| 1. |
- Carbone, Paris, et al.
(författare)
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Large-scale data stream processing systems
- 2017
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Ingår i: Handbook of Big Data Technologies. - Cham : Springer International Publishing. - 9783319493404 - 9783319493398 ; , s. 219-260
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In our data-centric society, online services, decision making, and other aspects are increasingly becoming heavily dependent on trends and patterns extracted from data. A broad class of societal-scale data management problems requires system support for processing unbounded data with low latency and high throughput. Large-scale data stream processing systems perceive data as infinite streams and are designed to satisfy such requirements. They have further evolved substantially both in terms of expressive programming model support and also efficient and durable runtime execution on commodity clusters. Expressive programming models offer convenient ways to declare continuous data properties and applied computations, while hiding details on how these data streams are physically processed and orchestrated in a distributed environment. Execution engines provide a runtime for such models further allowing for scalable yet durable execution of any declared computation. In this chapter we introduce the major design aspects of large scale data stream processing systems, covering programming model abstraction levels and runtime concerns. We then present a detailed case study on stateful stream processing with Apache Flink, an open-source stream processor that is used for a wide variety of processing tasks. Finally, we address the main challenges of disruptive applications that large-scale data streaming enables from a systemic point of view.
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| 2. |
- Junghanns, Martin, et al.
(författare)
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Management and analysis of big graph data : Current systems and open challenges
- 2017
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Ingår i: Handbook of Big Data Technologies. - Cham : Springer International Publishing. - 9783319493404 - 9783319493398 ; , s. 457-505
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Many big data applications in business and science require the management and analysis of huge amounts of graph data. Suitable systems to manage and to analyze such graph data should meet a number of challenging requirements including support for an expressive graph data model with heterogeneous vertices and edges, powerful query and graph mining capabilities, ease of use as well as high performance and scalability. In this chapter, we survey current system approaches for management and analysis of "big graph data". We discuss graph database systems, distributed graph processing systems such as Google Pregel and its variations, and graph dataflow approaches based on Apache Spark and Flink. We further outline a recent research framework called Gradoop that is build on the so-called Extended Property Graph Data Model with dedicated support for analyzing not only single graphs but also collections of graphs. Finally, we discuss current and future research challenges.
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