{"id":694,"date":"2021-07-06T17:42:24","date_gmt":"2021-07-06T17:42:24","guid":{"rendered":"http:\/\/library.gito.de\/?p=694"},"modified":"2021-08-06T17:34:11","modified_gmt":"2021-08-06T17:34:11","slug":"wi2020-community-tracks-9","status":"publish","type":"post","link":"https:\/\/library.gito.de\/en\/2021\/07\/wi2020-community-tracks-9\/","title":{"rendered":"WI2020 Community Tracks"},"content":{"rendered":"<p><\/p>\n<div>\n<div class=\"literatur\">\n<p>1. Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing. Methods and applications. Journal of Manufacturing Systems 48, 144\u2013156 (2018)<br \/>\n2. Luckow, A., Kennedy, K., Manhardt, F., Djerekarov, E., Vorster, B., Apon, A. (eds.): Automotive big data. Applications, workloads and infrastructures (2015)<br \/>\n3. Urbach, N., Drews, P., Ross, J.: Digital business transformation and the changing role of the IT function. MIS Quarterly Executive 16, 2\u20134 (2017)<br \/>\n4. Urbach, N., Ahlemann, F., B\u00f6hmann, T., Drews, P., Brenner, W., Schaudel, F., Sch\u00fctte, R.: The impact of digitalization on the IT department. Business &amp; Information Systems Engineering 61, 123\u2013131 (2019)<br \/>\n5. Mittal, S., Khan, M.A., Romero, D., Wuest, T.: Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, 1342\u20131361 (2019)<br \/>\n6. Surdilovic, D., Schreck, G., Schmidt, U. (eds.): Development of collaborative robots (COBOTS) for flexible human-integrated assembly automation (2010)<br \/>\n7. Bullinger, H.-J., Meiren, T., N\u00e4gele, R.: Smart services in manufacturing companies. Unpublished<br \/>\n8. Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. Journal of Manufacturing Systems 48, 157\u2013169 (2018)<br \/>\n9. Widom, J.: Research Problems in Data Warehousing. In: Fourth International Conference on Information and Knowledge Management (CIKM 1995) (1995)<br \/>\n10. Fang, H.: Managing data lakes in big data era: What&#8217;s a data lake and why has it became popular in data management ecosystem. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 820\u2013824. IEEE (2015 &#8211; 2015)<br \/>\n11. Sang, G.M., Xu, L., Vrieze, P.T.d. (eds.): A reference architecture for big data systems (2016)<br \/>\n12. Groggert, S., Wenking, M., Schmitt, R.H., Friedli, T. (eds.): Status quo and future potential of manufacturing data analytics. An empirical study (2017)<br \/>\n13. Li, J., Tao, F., Cheng, Y., Zhao, L.: Big data in product lifecycle management. The International Journal of Advanced Manufacturing Technology 81, 667\u2013684 (2015)<br \/>\n14. Tariq, R.S., Thabet, N.: Big data challenges. Computer Engineering &amp; Information Technology 4 (2015)<br \/>\n15. Ji, W., Wang, L.: Big data analytics based fault prediction for shop floor scheduling. Journal of Manufacturing Systems 43, 187\u2013194 (2017)<br \/>\n16. Santos, M.Y., Oliveira e S\u00e1, J., Costa, C., Galv\u00e3o, J., Andrade, C., Martinho, B., Lima, F.V., Costa, E. (eds.): A big data analytics architecture for industry 4.0 (2017)<br \/>\n17. Riggins, F.J., Wamba, S.F. (eds.): Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics (2015)<br \/>\n18. Li, S., Peng, G.C., Xing, F.: Barriers of embedding big data solutions in smart factories. Insights from SAP consultants. Industrial Management &amp; Data Systems 119, 1147\u20131164 (2019)<br \/>\n19. Gr\u00f6ger, C., Kassner, L., Hoos, E., K\u00f6nigsberger, J., Kiefer, C., Silcher, S., Mitschang, B. (eds.): The data-driven factory. Leveraging big industrial data for agile, learning and human-centric manufacturing (2016)<br \/>\n20. Hai, R., Geisler, S., Quix, C. (eds.): Constance. An intelligent data lake system (2016)<br \/>\n21. Cuzzocrea, A., Song, I.-Y., Davis, K.C.: Analytics over large-scale multidimensional data. In: International Workshop on Data Warehousing, pp. 101\u2013103<br \/>\n22. Bauernhansl, T., Hompel, M. ten, Vogel-Heuser, B.: Industrie 4.0 in Produktion, Automatisierung und Logistik. Anwendung &#8211; Technologien &#8211; Migration. Springer Fachmedien Wiesbaden, Wiesbaden (2014)<br \/>\n23. Wixom, B.H., Watson, H.J.: An empirical investigation of the factors affecting data warehousing success. MIS Quarterly 25, 17 (2001)<br \/>\n24. Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. ACM Sigmod Record 26, 65\u201374 (1997)<br \/>\n25. Gupta, H.: Selection of views to materialize in a data warehouse. IEEE Transactions on Knowledge and Data Engineering 17, 98\u2013112 (2005)<br \/>\n26. Tryfona, N., Busborg, F., Borch Christiansen, J.G. (eds.): starER. A conceptual model for data warehouse design (1999)<br \/>\n27. Kimball, R., Ross, M.: The data warehouse toolkit. The complete guide to dimensional modeling. John Wiley &amp; Sons, Indianapolis, IN (2011)<br \/>\n28. Davenport, T.H., Barth, P., Bean, R.: How &#8216;big data&#8217; is different. MIT Sloan Management Review 54 (2012)<br \/>\n29. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data. The next frontier for innovation, competition, and productivity. New York, NY (2011)<br \/>\n30. Mathis, C.: Data lakes. Datenbank Spektrum 17, 289\u2013293 (2017)<br \/>\n31. Bruckner, R.M., List, B., Schiefer, J. (eds.): Striving towards near real-time data integration for data warehouses (2002)<br \/>\n32. Miloslavskaya, N., Tolstoy, A.: Big data, fast data and data lake concepts. Procedia Computer Science 88, 300\u2013305 (2016)<br \/>\n33. van der Lans, Rick F.: Architecting the multi-purpose data lake with data virtualization. Lisse (2018)<br \/>\n34. Madera, C., Laurent, A. (eds.): The next information architecture evolution. The data lake wave (2016)<br \/>\n35. Hevner, A.R., Ram, S., March, S.T.: Design science in information systems research. MIS Quarterly 28, 75\u2013105 (2004)<br \/>\n36. March, S.T., Smith, G.F.: Design and natural science research on information technology. Decision Support Systems 15, 251\u2013266 (1995)<br \/>\n37. Benbasat, I., Goldstein, D.K., Mead, M.: The Case Research Strategy in Studies of Information Systems. MIS Quarterly 11, 369 (1987)<br \/>\n38. Eisenhardt, K.M.: Building theories from case study research. The Academy of Management Review 14, 532\u2013550 (1989)<br \/>\n39. Yin, R.K.: Case study research and applications. Design and methods. Sage, Los Angeles, CA (2018)<br \/>\n40. Ketokivi, M., Choi, T.: Renaissance of case research as a scientific method. Journal of Operations Management 32, 232\u2013240 (2014)<br \/>\n41. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future. Writing a literature review. MIS Quarterly 26, 13\u201323 (2002)<br \/>\n42. Cooper, D.R., Schindler, P.S.: Business research methods. McGraw-Hill Irwin, New York, NY (2014)<br \/>\n43. Bauer, D., Maurer, T., Henkel, C., Bildstein, A.: Big-Data-Analytik. Datenbasierte Optimierung produzierender Unternehmen. Zenodo, Stuttgart (2017)<br \/>\n44. Delen, D., Demirkan, H.: Data, information and analytics as services. Decision Support Systems 55, 359\u2013363 (2013)<br \/>\n45. Watson, H.J.: Tutorial: big data analytics. Concepts, technologies, and applications. Communications of the Association for Information Systems 34, 1247\u20131268 (2014)<br \/>\n46. Hu, H., Wen, Y., Chua, T.-S., Li, X.: Toward scalable systems for big data analytics. A technology tutorial. IEEE Access 2, 652\u2013687 (2014)<br \/>\n47. Freitag, M., K\u00fcck, M., Alla, A.A., L\u00fctjen, M.: Potenziale von Data Science in Produktion und Logistik. Teil 1 &#8211; Eine Einf\u00fchrung in aktuelle Ans\u00e4tze der Data Science. Industrie Management 31, 22\u201326 (2015)<br \/>\n48. Bokranz, R., Landau, K.: Handbuch Industrial Engineering. Produktivit\u00e4tsmanagement mit MTM. Sch\u00e4ffer-Poeschel, Stuttgart (2012)<br \/>\n49. Verband f\u00fcr Arbeitsgestaltung, Betriebsorganisation und Unternehmensentwicklung: Industrial Engineering. Standardmethoden zur Produktivit\u00e4tssteigerung und Prozessoptimierung. Hanser, M\u00fcnchen (2015)<br \/>\n50. Dorner, M., Stowasser, S.: Das Produktivit\u00e4tsmanagement des Industrial Engineering. \u2022 Modellentwicklung \u2022 F\u00fchrungssystem \u2022 Produktivit\u00e4tskennzahl \u2022 Verbesserung der Arbeitsproduktivit\u00e4t \u2022 Produktivit\u00e4tscontrolling. Zeitschrift f\u00fcr Arbeitswissenschaft 66, 212\u2013225 (2012)<br \/>\n51. Scheffler, A., Wirths, C.P.: Data innovation @ AXA Germany. Journey towards a datadriven insurer. In: Urbach, N., R\u00f6glinger, M. (eds.) Digitalization cases, pp. 363\u2013378. Springer International Publishing, Cham (2019)<br \/>\n52. Nyhuis, P., Reinhart, G., Abele, E. (eds.): Wandlungsf\u00e4hige Produktionssysteme. Heute die Industrie von morgen gestalten. Produktionstechnisches Zentrum Hannover, Garbsen (2008)<br \/>\n53. Berthold, M.R., Borgelt, C., H\u00f6ppner, F., Klawonn, F.: Guide to intelligent data analysis. How to intelligently make sense of real data. Springer London, London (2010)<br \/>\n54. Anagnostopoulos, I., Zeadally, S., Exposito, E.: Handling big data. Research challenges and future directions. The Journal of Supercomputing 72, 1494\u20131516 (2016)<br \/>\n55. Balachandran, B.M., Prasad, S.: Challenges and benefits of deploying big data analytics in the cloud for business intelligence. Procedia Computer Science 112, 1112\u20131122 (2017)<br \/>\n56. Assun\u00e7\u00e3o, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A.S., Buyya, R.: Big data computing and clouds. Trends and future directions. Journal of Parallel and Distributed Computing 79-80, 3\u201315 (2015)<br \/>\n57. Sheth, A.P.: Changing focus on interoperability in information systems. From system, syntax, structure to semantics. In: Goodchild, M., Egenhofer, M., Fegeas, R., Kottman, C. (eds.) Interoperating geographic information systems, 495, pp. 5\u201329. Springer US, Boston, MA (1999)<br \/>\n58. Gerloff, C., Cleophas, C. (eds.): Excavating the treasure of IoT data. An architecture to empower rapid data analytics for predictive maintenance of connected vehicles (2017)<br \/>\n59. Jacoby, M., Antoni\u0107, A., Kreiner, K., \u0141apacz, R., Pielorz, J. (eds.): Semantic interoperability as key to IoT platform federation (2017)<br \/>\n60. Wunder, M., Grosche, J.: Verteilte F\u00fchrungsinformationssysteme. Springer Berlin Heidelberg, Berlin, Heidelberg (2009)<br \/>\n61. Khatri, V., Brown, C.V.: Designing data governance. Communications of the ACM 53, 148\/152 (2010)<br \/>\n62. Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A.: Data quality for data science, predictive analytics, and big data in supply chain management. An introduction to the problem and suggestions for research and applications. International Journal of Production Economics 154, 72\u201380 (2014)<br \/>\n63. Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1, 51\u201359 (2013)<br \/>\n64. Baesens, B., Bapna, R., Marsden, J.R., Vanthienen, J., Zhao, J.L.: Transformational issues of big data and analytics in networked business. MIS Quarterly 40, 807\u2013818 (2016)65. Ghasemaghaei, M., Ebrahimi, S., Hassanein, K.: Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems 27, 101\u2013 113 (2018)<br \/>\n66. Bose, R.: Advanced analytics. Opportunities and challenges. Industrial Management &amp; Data Systems 109, 155\u2013172 (2009)<br \/>\n67. Divate, R., Sah, S., Singh, M.: High performance computing and big data. In: Srinivasan, S. (ed.) Guide to big data applications, 26, pp. 125\u2013147. Springer International Publishing, Cham (2018)<br \/>\n68. Singh, D., Reddy, C.K.: A survey on platforms for big data analytics. Journal of Big Data 2, 1\u201320 (2015)<br \/>\n69. Kimball, R., Ross, M.: The data warehouse toolkit. The definitive guide to dimensional modeling. John Wiley &amp; Sons, Indianapolis, IN (2013)<br \/>\n70. van der Aalst, W.: Process mining. Data science in action. Springer Berlin Heidelberg, Berlin, Heidelberg (2016)<br \/>\n71. Vermeulen, A.F.: Practical data science. A guide to building the technology stack for turning data lakes into business assets. Apress, Berkeley, CA (2018)<br \/>\n72. Periasamy, M., Chelliah, P.R.: Big data analytics. Enabling technologies and tools. In: Mahmood (Ed.) 2016 &#8211; data science and big data, pp. 221\u2013243<br \/>\n73. Diemer, J.: Sichere Industrie-4.0-Plattformen auf Basis von Community-Clouds. In: Vogel-Heuser, B., Bauernhansl, T., Hompel, M. ten (eds.) Handbuch Industrie 4.0 Bd.1. Produktion, pp. 177\u2013204. Springer Berlin Heidelberg, Berlin, Heidelberg (2017)<br \/>\n74. Krishnan, K.: Data warehousing in the age of big data. Morgan Kaufmann, Waltham, MA (2013)<br \/>\n75. D. Sudarsan, S., Jetley, R., Ramaswamy, S.: Security and privacy of big data. In: Big Data, pp. 121\u2013136<\/p>\n<\/div>\n<\/div>\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>1. Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing. Methods and applications. Journal of Manufacturing Systems 48, 144\u2013156 (2018) 2. Luckow, A., Kennedy, K., Manhardt, F., Djerekarov, E., Vorster, B., Apon, A. (eds.): Automotive big data. Applications, workloads and infrastructures (2015) 3. Urbach, N., Drews, P., Ross, J.:&hellip; <a class=\"more-link\" href=\"https:\/\/library.gito.de\/en\/2021\/07\/wi2020-community-tracks-9\/\">Continue reading <span class=\"screen-reader-text\">WI2020 Community Tracks<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[14],"tags":[],"class_list":["post-694","post","type-post","status-publish","format-standard","hentry","category-band-2","entry"],"acf":[],"_links":{"self":[{"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/posts\/694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/comments?post=694"}],"version-history":[{"count":2,"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/posts\/694\/revisions"}],"predecessor-version":[{"id":1364,"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/posts\/694\/revisions\/1364"}],"wp:attachment":[{"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/media?parent=694"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/categories?post=694"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/library.gito.de\/en\/wp-json\/wp\/v2\/tags?post=694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}