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About 10,000 papers for “Stream processing”

Stream Processing

Michael Stonebraker · Encyclopedia of Database Systems · 2009

Cited by 1

Stream Processing

Michael Stonebraker, Uğur Çetintemel · Encyclopedia of Database Systems · 2018

Cited by 1

Stream Processing

Gibbins, Nicholas · EdShare Southampton (University of Southampton)

Cited by 0

Bit-stream processing

Alois M. Bock · Institution of Engineering and Technology eBooks · 2009

This chapter presents the small bit-rate changes can be achieved with bit-rate changers, thus avoiding the need for decoders and encoders. Transcoders consist of closely coupled decoders and re-encoders. Transcoders are used for large bit-rate or profile changes or conversion from one compression standard to another. Splicing between statistically multiplexed video signals requires bit-rate changers.

Cited by 0

Stream-Processing Points

Renato Pajarola · Zurich Open Repository and Archive (University of Zurich) · 2006

Figure 1.Simulated stream-processing stages with (r.t.l.): points to be read from input stream (black points), in nearest neighborhood evaluation (red points), during normal computation (yellow points), amid curvature estimation (shaded grey points) and fully processed and written to output stream (shaded color-coded splats).Note that the extent of the active point set is greatly exagerated in this illustration compared to the real data (see Figure 4).

Cited by 41Open access

XML Stream Processing

Junichi Tatemura · IGI Global eBooks · 2010

This chapter reviews recent advances on stream XML query evaluation algorithms with stack-based encoding of intermediary data. Originally proposed for disk-resident XML, the stack-based architecture has been extended for streaming algorithms for both single and multiple query processing, ranging from XPath filtering to more complex XQuery. The key benefit of the stack-based architecture is its succinct encoding of partial query results, which can cause exponential enumeration if encoded naively. In addition, the chapter discusses opportunities to integrate benefits demonstrated in the reviewed work. For single-query processing, a sketch is given for an integrated algorithm, StreamTwig2Stack, that achieves all the benefits of existing algorithms in terms of functionality, time complexity, and buffer memory optimality.

Cited by 2

Algebraic Stream Processing

, · Cronfa (Swansea University) · 1995

This thesis is concerned with the development of algebraic techniques for the study of systems that com pute over infinite sequences of data called streams.We call the mathematical representation of systems that take streams as input and produce streams as output stream transformers (STs) and we call the implementations of STs stream processing systems (SPSs).Collectively, we call the study of STs and SPSs stream processing.Stream processing encompasses many classes of systems that are studied in Computer Science.For example, dataflow systems, signal processing systems, reactive systems and synchronous concurrent al gorithms can all be formalized mathematically as STs.These classes of systems are broad and include all traditional forms of sequential and parallel hardware and many specialized models of computation including artificial neural networks and systolic architectures.We identify and analyse the typically higher-order approaches to stream processing in the literature.From this analysis we motivate an alternative approach to the specification of SPSs as STs based on an essentially first-order equational representation.This technique is called Cartesian form specification.More specifically, while STs are properly second-order objects we show that using Cartesian forms, the second-order models needed to formalize STs are so weak that we may use and develop well-understood first-order methods from computability theory and mathematical logic to reason about their properties.Indeed, we show that by specifying STs equationally in Cartesian form as primitive recursive functions we have the basis of a new, general purpose and mathematically sound theory of stream processing that emphasizes the formal specification and formal verification of STs.The main topics that we address in the development of this theory are as follows.We present a theoretically well-founded general purpose stream processing language ASTRAL (Algebr aic Stream TTMnsformer Language) that supports the use of modular specification techniques for full second-order STs.We show how ASTRAL specifications can be given a Cartesian form semantics using the language PREQ that is an equational characterization of the primitive recursive functions.In more detail, we show that by compiling ASTRAL specifications into an equivalent Cartesian form in PREQ we can use first-order equational logic with induction as a logical calculus to reason about STs.In particular, using this calculus we identify a syntactic class of correctness statements for which the verification of ASTRAL programmes is decidable relative to this calculus.We define an effective algorithm based on term re-writing techniques to implement this calculus and hence to automatically verify a very broad class of STs including conventional hardware devices.Finally, we analyse the properties of this abstract algorithm as a proof assistant and discuss various techniques that have been adopted to develop software tools based on this algorithm.

Cited by 0Open access

Stream Processing Planning

G. Ya. Grabarnik, Z. Liu, A. Riabov et al. · 2004

LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research

Cited by 0

GPU STREAM PROCESSING

Сергій Романюк, Олександр Романюк, Oleksandr Shenshin et al. · European Science · 2023

The organization of threads in the GPU, features of working with memory, synchronization technologies, implementation of conditional constructs, development of streaming information processing in rendering are considered.

Cited by 0Open access