Markovic, M., Asif, W., Corsar, D., Jacobs, N., Edwards, P., Rajarajan, M., & Cottrill, C. (Accepted/In press). Towards automated privacy risk assessments in IoT systems. In M4IOT ’18: Workshop on Middleware and Applications for the Internet of Things New York: ACM.
Yeboah, G., Cottrill, C. D., Nelson, J. D., Corsar, D., Markovic, M., & Edwards, P. (2018) Factors Influencing Public Transport Passengers’ Pre-travel Information Seeking Behaviour: Advancing the Evidence Base (No. 18-02956). TRB 97th Annual Meeting Compendium of Papers. Transportation Research Board, Transportation Research Board 97th Annual Meeting, January 2017, Washington, D.C., United States.
Corsar D., Markovic M., Edwards P. (2018) Capturing the Provenance of Internet of Things Deployments. In: Belhajjame K., Gehani A., Alper P. (eds) Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science, vol 11017. Springer, Cham
Markovic M., Corsar D., Asif W., Edwards P., Rajarajan M. (2018) Towards Transparency of IoT Message Brokers. In: Belhajjame K., Gehani A., Alper P. (eds) Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science, vol 11017. Springer, Cham
Milan Markovic and Peter Edwards, "Semantic Stream Processing for IoT Devices in the Food Safety Domain", In Proceedings of the Posters and Demos Track of 12th International Conference on Semantic Systems - SEMANTiCS 2016, Leipzig, Germany, September 12-15, 2016.
Milan Markovic, Peter Edwards, Martin Kollingbaum and Alan Rowe, "Modelling Provenance of Sensor Data for Food Safety Compliance Checking", In Proceedings of the 6th International Provenance & Annotation Workshop-IPAW 2016, Vol. 9672, pp. 134-145, McLean, Virginia, June 2016, Springer Berlin Heidelberg.
David Corsar, Milan Markovic and Peter Edwards, "Social Media Data in Research: Provenance Challenges", In Proceedings of the 6th International Provenance & Annotation Workshop-IPAW 2016, June 2016, McLean, Virginia, USA, Springer Berlin Heidelberg.
In this paper we argue that understanding the provenance of social media datasets and their analysis is critical to addressing chal- lenges faced by the social science research community in terms of the reliability and reproducibility of research utilising such data. Based on analysis of existing projects that use social media data, we present a number of research questions for the provenance community, which if addressed would help increase the transparency of the research process, aid reproducibility, and facilitate data reuse in the social sciences.
Milan Markovic, "Utilising Provenance to Enhance Social Computation", PhD Thesis, University of Aberdeen, 2016.
David Corsar, Milan Markovic, Peter Edwards and John Nelson (2015), "The Transport Disruption Ontology", In The Semantic Web - ISWC 2015, Vol. 9367, pp. 329-336, October 2015, Bethlehem, Pennsylvania, USA,Springer Berlin Heidelberg.
David Corsar, Milan Markovic, Paul Gault, Mujtaba Mehdi, Peter Edwards, John Nelson, Caitlin Cottrill and Somayajulu Sripada (2015), "TravelBot: Journey Disruption Alerts Utilising Social Media and Linked Data", In Proceedings of the ISWC 2015 Posters & Demonstrations Track co-located with the 14th International Semantic Web Conference (ISWC-2015. Bethlehem, Pennsylvania, USA, October, 2015. (1486) CEUR Workshop Proceedings.
Milan Markovic, Peter Edwards, David Corsar, "SC-PROV: A Provenance Vocabulary for Social Computation", In Proceedings of the 5th International Provenance & Annotation Workshop-IPAW 2014, June 2014, Cologne, Germany, Springer Berlin Heidelberg.
Many online platforms employ networks of human workers to perform computational tasks that can be difficult for a machine (e.g. reporting travel disruption). Such systems have to make a range of decisions, for example, selection of suitable workers for a task. In this paper we present an approach that utilises Semantic Web technologies and provenance to support such decision-making processes.
Milan Markovic, Peter Edwards, David Corsar, "A Role for Provenance in Social Computation", In Proceedings of the 1st International Workshop on Crowdsourcing the Semantic Web co-located with 12th International Semantic Web Conference (ISWC 2013), 19 October 2013, Sydney, Australia, CEUR-WS series.
We argue that existing systems to support social computation suffer from a lack of transparency and that this can be addressed by integrating provenance capture mechanisms into such systems. We discuss how Semantic Web technologies can be used to facilitate this, and how the provenance record could be used to support various forms of decision-making about tasks such as workforce selection.
GetThere is a real-timepassengerinformationsystem(RTPI) for rural areas that uses a citizen sensing approach to acquire information from public transport users. This paper describes the use of ontologies in GetThere to represent and integrate citizen sensors with data required to provide RTPI (e.g. timetable and route descriptions). The service architecture used to manage semantic sensor data is also described.
Milan Markovic, Peter Edwards, David Corsar and Jeff Z. Pan, "Provenance and Social Machines". Digital Futures 2012, October 2012, Aberdeen, UK.
Social machines that outsource tasks to the crowd often have to address issues associated with the quality of contributions. In this paper we discuss a solution based on the maintenance and use of a provenance record.
Milan Markovic, Peter Edwards, David Corsar and Jeff Z. Pan, "DEMO: Managing the Provenance of Crowdsourced Disruption Reports, In Proceedings of the 4th International Provenance & Annotation Workshop-IPAW 2012, ISBN 978-3-642-34221-9, DOI: 10.1007/978-3-642-34222-6 17, 19-21 June 2012, Santa Barbara, California, USA, Springer Berlin Heidelberg.
Human computation systems that outsource tasks to the crowd often have to address issues associated with the quality of contributions. We are exploring the potential role of provenance to facilitate processes such as quality assessment within such systems. In this demo we present an application for managing traffic disruption reports generated by the crowd, and outline the technologies used to integrate provenance, linked data, and streams.
Peter Edwards, John Nelson, David Corsar, Nagendra Velaga, Mark Beecroft, Somayajulu Sripada, Christopher Baillie, Konstantinos Papangelis and Milan Markovic. "A Rural Real-time Passenger Information Ecosystem". In Proceedings of Mobisys 2012 Workshop on Next Generation Mobile Computing for Dynamic Personalised Travel Planning, ISBN: 978-1-4503-1325-4, DOI: 10.1145/2307874.2307885, June 2012. Lake District, UK.
Real time passenger information (RTPI) is commonly available in urban areas for immediately before and during journeys; however, non-urban areas (e.g. suburban, rural, and remote areas) often have little or no access to it. One of the main challenges in providing RTPI in rural areas is the lack of infrastructure for obtaining real time vehicle location, and for providing information to passengers. In the Informed Rural Passenger Project, we attempt to address this problem through the use of passengers' smartphones. We have developed GetThere, a smartphone app that crowdsources travel information, including vehicle location, from the passengers. This information is then integrated using linked data principles with open data from various sources, and used by various web services to provide RTPI to travellers in rural areas. Initial evaluation of the GetThere system has been performed on urban, suburban, and rural bus routes in the North East of Scotland.
The usefulness of intelligent applications/services reasoning with linked data is dependent on the availability and correctness of this data. The crowd potentially has an important role to play in performing the non-trivial tasks of creating, validating, and maintaining the online linked data sets used by applications and services. Additional information captured within a provenance record can be used in these tasks and others, such as evaluating the performance of the crowd and its members. In this paper we describe two roles for the crowd in the web of linked data (creation and maintenance), and argue that incorporating provenance into these tasks is beneficial especially in scenarios when the population of available workers is small. We also identify several challenges for the use of provenance in this context and define a set of requirements for a provenance model to address these challenges.