Principles of programming with complex objects and collection types

Buneman, Peter; Naqvi, Shamim A.; Tannen, Val; Wong, Limsoon

doi:10.1016/0304-3975(95)00024-q

Cited by 211 publications

(197 citation statements)

References 14 publications

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“…For example, the way in which collection-aware actors operate on pipelined nested collections has similarities with some XML stream processing techniques [6]. The approach is also similar in spirit to list processing constructs in functional programming [2] as well as dataflow programming [18]. Because the abstract model of nested data collections is (essentially) a subset of XML, we can leverage and adapt existing XML-based query processing [7] and optimization techniques [8] for managing nested collections.…”

Section: Resultsmentioning

confidence: 99%

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Collection-Oriented Scientific Workflows for Integrating and Analyzing Biological Data

McPhillips

Bowers

Ludäscher

2006

Lecture Notes in Computer Science

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Abstract. Steps in scientific workflows often generate collections of results, causing the data flowing through workflows to become increasingly nested. Because conventional workflow components (or actors) typically operate on simple or application-specific data types, additional actors often are required to manage these nested data collections. As a result, conventional workflows become increasingly complex as data becomes more nested. This paper describes a new paradigm for developing scientific workflows that transparently manages nested data collections. Collection-oriented workflows have a number of advantages over conventional approaches including simpler workflow designs (e.g., requiring fewer actors and control-flow constructs) that are invariant under changes in data nesting. Our implementation within the Kepler scientific workflow system enables the explicit representation of collections and collection schemas, concurrent operation over collection contents via multi-level pipeline parallelism, and allows collection-aware actors to be composed readily from conventional actors.

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Section: Resultsmentioning

confidence: 99%

“…2 Kepler extends the Ptolemy II 3 system (hereafter, Ptolemy) with new features and components for scientific workflow design and for efficient workflow execution using distributed computational and experimental resources. Ptolemy was originally developed as a modeling and simulation environment, e.g.…”

Section: The Kepler Scientific Workflow Systemmentioning

confidence: 99%

Collection-Oriented Scientific Workflows for Integrating and Analyzing Biological Data

McPhillips

Bowers

Ludäscher

2006

Lecture Notes in Computer Science

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“…NRC is a simple functional programming language [3], built around the basic operations on records and sets, with for-loops and if-then-else (and let-expressions) as the only programming constructs. We naturally augment NRC with service calls.…”

Section: Dataflows Runs and Provenancementioning

confidence: 99%

“…Since these inference rules are known from the literature [3], we just present a sample of them, using big union, if-then-else, and for-loops as examples. We also show the rule for service calls:…”

Section: Dataflows Runs and Provenancementioning

confidence: 99%

“…NRC [3] is a well-studied language with exactly the right set of operations that are needed for the manipulation of the types of complex data that occur in a dataflow [4]. The suitability of NRC (in the form of a variant language called CPL) for scientific data manipulation and integration purposes has already been amply demonstrated by the Kleisli system [5,6].…”

Section: Introductionmentioning

confidence: 99%

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