Design of the S system for data analysis

Becker, Richard A.; Chambers, John M.

doi:10.1145/358189.358078

Cited by 20 publications

(4 citation statements)

References 13 publications

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“…Another method for adding a third and even fourth dimension on a scatterplot is to mark the scatters with the different colors and sizes; among them, a diagram with size-diverse scatters is usually called the "bubble chart". The ultimate method to display high-dimensional data is the scatterplot matrix, which proposed in 1980s [6][7][8][9][10]. For a set of data variables x 1 , x 2 , .…”

Section: Historymentioning

confidence: 99%

Visualizing Space–Time Multivariate Data Consisting of Discrete and Continuous Variables: A Method for the General Public

Li,

Wu,

Kuo

et al. 2024

Foundations

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Visualizing multivariate data can be challenging, especially for the general public. The difficulties extend beyond determining how to present the data; they also involve comprehension. Early literature has identified various methods, including Chernoff’s face, but these methods often have significant drawbacks, making them challenging to interpret. Subsequently, other techniques, such as scatterplots, parallel coordinate plots, and dynamic graphics, have been introduced. However, many of these methods can be intricate to create and interpret, particularly when visualizing high-dimensional data. Additionally, simultaneously representing discrete aspects (including “space”) and continuous aspects (including “time”) presents another challenge. This study proposes a novel approach named the “Δ table” (delta table), which transforms space–time multivariate data consisting of discrete and continuous variables into a tabular format. The Δ table is believed to be more user-friendly for the general public, which is its most significant advantage compared to previous methods. Finally, we used a case study of the decoupling of the world’s developed, newly industrialized, and developing economies in recent decades as an example of an attempt to apply the Δ table.

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Section: Historymentioning

confidence: 99%

Visualizing Space–Time Multivariate Data Consisting of Discrete and Continuous Variables: A Method for the General Public

Li,

Wu,

Kuo

et al. 2024

Foundations

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“…Those adjustments are summarized here. The general goal was to make Datastream a tool that would extract interesting subsets of databases for further analysis by a system such as S. 3,4 The original prototype of Datastream performed rudimentary computations such as conversion between English and metric units, but it could not do aggregate computations such as sums and averages. Datastream's computational facilities needed to be much stronger to make it useful as an analytical tool.…”

Section: Statistical Database Systemmentioning

confidence: 99%

Datastream-A Language for Large Files

Swartwout

1985

At&T Technical Journal

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Datastream is a simple language designed for writing programs that perform computations on large data files in the UNIX™ operating system. Datastream handles larger files and a wider variety of computations than most UNIX database management systems, but it provides no explicit support for updates. Its features and performance characteristics are particularly good for working with infrequently updated, mostly statistical data. This paper describes the Datastream language, outlines its evolution, and summarizes users' experience with a prototype implementation.

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“…In this section we identify four generic stream processing primitives that can be found in the logic programming literature. We conclude the section with some concrete examples of these types of stream processing primitives based on the list given in Becker and Chambers [1984],…”

Section: 7 S Tr E a M P R O C E S S In G P R Im Itiv E S In L Ogic P R O G R A M M In Gmentioning

confidence: 99%

“…We now list the examples of second-order stream processing primitives (in functional form) presented informally in Parker [1990] based on the list given in Becker and Chambers [1984], Let A be any standard 5-sorted E-algebra.…”

Section: E Xam Ples O F R Elational Stream Processing Prim Itivesmentioning

confidence: 99%

Algebraic Stream Processing

Stephens¹

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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 formalise 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 emphasises 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 (Algebraic Stream TRAnsformer 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 characterisation 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.

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Design of the S system for data analysis

Cited by 20 publications

References 13 publications

Visualizing Space–Time Multivariate Data Consisting of Discrete and Continuous Variables: A Method for the General Public

Visualizing Space–Time Multivariate Data Consisting of Discrete and Continuous Variables: A Method for the General Public

Datastream-A Language for Large Files

Algebraic Stream Processing

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