to verify and validate crowdsourcing data according to defined data types and value ranges. Before involving potential workers in a crowdsourcing task, requesters can also judge their trustworthiness based on previous accomplished tasks and hence improve the recruitment process. 3. Task definition: A standardized set of semantic entities supports the configuration of a crowdsourcing task. Requesters can evaluate historical crowdsourcing data to get suggestions for equal or similar crowdsourcing tasks, for example, which incentive or evaluation mechanism to use. They may also decrease their time to configure a crowdsourcing task by reusing well-established task specifications of a particular type. 4. Data integration and exchange: Applying a semantic vocabulary as a standard format for describing enterprise crowdsourcing activities allows not only crowdsourcing systems inside but also crowdsourcing intermediaries outside the company to extract crowdsourcing data from other business applications, such as project management, enterprise resource planning, or social software, and use it for further processing without retyping and copying the data. Additionally, enterprise or web search engines may exploit the structured data and provide enhanced search, browsing, and navigation capabilities, for example, clustering similar crowdsourcing tasks according to the required qualifications or the offered incentives.:Summary: Hetmank, L. (2014). Enhancing Automation and Interoperability in Enterprise Crowdsourcing Environments (Summary). Article 1: Hetmank, L. (2013). Components and Functions of Crowdsourcing Systems – A Systematic Literature Review. In 11th International Conference on Wirtschaftsinformatik (WI). Leipzig. Article 2: Hetmank, L. (2014). A Synopsis of Enterprise Crowdsourcing Literature. In 22nd European Conference on Information Systems (ECIS). Tel Aviv. Article 3: Hetmank, L. (2013). Towards a Semantic Standard for Enterprise Crowdsourcing – A Scenario-based Evaluation of a Conceptual Prototype. In 21st European Conference on Information Systems (ECIS). Utrecht. Article 4: Hetmank, L. (2014). Developing an Ontology for Enterprise Crowdsourcing. In Multikonferenz Wirtschaftsinformatik (MKWI). Paderborn. Article 5: Hetmank, L. (2014). An Ontology for Enhancing Automation and Interoperability in Enterprise Crowdsourcing Environments (Technical Report). Retrieved from http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-155187

A framework for design assurance in developing embedded systems

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringStephen A. DyerSteven WarrenEmbedded systems control nearly every device we encounter. Examples abound: appliances, scientific instruments, building environmental controls, avionics, communications, smart phones, and transportation subsystems. These embedded systems can fail in various ways: performance, safety, and meeting market needs. Design errors often cause failures in performance or safety. Market failures, particularly delayed schedule release or running over budget, arise from poor processes. Rigorous methods can significantly reduce the probability of failure. Industry has produced and widely published “best practices” that promote rigorous design and development of embedded systems. Unfortunately, 20 to 35% of development teams do not use them, which leads to operational failures or missed schedules and budgets. This dissertation increases the potential for success in designing and developing embedded systems through the following: 1. It identifies, through literature review, the reasons and factors that cause teams to avoid best practices, which in turn contribute to development failures. 2. It provides a framework, as a psychologically unbiased mediator, to help teams institute best practices. The framework is both straightforward to implement and use and simple to learn. 3. It examines the feasibility of both crowdsourcing and the Delphi method to aid, through anonymous comments on proposed projects, unbiased mediation and estimation within the framework. In two separate case studies, both approaches resulted in underestimation of both required time and required effort. The wide variance in the surveys’ results from crowdsourcing indicated that approach to not be particularly useful. On the other hand, convergence of estimates and forecasts in both projects resulted when employing the Delphi method. Both approaches required six or more weeks to obtain final results. 4. It develops a recommendation model, as a plug-in module to the framework, for the build-versus-buy decision in design of subsystems. It takes a description of a project, compares designing a custom unit with integrating a commercial unit into the final product, and generates a recommendation for the build-versus-buy decision. A study of 18 separate case studies examines the sensitivity of 14 parameters in making the build-versus-buy decision when developing embedded systems. Findings are as follows: team expertise and available resources are most important; partitioning tasks and reducing interdependence are next in importance; the quality and support of commercial units are less important; and finally, premiums and product lifecycles have the least effect on the cost of development. A recommendation model incorporates the results of the sensitivity study and successfully runs on 16 separate case studies. It shows the feasibility and features of a tool that can recommend a build-or-buy decision. 5. It develops a first-order estimation model as another plug-in module to the framework. It aids in planning the development of embedded systems. It takes a description of a project and estimates required time, required effort, and challenges associated with the project. It is simple to implement and easy to use; it can be a spreadsheet, a Matlab model or a webpage; each provides an output like the model for the build-versus-buy decision