Advertisement

Collective Sensing Platforms

  • Martin Atzmueller
  • Martin Becker
  • Juergen Mueller
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

This chapter provides an overview of web-based information and communications technology platforms that collect and display sensor based information. We focus on collective sensing platforms that allow to extend the collected sensor information, e.g., using tags or other annotations. We provide an overview on such platforms and discuss critical issues such as big data and sensor cloud storage. Furthermore, we discuss specific technological challenges, covering the complete data cycle from the smartphone application to the web system, and its effectiveness.

Keywords

Sensor Node Wireless Sensor Network Data Stream Data Port Data Alignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Alamri, A., Ansari, W.S., Hassan, M.M., Hossain, M.S., Alelaiwi, A., Hossain, M.A.: A survey on sensor-cloud: architecture, applications, and approaches. Int. J. Distrib. Sens. Netw. (2013). doi:10.1155/2013/917923Google Scholar
  2. Atzmueller, M.: Onto collective intelligence in social media: exemplary applications and perspectives. In: Proceedings of 3rd International Workshop on Modeling Social Media (MSM 2012), Hypertext 2012. ACM, New York (2012)Google Scholar
  3. Atzmueller, M.: Subgroup discovery—advanced review. WIREs: Data Min. Knowl. Disc. 5(1), 35–49 (2015). doi:10.1002/widm.1144Google Scholar
  4. Atzmueller, M., Lemmerich, F.: VIKAMINE - open-source subgroup discovery, pattern mining, and analytics. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. Atzmueller, M., Puppe, F.: A case-based approach for characterization and analysis of subgroup patterns. J. Appl. Intell. 28(3), 210–221 (2008)CrossRefGoogle Scholar
  6. Atzmueller, M., Puppe, F., Buscher, H.P.: Profiling examiners using intelligent subgroup mining. In: Proceedings of 10th International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2005), pp. 46–51. Aberdeen (2005)Google Scholar
  7. Atzmueller, M., Kluegl, P., Puppe, F.: Rule-based information extraction for structured data acquisition using textmarker. In: Proceedings of Lernen, Wissensentdeckung und Adaptivität, LWA 2008, Würzburg, October 06–08, 2008. University of Würzburg, Würzburg (2008)Google Scholar
  8. Atzmueller, M., Lemmerich, F., Krause, B., Hotho, A.: Who are the spammers? Understandable local patterns for concept description. In: Proceedings of 7th Conference on Computer Methods and Systems (2009)Google Scholar
  9. Atzmueller, M., Beer, S., Puppe, F.: A data warehouse-based approach for quality management, evaluation and analysis of intelligent systems using subgroup mining. In: Proceedings of 22nd International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp. 402–407. AAAI Press, Palo Alto, CA (2009)Google Scholar
  10. Atzmueller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.E., Mitzlaff, F., Scholz, C., Stumme, G.: Enhancing social interactions at conferences. Inf. Technol. 53(3), 101–107 (2011)Google Scholar
  11. Atzmueller, M., Doerfel, S., Hotho, A., Mitzlaff, F., Stumme, G.: Face-to-face contacts at a conference: dynamics of communities and roles. In: Modeling and Mining Ubiquitous Social Media. International Workshops MSM 2011, Boston, MA, October 9, 2011, and MUSE 2011, Athens, September 5, 2011. Revised Selected Papers, Lecture Notes in Computer Science, vol. 7472, pp. 21–39. Springer, Berlin/Heidelberg (2012) doi:10.1007/978-3-642-33684-3_2Google Scholar
  12. Atzmueller, M., Becker, M., Doerfel, S., Kibanov, M., Hotho, A., Macek, B.E., Mitzlaff, F., Mueller, J., Scholz, C., Stumme, G.: Ubicon: observing social and physical activities. In: IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besancon, 20–23 November, 2012, pp. 317–324. IEEE, Washington, DC (2012). doi:10.1109/GreenCom.2012.75Google Scholar
  13. Atzmueller, M., Behrenbruch, K., Hoffmann, A., Kibanov, M., Macek, B.E., Scholz, C., Skistims, H., Söllner, M., Stumme, G.: Connect-U: A System for Enhancing Social Networking. In: Socio-Technical Design of Ubiquitous Computing Systems. Springer, Heidelberg (2014)Google Scholar
  14. Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., Macek, B.E., Mitzlaff, F., Mueller, J., Stumme, G.: Ubicon and its applications for ubiquitous social computing. New Rev. Hypermedia Multimedia 20(1), 53–77 (2014). doi:10.1080/13614568.2013.873488ADSCrossRefGoogle Scholar
  15. Atzmueller, M., Mueller, J., Becker, M.: Exploratory subgroup analytics on ubiquitous data. In: Mining, Modeling and Recommending ‘Things’ in Social Media. Lecture Notes in Artificial Intelligence, vol. 8940. Springer, Heidelberg (2015)Google Scholar
  16. Atzmueller, M., Doerfel, S., Mitzlaff, F. Description-Oriented Community Detection using Exhaustive Subgroup Discovery. Information Sciences, (329):965–984, 2016.Google Scholar
  17. Bannach, D., Amft, O., Lukowicz, P.: Rapid prototyping of activity recognition applications. IEEE Pervasive Comput. 7(2), 22–31 (2008). doi:10.1109/MPRV.2008.36CrossRefGoogle Scholar
  18. Bannach, D., Kunze, K.S., Weppner, J., Lukowicz, P.: Integrated tool chain for recording and handling large, multimodal context recognition data sets. In: Proceedings of the 12th ACM International Conference Adjunct Papers on Ubiquitous Computing, Ubicomp 2010, Copenhagen, September 26–29, 2010. pp. 357–358. ACM, New York (2010). doi:10.1145/1864431.1864434Google Scholar
  19. Baraki, H., Geihs, K., Hoffmann, A., Voigtmann, C., Kniewel, R., Macek, B.E., Zirfas, J.: Towards interdisciplinary design patterns for ubiquitous computing applications. Technical Report, Research Center for Information System Design (ITeG), University of Kassel (2014)Google Scholar
  20. Barnaghi, P., Sheth, A., Henson, C.: From data to actionable knowledge: big data challenges in the web of things. Intell. Syst. 28(6), 6–11 (2013). doi:10.1109/MIS.2013.142CrossRefGoogle Scholar
  21. Becker, M., Mueller, J., Hotho, A., Stumme, G.: A generic platform for ubiquitous and subjective data. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013; 1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications, PUCAA 2013, Zurich, September 8–12, 2013. pp. 1175–1182. ACM, New York (2013). doi:10.1145/2494091.2499776Google Scholar
  22. Bishop, J., Klavins, E.: Collective sensing with self-organizing robots. In: Proceedings of 45th IEEE Conference on Decision and Control, CDC 2006, San Diego, CA, December 13–15, 2006, pp. 4175–4181. IEEE, New York (2006). doi:10.1109/CDC.2006.377102Google Scholar
  23. Blaschke, T., Hay, G.J., Weng, Q., Resch, B.: Collective sensing: integrating geospatial technologies to understand urban systems–an overview. Remote Sens. 3(8), 1743–1776 (2011). doi:10.3390/rs3081743ADSCrossRefGoogle Scholar
  24. Cuzzocrea, A., Song, I.Y., Davis, K.C.: Analytics over large-scale multidimensional data: the big data revolution! In: Proceedings of 14th International Workshop on Data Warehousing and OLAP at 20th International Conference on Information and Knowledge Management, CIKM 2011, Glasgow, October 24–28, 2011. pp. 101–104. ACM, New York (2011). doi:10.1145/2064676.2064695Google Scholar
  25. Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008). doi:10.1145/1327452.1327492CrossRefGoogle Scholar
  26. Dey, A.K., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16(2), 97–166 (2001). doi:10.1207/S15327051HCI16234_02CrossRefGoogle Scholar
  27. Foster, I.T., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Proceedings of Grid Computing Environments Workshop at IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, GCE 2008, Austin, TX – November 12–16, 2008. IEEE, New York (2009). doi:10.1109/GCE.2008.4738445Google Scholar
  28. Haklay, M.: Citizen science and volunteered geographic information: overview and typology of participation. In: Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice, pp. 105–122. Springer, Netherlands (2013). doi:10.1007/978-94-007-4587-2_7Google Scholar
  29. Han, J., Haihong, J., Le, G., Du, J.: Survey on nosql database. In: Proceedings of 6th International Conference on Pervasive Computing and Applications, ICPCA 2011, Port Elizabeth, October 26–28, 2011. pp. 363–366. IEEE, New York (2011a). doi:10.1109/ICPCA.2011.6106531Google Scholar
  30. Han, J., Song, M., Song, J.: A novel solution of distributed memory nosql database for cloud computing. In: Proceedings of the IEEE/ACIS 10th International Conference on Computer and Information Science, ICIS 2011, Sanya, China, May 16–18, 2011. pp. 351–355. IEEE, New York (2011b). doi:10.1109/ICIS.2011.61Google Scholar
  31. Kibanov, M., Atzmueller, M., Scholz, C., Stumme, G.: Temporal evolution of contacts and communities in networks of face-to-face human interactions. Sci. China 57(3), 1–17 (2014)Google Scholar
  32. Klein, D., Tran-Gia, P., Hartmann, M.: Big data. Informatik-Spektrum 36(3), 319–323 (2013). doi:10.1007/s00287-013-0702-3CrossRefGoogle Scholar
  33. Kluegl, P., Atzmueller, M., Puppe, F.: Meta-level information extraction. In: Proceedings of the KI 2009: Advances in Artificial Intelligence. 32nd Annual German Conference on AI, Paderborn, September 2009. Lecture Notes in Computer Science, vol. 5803, pp. 233–240. Springer, Berlin/Heidelberg (2009). doi:10.1007/978-3-642-04617-9_30Google Scholar
  34. Klügl, P., Toepfer, M., Lemmerich, F., Hotho, A., Puppe, F.: Collective information extraction with context-specific consistencies. In: Proceedings of the ECML/PKDD, pp. 728–743 (2012)Google Scholar
  35. Kunze, K., Bannach, D.: Towards dynamically configurable context recognition systems. In: Proceedings of Activity Context Representation Workshops at the 26th AAAI Conference on Artificial Intelligence, AAAI 2012, Toronto, July 22–23, 2012. AAAI, Palo Alto, CA (2012)Google Scholar
  36. Leimeister, J.M.M.: Collective intelligence. Bus. Inf. Syst. Eng. 2(4), 245–248 (2010). doi:10.1007/s12599-010-0114-8CrossRefGoogle Scholar
  37. Macek, B.E., Scholz, C., Atzmueller, M., Stumme, G.: Anatomy of a conference. In: Proceedings of 23rd ACM Conference on Hypertext and Social Media, HT 2012, Milwaukee, WI, June 25–28, 2012, pp. 245–254. ACM, New York (2012). doi:10.1145/2309996.2310038Google Scholar
  38. Malone, T.W., Laubacher, R., Dellarocas, C.: The collective intelligence genome. Spring 51(3), 21–31 (2010)Google Scholar
  39. Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems, 1st edn. Manning, Shelter Island, NY (2013)Google Scholar
  40. Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Community assessment using evidence networks. In: Analysis of Social Media and Ubiquitous Data. International Workshops MSM 2010, Toronto, June 13, 2010, and MUSE 2010, Barcelona, September 20, 2010, Revised Selected Papers, Lecture Notes in Computer Science, vol. 6904, pp. 79–98. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23599-3_5Google Scholar
  41. Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: User-relatedness and community structure in social interaction networks. CoRR abs/1309.3888, pp. 1–20 (2013a)Google Scholar
  42. Mitzlaff, F., Atzmueller, M., Stumme, G., Hotho, A.: Semantics of user interaction in social media. In: Complex Networks IV. Proceedings of the 4th Workshop on Complex Networks CompleNet 2013. Studies in Computational Intelligence, vol. 476, pp. 13–25. Springer, Berlin/Heidelberg (2013b). doi:10.1007/978-3-642-36844-8_2Google Scholar
  43. Ponmagal, R.S., Raja, J.: An extensible cloud architecture model for heterogeneous sensor services. Int. J. Comput. Sci. Inf. Secur. 9(1), 147–155 (2011)Google Scholar
  44. Resch, B.: People as sensors and collective sensing-contextual observations complementing geo-sensor network measurements. In: Progress in Location-Based Services. Lecture Notes in Geoinformation and Cartography, pp. 391–406. Springer, Berlin/Heidelberg (2013). doi:10.1007/978-3-642-34203-5_22Google Scholar
  45. Salber, D., Dey, A.K., Abowd, G.D.: The context toolkit: aiding the development of context-enabled applications. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems, CHI 1999, Pittsburgh, PA, May 15–20, 1999. pp. 434–441. ACM, New York (1999). doi:10.1145/302979.303126Google Scholar
  46. Scholz, C., Atzmueller, M., Barrat, A., Cattuto, C., Stumme, G.: New insights and methods for predicting face-to-face contacts. In: Proceedings of 7th International AAAI Conference on Weblogs and Social Media, ICAPS 2013, Cambridge, MA, July 8–10, 2013. AAAI, Palo Alto, CA (2013)Google Scholar
  47. Vuran, M.C., Akan, Ö.B., Akyildiz, I.F.: Spatio-temporal correlation: theory and applications for wireless sensor networks. Comput. Netw. 45(3), 245–259 (2004). doi:10.1016/j.comnet.2004.03.007CrossRefzbMATHGoogle Scholar
  48. Witten, I.H., Moffat, A., Bell, T.C.: Managing Gigabytes Compressing and Indexing Documents and Images, 2nd edn. Morgan Kaufmann, Burlington, MA (1999)zbMATHGoogle Scholar
  49. Yuriyama, M., Kushida, T.: Sensor-cloud infrastructure - physical sensor management with virtualized sensors on cloud computing. In: Proceedings of 13th International Conference on Network-Based Information Systems, NBiS 2010, Takayama, Japan, September 14–16, 2010, pp. 1–8. IEEE, New York, NY (2010). doi:10.1109/NBiS.2010.32Google Scholar
  50. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: Cluster Computing with Working Sets. In: Proceedings of USENIX Conference on Hot Topics in Cloud Computing, HotCloud’10, pp. 10–10. USENIX Association, Berkeley, CA (2010)Google Scholar
  51. Zheng, W., Xu, P., Huang, X., Wu, N.: Design a cloud storage platform for pervasive computing environments. Clust. Comput. 13(2), 141–151 (2010). doi:10.1007/s10586-009-0111-1CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Martin Becker
    • 2
    • 3
  • Juergen Mueller
    • 3
    • 4
  1. 1.Research Center for Information System DesignUniversity of KasselKasselGermany
  2. 2.Data Mining and Information Retrieval GroupUniversity of WuerzburgAm Hubland, WürzburgGermany
  3. 3.L3S Research CenterHannoverGermany
  4. 4.Data Engineering GroupUniversity of KasselKasselGermany

Personalised recommendations