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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literaturverzeichnis
Abadi D (2010) Distinguishing Two Major Types of Column-Stores.
Abbott (2010) Art of Scalability. Addison-Wesley Professional.
Amazon (2017) AWS - Elastic Load Balancing Documentation. Abgerufen am 17.06.2017.
Anikin D (2016) What an in-memory database is and how it persists data efficiently.
Awadallah A (2009) Schema-on-Read vs. Schema-on-Write.
Brunauer R, Rehrl K (2016) Big Data in der Mobilität–FCD Modellregion Salzburg. In: Big Data. Springer, S. 235-267.
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.
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.
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.
Feldman D (2017) Data Lakes, Data Hubs, Federation: Which One Is Best?
Hagedorn S, Götze P, Sattler K-U (2017) Big Spatial Data Processing Frameworks: Feature and Performance Evaluation, EDBT.
Halliday L (2017) Unleash the Power of Storing JSON in Postgres.
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.
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.
Helland P (2015) Immutability changes everything. Queue 13 (9): S. 40.
Hellström I (2016) An Overview of Apache Streaming Technologies.
Hsu L, Obe R (2012) File FDW Family: Part 1 file_fdw.
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.
International Organization for Standardization (2016) ISO/IEC 9075-9:2016: Information technology - Database languages - SQL - Part 9: Management of External Data (SQL/MED).
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.
Jones M, Bradley J, Sakimura N (2015) Json web token (jwt).
Karwin B (2010) SQL antipatterns: avoiding the pitfalls of database programming. Pragmatic Bookshelf.
Kimball R, Ross M (2011) The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons.
Kleppmann M (2012) Rethinking caching in web apps.
Kleppmann M (2017) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. “ O’Reilly Media, Inc.”.
Kulkarni A (2017) Why SQL is beating NoSQL, and what this means for the future of data.
Lakshman A, Malik P (2010) Cassandra: a decentralized structured storage system. ACM SIGOPS operating systems review 44 (2): S. 35-40.
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.
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.
Lott SF (2017) NoSQL Database Doesn’t Mean No Schema.
Meier A, Kaufmann M, Kaufmann M (2016) SQL-& NoSQL-Datenbanken. Springer.
Mulhuijzen R (2015) Reusing backend connections to increase performance.
Nemil (2017) Why Did So Many Startups Choose MongoDB?
Newman S (2015) Building microservices: designing fine-grained systems. “ O’Reilly Media, Inc.”.
Nielsen J (2010) Website Response Times.
Nittel S (2015) Real-time sensor data streams. SIGSPATIAL Special 7 (2): S. 22-28.
Open Geospatial Consortium (2012) Sensor Observation Service Interface Standard
Organization for Economic Cooperation and Development (2015) Data-Driven Innovation: Big Data for Growth and Well-Being. OECD Publishing, Paris.
Ouyang A (2015) Cassandra: Daughter of Dynamo and BigTable.
Pavlo A, Aslett M (2016) What’s Really New with NewSQL? ACM Sigmod Record 45 (2): S. 45-55.
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.
Pfannenschmidt L, Wisniewski F (2016) Use Cases für Apache Kafka: „Viele Data-Probleme sind gar nicht so big“. JAXenter.
Qu C, Calheiros RN, Buyya R (2016) Auto-scaling web applications in clouds: a taxonomy and survey.
Rauber T, Rünger G (2013) Parallel programming: For multicore and cluster systems. Springer Science & Business Media.
Reinheimer (2011) Squid Log Parsing for Proxy Billing.
Stonebraker M (2015) The case of polystores.
Terpolilli N (2015) Open Data Purists vs Open Data Pragmatists.
Videla A, Williams JJ (2012) RabbitMQ in action: distributed messaging for everyone. Manning.
Wysocki RM (2015) It’s a view, it’s a table… no, it’s a materialized view!
Young G (2014) CQRS and Event Sourcing. Code on the Beach 2014.
Zulauf C (2017) Memcached vs. Redis? https://stackoverflow.com/a/11257333/8919948. Abgerufen am 01.11.2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-658-21214-8_8
Published:
Publisher Name: Springer Gabler, Wiesbaden
Print ISBN: 978-3-658-21213-1
Online ISBN: 978-3-658-21214-8
eBook Packages: Business and Economics (German Language)