Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Distributed Incremental View Maintenance

  • Milos NikolicEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_148



A database view is a virtual table defined by a query over the given data sources. Database systems recompute the contents of a view upon each reference to its name. A materialized view precomputes and stores the result of its definition query. Incremental view maintenance keeps the contents of materialized views up-to-date for changes in the source relations. Distributed incremental view maintenance is the procedure of updating materialized views when the data is distributed at multiple sites in a computer network.


Modern applications require real-time analytics over changing datasets. In a growing number of domains – Internet of Things (IoT), clickstream analysis, algorithmic trading, network monitoring, and fraud detection to name a few – applications monitor streaming data to promptly detect certain patterns, anomalies, or future trends. Such applications implement their logic using...

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  1. Chandramouli B, Goldstein J, Barnett M, DeLine R, Fisher D, Platt JC, Terwilliger JF, Wernsing J (2014) Trill: a high-performance incremental query processor for diverse analytics. PVLDB 8(4):401–412Google Scholar
  2. Chirkova R, Yang J (2012) Materialized views. Found Trends® Databases 4(4):295–405CrossRefGoogle Scholar
  3. Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRefGoogle Scholar
  4. Green TJ, Karvounarakis G, Tannen V (2007) Provenance semirings. In: PODS, pp 31–40Google Scholar
  5. Griffin T, Libkin L (1995) Incremental maintenance of views with duplicates. SIGMOD Rec 24(2):328–339CrossRefGoogle Scholar
  6. Koch C (2010) Incremental query evaluation in a ring of databases. In: PODS, pp 87–98Google Scholar
  7. Koch C, Ahmad Y, Kennedy O, Nikolic M, Nötzli A, Lupei D, Shaikhha A (2014) DBToaster: higher-order delta processing for dynamic, frequently fresh views. VLDB J 23(2):253–278CrossRefGoogle Scholar
  8. Murray DG, McSherry F, Isaacs R, Isard M, Barham P, Abadi M (2013) Naiad: a timely dataflow system. In: SOSP, pp 439–455Google Scholar
  9. Nikolic M, Dashti M, Koch C (2016) How to win a hot dog eating contest: distributed incremental view maintenance with batch updates. In: SIGMOD, pp 511–526Google Scholar
  10. Qian X, Wiederhold G (1991) Incremental recomputation of active relational expressions. IEEE Trans Knowl and Data Eng 3(3):337–341CrossRefGoogle Scholar
  11. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauly M, Franklin MJ, Shenker S, Stoica I (2012) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: NSDI, pp 15–28Google Scholar
  12. Zaharia M, Das T, Li H, Hunter T, Shenker S, Stoica I (2013) Discretized streams: fault-tolerant streaming computation at scale. In: SOSP, pp 423–438Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of OxfordOxfordUK