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- W3210466936 abstract "We present a type-theoretic framework for data stream processing for real-time decision making, where the desired computation involves a mix of sequential computation, such as smoothing and detection of peaks and surges, and naturally parallel computation, such as relational operations, key-based partitioning, and map-reduce. Our framework unifies sequential (ordered) and relational (unordered) data models. In particular, we define synchronization schemas as types, and series-parallel streams (SPS) as objects of these types. A synchronization schema imposes a hierarchical structure over relational types that succinctly captures ordering and synchronization requirements among different kinds of data items. Series-parallel streams naturally model objects such as relations, sequences, sequences of relations, sets of streams indexed by key values, time-based and event-based windows, and more complex structures obtained by nesting of these. We introduce series-parallel stream transformers (SPST) as a domain-specific language for modular specification of deterministic transformations over such streams. SPSTs provably specify only monotonic transformations allowing streamability, have a modular structure that can be exploited for correct parallel implementation, and are composable allowing specification of complex queries as a pipeline of transformations." @default.
- W3210466936 created "2021-11-08" @default.
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- W3210466936 date "2021-06-20" @default.
- W3210466936 modified "2023-09-26" @default.
- W3210466936 title "Synchronization Schemas" @default.
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- W3210466936 doi "https://doi.org/10.1145/3452021.3458317" @default.
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