A Lightweight Process Engine for Enabling Advanced Mobile Applications

详细信息    查看全文

• 关键词：Mobile process engine ; Mobile data collection ; Smart mobile device ; Mobile process ; Mobile healthcare
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10033
• 期：1
• 页码：552-569
• 全文大小：3,285 KB
• 参考文献：1.Van der Aalst, W.M., Weijters, A.: Process mining: a research agenda. Comput. Ind. 53(3), 231–244 (2004)CrossRef
  2.Ainsworth, J., Palmier-Claus, J.E., Machin, M., Barrowclough, C., Dunn, G., Rogers, A., Buchan, I., Barkus, E., Kapur, S., Wykes, T., et al.: A comparison of two delivery modalities of a mobile phone-based assessment for serious mental illness: native smartphone application vs text-messaging only implementations. Med. Int. Res. 15(4), 1–13 (2013)
  3.Anandarajan, M., Anandarajan, A., Srinivasan, C.A.: Business Intelligence Techniques: A Perspective from Accounting and Finance. Springer, Heidelberg (2012)
  4.Commons, A.: Java Expression Language (JEXL). http://​commons.​apache.​org/​proper/​commons-jexl/​ , July 10, 2016
  5.Balagtas-Fernandez, F., Tafelmayer, M., Hussmann, H.: Mobia modeler: easing the creation process of mobile applications for non-technical users. In: Proceedings of the 15th Int’l Conference on Intelligent User Interfaces, pp. 269–272. ACM (2010)
  6.Baresi, L., Maurino, A., Modafferi, S.: Workflow partitioning in mobile information systems. In: Lawrence, E., Pernici, B., Krogstie, J. (eds.) MOBIS 2004. IIFIP, vol. 158, pp. 93–106. Springer, Heidelberg (2005). doi:10.​1007/​0-387-22874-8_​7 CrossRef
  7.Dunkel, J., Bruns, R.: Model-driven architecture for mobile applications. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 464–477. Springer, Heidelberg (2007). doi:10.​1007/​978-3-540-72035-5_​36 CrossRef
  8.Hackmann, G., Gill, C., Roman, G.C.: Extending BPEL for interoperable pervasive computing. In: IEEE Int’l Conference on Pervasive Services, pp. 204–213. IEEE (2007)
  9.Hackmann, G., Haitjema, M., Gill, C., Roman, G.-C.: Sliver: A BPEL workflow process execution engine for mobile devices. In: Dan, A., Lamersdorf, W. (eds.) ICSOC 2006. LNCS, vol. 4294, pp. 503–508. Springer, Heidelberg (2006). doi:10.​1007/​11948148_​47 CrossRef
  10.Heitkötter, H., Majchrzak, T.A., Kuchen, H.: Cross-platform model-driven development of mobile applications with \(md^2\) . In: Proceedings of the 28th Annual ACM Symp on Applied Computing, pp. 526–533. ACM (2013)
  11.Khambati, A., Grundy, J., Warren, J., Hosking, J.: Model-driven development of mobile personal health care applications. In: Proceedings of the 23rd IEEE/ACM Int’l Conference on Automated Software Engineering, pp. 467–470. IEEE Computer Society (2008)
  12.Kocurova, A., Oussena, S., Komisarczuk, P., Clark, T.: MobWEL - mobile context-aware content-centric workflow execution language. In: 3rd Int’l Conference on Advanced Collaborative Networks, Systems and Applications, pp. 61–70 (2013)
  13.Kunze, C., Zaplata, S., Lamersdorf, W.: Mobile processes: enhancing cooperation in distributed mobile environments. J. Comput. 2(1), 1–11 (2007)MathSciNet CrossRef
  14.Luckham, D.C.: Event Processing for Business: Organizing the Real-Time Enterprise. Wiley, Hoboken (2011)
  15.Mudano, A.S., Gary, L.C., Oliveira, A.L., Melton, M., Wright, N.C., Curtis, J.R., Delzell, E., Harrington, T.M., Kilgore, M.L., Lewis, C.E., et al.: Using tablet computers compared to interactive voice response to improve subject recruitment in osteoporosis pragmatic clinical trials: feasibility, satisfaction, and sample size. Patient Prefer. Adherence 7, 517 (2013)CrossRef
  16.Newman, S.: Building Microservices: Designing Fine-Grained Systems. O’Reilly Media Inc., Sebastopol (2015)
  17.Pajunen, L., Chande, S.: Developing workflow engine for mobile devices. In: 11th Int’l Conference Enterprise Distributed Object Computing, pp. 279–279. IEEE (2007)
  18.Pryss, R., Mundbrod, N., Langer, D., Reichert, M.: Supporting medical ward rounds through mobile task and process management. Inf. Syst. e-Bus. Manage. 13(1), 107–146 (2015)CrossRef
  19.Pryss, R., Reichert, M., Langguth, B., Schlee, W.: Mobile crowd sensing services for tinnitus assessment, therapy and research. In: IEEE 4th Int’l Conference on Mobile Services. IEEE Computer Society Press, June 2015
  20.Pryss, R., Tiedeken, J., Kreher, U., Reichert, M.: Towards flexible process support on mobile devices. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 150–165. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-17722-4_​11 CrossRef
  21.Reichert, M., Dadam, P.: Enabling adaptive process-aware information systems with ADEPT2. In: Cardoso, J., van der Aalst, W. (eds.) Handbook of Research on Business Process Modeling. Information Science Reference, Hershey, New York (March (2009)
  22.Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Systems: Challenges, Methods, Technologies. Springer, Heidelberg (2012)CrossRef MATH
  23.Russo, A., Mecella, M., de Leoni, M.: ROME4EU – A service-oriented process-aware information system for mobile devices. Softw. Pract. Experience 42(10), 1275–1314 (2012)CrossRef
  24.Schobel, J., Pryss, R., Schickler, M., Reichert, M.: Towards flexible mobile data collection in healthcare. In: 29th IEEE Int’l Symposium on Computer-Based Medical Systems. IEEE Computer Society Press, June 2016
  25.Schobel, J., Pryss, R., Schickler, M., Ruf-Leuschner, M., Elbert, T., Reichert, M.: End-user programming of mobile services: empowering domain experts to implement mobile data collection applications. In: IEEE 5th Int’l Conference on Mobile Services. IEEE Computer Society Press, June 2016
  26.Weerawarana, S., Curbera, F., Leymann, F., Storey, T., Ferguson, D.F.: Web Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL. WS-Reliable Messaging and More, Prentice Hall PTR (2005)
  27.Weske, M.: Business Process Management: Concepts, Languages, Architectures. Springer, Heidelberg (2012)CrossRef
  28.Zaplata, S., Hamann, K., Kottke, K., Lamersdorf, W.: Flexible execution of distributed business processes based on process instance migration. J. Syst. Integr. 1(3), 3–16 (2010)
  
• 作者单位：Johannes Schobel (20)
  Rüdiger Pryss (20)
  Marc Schickler (20)
  Manfred Reichert (20)
  
  20. Institute of Databases and Information Systems, Ulm University, Ulm, Germany
  
• 丛书名：On the Move to Meaningful Internet Systems: OTM 2016 Conferences
• ISBN：978-3-319-48472-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10033

文摘

The widespread dissemination of smart mobile devices offers new perspectives for timely data collection in large-scale scenarios. However, realizing sophisticated mobile data collection applications raises various technical issues like the support of different mobile operating systems and their platform-specific features. Often, specifically tailored mobile applications are implemented in order to meet particular requirements. In this context, changes of the data collection procedure become costly and profound programming skills are needed to adapt the respective mobile application accordingly. To remedy this drawback, we developed a model-driven approach, enabling end-users to create mobile data collection applications themselves. Basis to this approach are elements for flexibly defining sophisticated questionnaires, called instruments, which not only contain information about the data to be collected, but also on how the instrument shall be processed on different mobile operating systems. For the latter purpose, we provide an advanced mobile (kernel) service that is capable of processing the logic of sophisticated instruments on various platforms. The paper discusses fundamental requirements for such a kernel and introduces a generic architecture. The feasibility of this architecture is demonstrated through a prototypical implementation. Altogether, the mobile service allows for the effective use of smart mobile devices in a multitude of different data collection application scenarios (e.g., clinical and psychological trials).