The most successful systems for "big data" processing have all adopted functional APIs. We present a new programming model we call function passing designed to provide a more principled substrate on which to build data-centric distributed systems. A key idea is to build up a persistent functional data structure representing transformations on distributed immutable data by passing well-typed serializable functions over the wire and applying them to this distributed data. Thus, the function passing model can be thought of as a persistent functional data structure that is distributed, where transformations to data are stored in its nodes rather than the distributed data itself. The model simplifies failure recovery by design-in the event of a failure, data is recovered by replaying function applications atop immutable data loaded from stable storage. Deferred evaluation is also central to our model; by incorporating deferred evaluation into our design only at the point of initiating network communication, the function passing model remains easy to reason about while remaining efficient in time and memory. We formalize our programming model using small-step operational semantics, and we provide an open-source implementation of our model in and for the Scala programming language, along with a case study of several example frameworks and end-user programs written atop of this model.
The most successful systems for “big data” processing have all adopted functional APIs. We present a new programming model, we call function passing, designed to provide a more principled substrate, or middleware, upon which to build data-centric distributed systems like Spark. A key idea is to build up a persistent functional data structure representing transformations on distributed immutable data by passing well-typed serializable functions over the wire and applying them to this distributed data. Thus, the function passing model can be thought of as a persistent functional data structure that is distributed, where transformations performed on distributed data are stored in its nodes rather than the distributed data itself. One advantage of this model is that failure recovery is simplified by design – data can be recovered by replaying function applications atop immutable data loaded from stable storage. Deferred evaluation is also central to our model; by incorporating deferred evaluation into our design only at the point of initiating network communication, the function passing model remains easy to reason about while remaining efficient in time and memory. Moreover, we provide a complete formalization of the programming model in order to study the foundations of lineage-based distributed computation. In particular, we develop a theory of safe, mobile lineages based on a subject reduction theorem for a typed core language. Furthermore, we formalize a progress theorem that guarantees the finite materialization of remote, lineage-based data. Thus, the formal model may serve as a basis for further developments of the theory of data-centric distributed programming, including aspects such as fault tolerance. We provide an open-source implementation of our model in and for the Scala programming language, along with a case study of several example frameworks and end-user programs written atop this model.
In this chapter we will look at users’ taking action processes in Semantic Work Environments. We argue that the underlying motivational problem between vast semantic potential and extra personal investment can be considered as a “Semantic Prisoner’s Dilemma” that builds on two competing value perspectives: The micro and macroperspective. The former informs a user’s decision for action, whereas the latter informs a designer’s decision for offering services. An in-depth analysis of the term “Added- Value” reveals its double relativity, which allows a sophisticated evaluation of such services from a microperspective. We use this property of double relativity for suggesting the “Added-Value Analysis” as a design method for getting people into Semantic Work Environments—showcasing its strength with a description of CPoint and ConneXions.
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