Incoop: MapReduce for incremental computations

By Pramod Bhatotia, Alexander Wieder, Rodrigo Seromenho Miragaia Rodrigues, Umut A. Acar, R. Pasquini. ACM Symposium on Cloud Computing, 2011

339 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/636e6696171c6cc6daa54127431365aec4d694de

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Citations in the corpus

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “In addition, other research has focused on programming models for more specific tasks, such as cross joining two sets [173], iterative computation [174], [175], in-memory computation with fault-tolerance [176], incremental computation [177]–[180], and data-dependent flow control decision [181].”
Discretized streams: fault-tolerant streaming computation at scale (2013, background): “Incoop [6] modiﬁes Hadoop to support incremental recomputation of job outputs when an input ﬁle changes, and also includes a mechanism for strag-gler recovery, but it still uses replicated on-disk storage between timesteps, and does not offer an explicit streaming interface with concepts like…”
Big data related technologies, challenges and future prospects (2015, background): “Data integration is the cornerstone of modern commercial informatics, which involves the combination of data from different sources and provides users with a uniform view of data [38].”
Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (2012, background): “Batched Stream Processing: Researchers have recently proposed several incremental processing systems for applications that periodically update a result with new data [21, 15, 4].”
Kineograph: taking the pulse of a fast-changing and connected world (2012, background): “Incoop [2], Haloop [3], DryadInc [25], Yahoo continuous bulk processing (CBP) [16], Comet [11], and Nectar [10] achieve this by allowing their applications to reuse existing computation results.”
Big Data: A Survey (2014, background): “, iterative computations [105, 106], fault-tolerant memory computations [107], incremental computations [108], and flow control decisionmaking related to data [109].”