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Datenmanagement von Echtzeit-Verkehrsdaten

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Management digitaler Plattformen

Zusammenfassung

Im Gegensatz zu vielen vorranging statischen Mobilitätsplattformen treffen in einer produktiven Daten-getriebenen Mobilitätsdienste-Plattform schreibintensive Batch- bis Stream-Prozesse auf hohe Zugriffsraten von Nutzern über die Web-Services. Die gesamte Datenaufbereitung läuft asynchron ab und ermöglicht so die Unterteilung in verschiedene Softwarebausteine, auf welchen sich die jeweiligen technischen Anforderungen effizienter, weil einfacher als bei monolithischen Architekturen umsetzen lassen. Dieses Kapitel präsentiert für die verschiedenen Abschnitte des Datenflusses unterschiedliche Skalierungsmöglichkeiten.

Das Forschungsprojekt ExCELL wurde mit Mitteln des Bundesministeriums für Wirtschaft und Energie (BMWi) gefördert (Förderkennzeichen: 01MD15001D).

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Literaturverzeichnis

  • Abadi D (2010) Distinguishing Two Major Types of Column-Stores.

    Google Scholar 

  • Abbott (2010) Art of Scalability. Addison-Wesley Professional.

    Google Scholar 

  • Amazon (2017) AWS - Elastic Load Balancing Documentation. Abgerufen am 17.06.2017.

    Google Scholar 

  • Anikin D (2016) What an in-memory database is and how it persists data efficiently.

    Google Scholar 

  • Awadallah A (2009) Schema-on-Read vs. Schema-on-Write.

    Google Scholar 

  • Brunauer R, Rehrl K (2016) Big Data in der Mobilität–FCD Modellregion Salzburg. In: Big Data. Springer, S. 235-267.

    Google Scholar 

  • Catlett C, Malik T, Goldstein B, Giuffrida J, Shao Y, Panella A, Eder D, van Zanten E, Mitchum R, Thaler S (2014) Plenario: An Open Data Discovery and Exploration Platform for Urban Science. IEEE Data Engineering Bulletin 37 (4): S. 27-42.

    Google Scholar 

  • Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS) 26 (2): S. 4.

    Google Scholar 

  • DeCandia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W (2007) Dynamo: amazon’s highly available key-value store. ACM SIGOPS operating systems review 41 (6): S. 205-220.

    Google Scholar 

  • Feldman D (2017) Data Lakes, Data Hubs, Federation: Which One Is Best?

    Google Scholar 

  • Hagedorn S, Götze P, Sattler K-U (2017) Big Spatial Data Processing Frameworks: Feature and Performance Evaluation, EDBT.

    Google Scholar 

  • Halliday L (2017) Unleash the Power of Storing JSON in Postgres.

    Google Scholar 

  • Han G, Chen J, He C, Li S, Wu H, Liao A, Peng S (2015) A web-based system for supporting global land cover data production. ISPRS Journal of Photogrammetry and Remote Sensing 103: S. 66-80.

    Google Scholar 

  • Haynes D, Ray S, Manson S (2017) Terra Populus: Challenges and Opportunities with Heterogeneous Big Spatial Data. In: Advances in Geocomputation. Springer, S. 115-121.

    Google Scholar 

  • Helland P (2015) Immutability changes everything. Queue 13 (9): S. 40.

    Google Scholar 

  • Hellström I (2016) An Overview of Apache Streaming Technologies.

    Google Scholar 

  • Hsu L, Obe R (2012) File FDW Family: Part 1 file_fdw.

    Google Scholar 

  • Inel O, Khamkham K, Cristea T, Dumitrache A, Rutjes A, van der Ploeg J, Romaszko L, Aroyo L, Sips R-J (2014) Crowdtruth: Machine-human computation framework for harnessing disagreement in gathering annotated data, International Semantic Web Conference.

    Google Scholar 

  • International Organization for Standardization (2016) ISO/IEC 9075-9:2016: Information technology - Database languages - SQL - Part 9: Management of External Data (SQL/MED).

    Google Scholar 

  • Jin J, Gubbi J, Marusic S, Palaniswami M (2014) An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal 1 (2): S. 112-121.

    Google Scholar 

  • Jones M, Bradley J, Sakimura N (2015) Json web token (jwt).

    Google Scholar 

  • Karwin B (2010) SQL antipatterns: avoiding the pitfalls of database programming. Pragmatic Bookshelf.

    Google Scholar 

  • Kimball R, Ross M (2011) The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons.

    Google Scholar 

  • Kleppmann M (2012) Rethinking caching in web apps.

    Google Scholar 

  • Kleppmann M (2017) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. “ O’Reilly Media, Inc.”.

    Google Scholar 

  • Kulkarni A (2017) Why SQL is beating NoSQL, and what this means for the future of data.

    Google Scholar 

  • Lakshman A, Malik P (2010) Cassandra: a decentralized structured storage system. ACM SIGOPS operating systems review 44 (2): S. 35-40.

    Google Scholar 

  • Le-Phuoc D, Quoc HNM, Parreira JX, Hauswirth M (2011) The linked sensor middleware–connecting the real world and the semantic web, Semantic Web Challenge.

    Google Scholar 

  • Lehner W, Hümmer W, Redert M, Reinhard C (2001) Publish/Subscribe Systeme im Data-Warehousing: Mehr als nur eine Renaissance der Batch-Verarbeitung, vol 34(5). Advanced Techniques in Personalized Information Delivery. Friedrich-Alexander-Universität Erlangen-Nürnberg.

    Google Scholar 

  • Lott SF (2017) NoSQL Database Doesn’t Mean No Schema.

    Google Scholar 

  • Meier A, Kaufmann M, Kaufmann M (2016) SQL-& NoSQL-Datenbanken. Springer.

    Google Scholar 

  • Mulhuijzen R (2015) Reusing backend connections to increase performance.

    Google Scholar 

  • Nemil (2017) Why Did So Many Startups Choose MongoDB?

    Google Scholar 

  • Newman S (2015) Building microservices: designing fine-grained systems. “ O’Reilly Media, Inc.”.

    Google Scholar 

  • Nielsen J (2010) Website Response Times.

    Google Scholar 

  • Nittel S (2015) Real-time sensor data streams. SIGSPATIAL Special 7 (2): S. 22-28.

    Google Scholar 

  • Open Geospatial Consortium (2012) Sensor Observation Service Interface Standard

    Google Scholar 

  • Organization for Economic Cooperation and Development (2015) Data-Driven Innovation: Big Data for Growth and Well-Being. OECD Publishing, Paris.

    Google Scholar 

  • Ouyang A (2015) Cassandra: Daughter of Dynamo and BigTable.

    Google Scholar 

  • Pavlo A, Aslett M (2016) What’s Really New with NewSQL? ACM Sigmod Record 45 (2): S. 45-55.

    Google Scholar 

  • Pelkonen T, Franklin S, Teller J, Cavallaro P, Huang Q, Meza J, Veeraraghavan K (2015) Gorilla: A fast, scalable, in-memory time series database. Proceedings of the VLDB Endowment 8 (12): S. 1816-1827.

    Google Scholar 

  • Pfannenschmidt L, Wisniewski F (2016) Use Cases für Apache Kafka: „Viele Data-Probleme sind gar nicht so big“. JAXenter.

    Google Scholar 

  • Qu C, Calheiros RN, Buyya R (2016) Auto-scaling web applications in clouds: a taxonomy and survey.

    Google Scholar 

  • Rauber T, Rünger G (2013) Parallel programming: For multicore and cluster systems. Springer Science & Business Media.

    Google Scholar 

  • Reinheimer (2011) Squid Log Parsing for Proxy Billing.

    Google Scholar 

  • Stonebraker M (2015) The case of polystores.

    Google Scholar 

  • Terpolilli N (2015) Open Data Purists vs Open Data Pragmatists.

    Google Scholar 

  • Videla A, Williams JJ (2012) RabbitMQ in action: distributed messaging for everyone. Manning.

    Google Scholar 

  • Wysocki RM (2015) It’s a view, it’s a table… no, it’s a materialized view!

    Google Scholar 

  • Young G (2014) CQRS and Event Sourcing. Code on the Beach 2014.

    Google Scholar 

  • Zulauf C (2017) Memcached vs. Redis? https://stackoverflow.com/a/11257333/8919948. Abgerufen am 01.11.2017.

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Correspondence to Felix Kunde .

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Kunde, F., Pieper, S., Sauer, P. (2018). Datenmanagement von Echtzeit-Verkehrsdaten. In: Wiesche, M., Sauer, P., Krimmling, J., Krcmar, H. (eds) Management digitaler Plattformen. Informationsmanagement und digitale Transformation. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-21214-8_8

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  • DOI: https://doi.org/10.1007/978-3-658-21214-8_8

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