Apache research paper

The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since The criteria we used to select the 20 top papers are by using citation counts from three academic sources: HIC that presents how publications build upon and relate to each other is result of identifying meaningful citations.

Apache research paper

Naiad is now available under the Apache 2. For more details about the software release, see the online Naiad documentation. The Naiad project is an investigation of data-parallel dataflow computation, like Dryad and DryadLINQ, but with a focus on low-latency streaming and cyclic computations.

Naiad introduces a new computational model, timely dataflow, which combines low-latency asynchronous message flow with lightweight coordination when required. These primitives allow the efficient implementation of many dataflow patterns, from bulk and streaming computation to iterative graph processing and machine learning.

Naiad is system for data-parallel dataflow computation which attempts to raise the levels of abstraction used by programmers from an imperative sequence of MapReduce-style statements, to involve higher level concepts of loops and streaming.

While Naiad is not the first system to support loops or streaming computation, it does provide support for the combination of the two, nesting loops inside streaming contexts and indeed other loops, while maintaining a clean separation between the many reasons new records may flow through the computation.

Naiad is based on a computational model called Timely Dataflow. Informally, Timely Dataflow supports directed dataflow graphs with structured cycles, analogous to structured loops in a standard imperative programming language.

By removing the overhead associated with moving between computational stages, Naiad supports efficient implementations of a variety of programming patterns not often found in dataflow systems, including prioritized iteration, nested iterative algorithms, and incremental updates to iterative computations.

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These lead to simple, performant, and composable libraries for event processing, graph computation, machine learning, and other real-time analytics.

Differential Dataflow Our initial work on Naiad was aimed at incremental re-evaluation of declarative data-parallel computations, including those with iterative fixed-point computations.

Our work here gave rise to a new computational model, differential dataflow, capable of efficiently processing substantially more complex computations than current systems support, namely incremental and arbitrarily nested iterative dataflow computation.

Differential dataflow is implemented as a library atop Naiad, and is available with the Naiad source. Frank McSherry introduces Naiad and Differential Dataflow Consider the problem of computing the connected component structure of a graph.

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One natural iterative data-parallel approach has each vertex assume a label initially their own name and repeatedly share their label with their neighbors, assuming the least label in their neighborhood. Eventually, all vertices in the same component will be labeled with the name of the least vertex in their component.

Several data-processing systems make this sort of iterative computation easy to write and efficient to execute.

However, what happens if the input changes?

Apache research paper

Perhaps a single edge is removed, which can result in the separation of two previously connected components. The labels above are invalidated, and it is not easy to determine how to unwind their propagation to return the computation to a state from which new correct labels can be determined.

The data-processing systems alluded to above are forced to discard the results of their previous computation and start over from scratch.

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Naiad, by comparison, represents a dataset in a compact form indicating where and when records have changed. The specific representation enables efficient combination of incremental and iteration computation, and allows us to update computations like the connected components example above in a fraction of a second.International Journal of Scientific & Engineering Research, Volume 7, Issue 12, December 65 This paper is to evaluate the performance of latest four hypervisors VMware , ESXIMicrosoft Hyper- A comparative study of Various Hypervisors Performance Author: Budhprakash, ashio-midori.com Bhatia, Dr.

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Top 20 Recent Research Papers on Machine Learning and Deep Learning

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Solr is the popular, blazing-fast, open source enterprise search platform built on Apache Lucene ™. Learn more about Solr.

Apache research paper

Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and .

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