Program your city: Designing an urban integrated open data API

Cities accumulate and distribute vast sets of digital information. Many decision-making and planning processes in councils, local governments and organisations are based on both real-time and historical data. Until recently, only a small, carefully selected subset of this information has been released to the public – usually for specific purposes (e.g. train timetables, release of planning application through websites to name just a few). This situation is however changing rapidly. Regulatory frameworks, such as the Freedom of Information Legislation in the US, the UK, the European Union and many other countries guarantee public access to data held by the state. One of the results of this legislation and changing attitudes towards open data has been the widespread release of public information as part of recent Government 2.0 initiatives. This includes the creation of public data catalogues such as data.gov.au (U.S.), data.gov.uk (U.K.), data.gov.au (Australia) at federal government levels, and datasf.org (San Francisco) and data.london.gov.uk (London) at municipal levels. The release of this data has opened up the possibility of a wide range of future applications and services which are now the subject of intensified research efforts. Previous research endeavours have explored the creation of specialised tools to aid decision-making by urban citizens, councils and other stakeholders (Calabrese, Kloeckl & Ratti, 2008; Paulos, Honicky & Hooker, 2009). While these initiatives represent an important step towards open data, they too often result in mere collections of data repositories. Proprietary database formats and the lack of an open application programming interface (API) limit the full potential achievable by allowing these data sets to be cross-queried. Our research, presented in this paper, looks beyond the pure release of data. It is concerned with three essential questions: First, how can data from different sources be integrated into a consistent framework and made accessible? Second, how can ordinary citizens be supported in easily composing data from different sources in order to address their specific problems? Third, what are interfaces that make it easy for citizens to interact with data in an urban environment? How can data be accessed and collected

Towards a Tool-based Development Methodology for Pervasive Computing Applications

Despite much progress, developing a pervasive computing application remains a challenge because of a lack of conceptual frameworks and supporting tools. This challenge involves coping with heterogeneous devices, overcoming the intricacies of distributed systems technologies, working out an architecture for the application, encoding it in a program, writing specific code to test the application, and finally deploying it. This paper presents a design language and a tool suite covering the development life-cycle of a pervasive computing application. The design language allows to define a taxonomy of area-specific building-blocks, abstracting over their heterogeneity. This language also includes a layer to define the architecture of an application, following an architectural pattern commonly used in the pervasive computing domain. Our underlying methodology assigns roles to the stakeholders, providing separation of concerns. Our tool suite includes a compiler that takes design artifacts written in our language as input and generates a programming framework that supports the subsequent development stages, namely implementation, testing, and deployment. Our methodology has been applied on a wide spectrum of areas. Based on these experiments, we assess our approach through three criteria: expressiveness, usability, and productivity

Supporting service discovery, querying and interaction in ubiquitous computing environments.

In this paper, we contend that ubiquitous computing environments will be highly heterogeneous, service rich domains. Moreover, future applications will consequently be required to interact with multiple, specialised service location and interaction protocols simultaneously. We argue that existing service discovery techniques do not provide sufficient support to address the challenges of building applications targeted to these emerging environments. This paper makes a number of contributions. Firstly, using a set of short ubiquitous computing scenarios we identify several key limitations of existing service discovery approaches that reduce their ability to support ubiquitous computing applications. Secondly, we present a detailed analysis of requirements for providing effective support in this domain. Thirdly, we provide the design of a simple extensible meta-service discovery architecture that uses database techniques to unify service discovery protocols and addresses several of our key requirements. Lastly, we examine the lessons learnt through the development of a prototype implementation of our architecture

Setting the stage – embodied and spatial dimensions in emerging programming practices.

In the design of interactive systems, developers sometimes need to engage in various ways of physical performance in order to communicate ideas and to test out properties of the system to be realised. External resources such as sketches, as well as bodily action, often play important parts in such processes, and several methods and tools that explicitly address such aspects of interaction design have recently been developed. This combined with the growing range of pervasive, ubiquitous, and tangible technologies add up to a complex web of physicality within the practice of designing interactive systems. We illustrate this dimension of systems development through three cases which in different ways address the design of systems where embodied performance is important. The first case shows how building a physical sport simulator emphasises a shift in activity between programming and debugging. The second case shows a build-once run-once scenario, where the fine-tuning and control of the run-time activity gets turned into an act of in situ performance by the programmers. The third example illustrates the explorative and experiential nature of programming and debugging systems for specialised and autonomous interaction devices. This multitude in approaches in existing programming settings reveals an expanded perspective of what practices of interaction design consist of, emphasising the interlinking between design, programming, and performance with the system that is being developed

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Customizing smart environments: a tabletop approach

Smart environments are becoming a reality in our society and the number of intelligent devices integrated in these spaces is in-creasing very rapidly. As the combination of intelligent elements will open a wide range of new opportunities to make our lives easier, final users should be provided with a simplified method of handling complex intelligent features. Specifying behavior in these environments can be difficult for non-experts, so that more efforts should be directed towards easing the customization tasks. This work presents an entirely visual rule editor based on dataflow expressions for interactive tabletops which allows be-havior to be specified in smart environments. An experiment was carried out aimed at evaluating the usability of the editor in terms of non-programmers understanding of the abstractions and concepts involved in the rule model, ease of use of the pro-posed visual interface and the suitability of the interaction mechanisms implemented in the editing tool. The study revealed that users with no previous programming experience were able to master the proposed rule model and editing tool for specifying be-havior in the context of a smart home, even though some minor usability issues were detected.We would like to thank all the volunteers that participated in the empirical study. Our thanks are also due to the ASIC/Polimedia team for their computer hardware support. This work was partially funded by the Spanish Ministry of Science and Innovation under the National R&D&I Program within the project CreateWorlds (TIN2010-20488). It also received support from a postdoctoral fellowship within the VALi+d Program of the Conselleria d'Educacio, Cultura I Esport (Generalitat Valenciana) awarded to Alejandro Catala (APOSTD/2013/013). The work of Patricia Pons has been supported by the Universitat Politecnica de Valencia under the "Beca de Excelencia" program, and currently by an FPU fellowship from the Spanish Ministry of Education, Culture and Sports (FPU13/03831).Pons Tomás, P.; Catalá Bolós, A.; Jaén Martínez, FJ. (2015). Customizing smart environments: a tabletop approach. Journal of Ambient Intelligence and Smart Environments. 7(4):511-533. https://doi.org/10.3233/AIS-150328S51153374[1]C. Becker, M. Handte, G. Schiele and K. Rothermel, PCOM – a component system for pervasive computing, in: Proc. of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom’04), IEEE Computer Society, Washington, DC, USA, 2004, pp. 67–76.Bhatti, Z. W., Naqvi, N. Z., Ramakrishnan, A., Preuveneers, D., & Berbers, Y. (2014). Learning distributed deployment and configuration trade-offs for context-aware applications in Intelligent Environments. Journal of Ambient Intelligence and Smart Environments, 6(5), 541-559. doi:10.3233/ais-140274Bonino, D., & Corno, F. (2011). What would you ask to your home if it were intelligent? Exploring user expectations about next-generation homes. Journal of Ambient Intelligence and Smart Environments, 3(2), 111-126. doi:10.3233/ais-2011-0099[4]D. Bonino, F. Corno and L. Russis, A user-friendly interface for rules composition in intelligent environments, in: Ambient Intelligence – Software and Applications, Advances in Intelligent and Soft Computing, Vol. 92, Springer, Berlin, Heidelberg, 2011, pp. 213–217.[5]X. Carandang and J. Campbell, The design of a tangible user interface for a real-time strategy game, in: Proc. of the 34th International Conference on Information Systems (ICIS 2013), Association for Information Systems (AIS), 2013, pp. 3781–3790.Catalá, A., Garcia-Sanjuan, F., Jaen, J., & Mocholi, J. A. (2012). TangiWheel: A Widget for Manipulating Collections on Tabletop Displays Supporting Hybrid Input Modality. Journal of Computer Science and Technology, 27(4), 811-829. doi:10.1007/s11390-012-1266-4Catala, A., Pons, P., Jaen, J., Mocholi, J. A., & Navarro, E. (2013). A meta-model for dataflow-based rules in smart environments: Evaluating user comprehension and performance. Science of Computer Programming, 78(10), 1930-1950. doi:10.1016/j.scico.2012.06.010[8]C. Chen, Y. Xu, K. Li and S. Helal, Reactive programming optimizations in pervasive computing, in: Proc. of the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT’10), IEEE Computer Society, Washington, DC, USA, 2010, pp. 96–104.Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277-298. doi:10.1016/j.pmcj.2009.04.001Dey, A. K. (2009). Modeling and intelligibility in ambient environments. Journal of Ambient Intelligence and Smart Environments, 1(1), 57-62. doi:10.3233/ais-2009-0008[11]A.K. Dey, T. Sohn, S. Streng and J. Kodama, iCAP: Interactive prototyping of context-aware applications, in: Proc. of Pervasive Computing, Lecture Notes in Computer Science, Vol. 3968, Springer-Verlag, Berlin, Heidelberg, 2006, pp. 254–271.[12]N. Díaz, J. Lilius, M. Pegalajar and M. Delgado, Rapid prototyping of semantic applications in smart spaces with a visual rule language, in: Proc. of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, ACM, New York, NY, USA, 2013, pp. 1335–1338.Gámez, N., & Fuentes, L. (2011). FamiWare: a family of event-based middleware for ambient intelligence. Personal and Ubiquitous Computing, 15(4), 329-339. doi:10.1007/s00779-010-0354-0García-Herranz, M., Alamán, X., & Haya, P. A. (2010). Easing the Smart Home: A rule-based language and multi-agent structure for end user development in Intelligent Environments. Journal of Ambient Intelligence and Smart Environments, 2(4), 437-438. doi:10.3233/ais-2010-0085[17]J. Good, K. Howland and K. Nicholson, Young people’s descriptions of computational rules in role-playing games: An empirical study, in: Proc. of the 2010 IEEE Symposium on Visual Languages and Human-Centric Computing, IEEE, 2010, pp. 67–74.Gouin-Vallerand, C., Abdulrazak, B., Giroux, S., & Dey, A. K. (2013). A context-aware service provision system for smart environments based on the user interaction modalities. Journal of Ambient Intelligence and Smart Environments, 5(1), 47-64. doi:10.3233/ais-120190[19]S. Holloway and C. Julien, The case for end-user programming of ubiquitous computing environments, in: Proc. of the FSE/SDP Workshop on Future of Software Engineering Research (FoSER’10), ACM, New York, NY, USA, 2010, pp. 167–172.Horn, M. S., Crouser, R. J., & Bers, M. U. (2011). Tangible interaction and learning: the case for a hybrid approach. Personal and Ubiquitous Computing, 16(4), 379-389. doi:10.1007/s00779-011-0404-2[21]M.S. Horn, E.T. Solovey, R.J. Crouser and R.J.K. Jacob, Comparing the use of tangible and graphical programming languages for informal science education, in: Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI’09), ACM, New York, NY, USA, 2009, pp. 975–984.Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming. ACM Computing Surveys, 37(2), 83-137. doi:10.1145/1089733.1089734[23]J. Lee, L. Garduño, E. Walker and W. Burleson, A tangible programming tool for creation of context-aware applications, in: Proc. of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13), ACM, New York, NY, USA, 2013, pp. 391–400.Lézoray, J.-B., Segarra, M.-T., Phung-Khac, A., Thépaut, A., Gilliot, J.-M., & Beugnard, A. (2011). A design process enabling adaptation in pervasive heterogeneous contexts. Personal and Ubiquitous Computing, 15(4), 353-363. doi:10.1007/s00779-010-0356-y[25]B.Y. Lim and A.K. Dey, Assessing demand for intelligibility in context-aware applications, in: Proc. of the 11th International Conference on Ubiquitous Computing (Ubicomp’09), ACM, New York, NY, USA, 2009, pp. 195–204.[26]B.Y. Lim and A.K. Dey, Evaluating Intelligibility usage and usefulness in a context-aware application, in: Proc. of the 15th International Conference on Human-Computer, Lecture Notes in Computer Science, Vol. 8008, Springer, Berlin, Heidelberg, 2013, pp. 92–101.[28]P. Marshall, Do tangible interfaces enhance learning? in: Proc. of the 1st International Conference on Tangible and Embedded Interaction (TEI’07), ACM, New York, NY, USA, 2007, pp. 163–170.[30]C. Maternaghan and K.J. Turner, A configurable telecare system, in: Proc. of the 4th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA’11), ACM, New York, NY, USA, 2011, pp. 14:1–14:8.[31]D.A. Norman, The Invisible Computer, MITT Press, Cambridge, MA, USA, 1998.PANE, J. F., RATANAMAHATANA, C. «ANN», & MYERS, B. A. (2001). Studying the language and structure in non-programmers’ solutions to programming problems. International Journal of Human-Computer Studies, 54(2), 237-264. doi:10.1006/ijhc.2000.0410[33]P. Pons, A. Catala, J. Jaen and J.A. Mocholi, DafRule: Un modelo de reglas enriquecido mediante flujos de datos para la definición visual de comportamiento reactivo de entidades virtuales, in: Actas de las Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2011), 2011, 989–1002.Rasch, K. (2014). An unsupervised recommender system for smart homes. Journal of Ambient Intelligence and Smart Environments, 6(1), 21-37. doi:10.3233/ais-130242[35]K. Ryall, C. Forlines, C. Shen and M.R. Morris, Exploring the effects of group size and table size on interactions with tabletop shared-display groupware, in: Proc. of the 2004 ACM Conference on Computer Supported Cooperative Work, ACM, New York, NY, USA, 2004, pp. 284–293.Schmidt, A. (2000). Implicit human computer interaction through context. Personal Technologies, 4(2-3), 191-199. doi:10.1007/bf01324126[37]S.D. Scott, M. Sheelagh, T. Carpendale and K.M. Inkpen, Territoriality in collaborative tabletop workspaces, in: Proc. of the 2004 ACM Conference on Computer Supported Cooperative Work, ACM, New York, NY, USA, 2004, pp. 294–303.Sapounidis, T., & Demetriadis, S. (2013). Tangible versus graphical user interfaces for robot programming: exploring cross-age children’s preferences. Personal and Ubiquitous Computing, 17(8), 1775-1786. doi:10.1007/s00779-013-0641-7Shadbolt, N. (2003). Brain power. IEEE Intelligent Systems, 18(3), 2-3. doi:10.1109/mis.2003.1200718Shafti, L. S., Haya, P. A., García-Herranz, M., & Pérez, E. (2013). Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction. Journal of Ambient Intelligence and Smart Environments, 5(6), 563-587. doi:10.3233/ais-130232[41]A. Strawhacker, A. Sullivan and M.U. Bers, TUI, GUI, HUI: Is a bimodal interface truly worth the sum of its parts? in: Proc. of the 12th International Conference on Interaction Design and Children, ACM, New York, NY, USA, 2013, pp. 309–312.Sylla, C., Branco, P., Coutinho, C., & Coquet, E. (2011). TUIs vs. GUIs: comparing the learning potential with preschoolers. Personal and Ubiquitous Computing, 16(4), 421-432. doi:10.1007/s00779-011-0407-zTeruel, M. A., Navarro, E., López-Jaquero, V., Montero, F., Jaen, J., & González, P. (2012). Analyzing the understandability of Requirements Engineering languages for CSCW systems: A family of experiments. Information and Software Technology, 54(11), 1215-1228. doi:10.1016/j.infsof.2012.06.001[44]E. Tse, J. Histon, S.D. Scott and S. Greenberg, Avoiding interference: How people use spatial separation and partitioning in SDG workspaces, in: Proc. of the 2004 ACM Conference on Computer Supported Cooperative Work, ACM, New York, NY, USA, 2004, pp. 252–261.[45]P. Tuddenham, D. Kirk and S. Izadi, Graspables revisited: Multi-touch vs. tangible input for tabletop displays in acquisition and manipulation tasks, in: Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10), ACM, New York, NY, USA, 2010, pp. 2223–2232.[46]A. Uribarren, J. Parra, R. Iglesias, J.P. Uribe and D. López de Ipiña, A middleware platform for application configuration, adaptation and interoperability, in: Proc. of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, IEEE Computer Society, Washington, DC, USA, 2008, pp. 162–167.Weiser, M. (1991). The Computer for the 21st Century. Scientific American, 265(3), 94-104. doi:10.1038/scientificamerican0991-94[48]C. Wohlin, P. Runeson, M. Höst, M.C. Ohlsson, B. Regnell and A. Wesslén, Experimentation in Software Engineering: An Introduction, 1st edn, Kluwer Academic Publishers, Norwell, MA, USA, 2000.Zuckerman, O., & Gal-Oz, A. (2013). To TUI or not to TUI: Evaluating performance and preference in tangible vs. graphical user interfaces. International Journal of Human-Computer Studies, 71(7-8), 803-820. doi:10.1016/j.ijhcs.2013.04.00

Applying a User-centred Approach to Interactive Visualization Design

Analysing users in their context of work and finding out how and why they use different information resources is essential to provide interactive visualisation systems that match their goals and needs. Designers should actively involve the intended users throughout the whole process. This chapter presents a user-centered approach for the design of interactive visualisation systems. We describe three phases of the iterative visualisation design process: the early envisioning phase, the global specification hase, and the detailed specification phase. The whole design cycle is repeated until some criterion of success is reached. We discuss different techniques for the analysis of users, their tasks and domain. Subsequently, the design of prototypes and evaluation methods in visualisation practice are presented. Finally, we discuss the practical challenges in design and evaluation of collaborative visualisation environments. Our own case studies and those of others are used throughout the whole chapter to illustrate various approaches