Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

The ShaPE of ShaDe: a Coordination System

Abstract: ShaDe is an object-based coordination language. It offers a basic abstraction called the Object Space, that is similar to a tuple space with the difference that it contains both objects and messages. ShaDe objects are active, i.e. they are units (places) of computation. Each object encapsulates a state in form of multiset of tuples and methods in form of rewriting rules. The object space is a coordination medium supporting a number of inter-object associative communication mechanisms, namely unicast, multicast, and broadcast. The most interesting feature of Shade is that coordination is expressed by rules. We exploit such a feature to build "coordination" services enacting declarative cooperation laws. We demonstrate the use of the language building two coordination applications, namely a distributed auction system and a stock exchange system.

A Survey of Join Processing in Data Streams

Abstract: Given the fundamental role played by joins in querying relational databases, it is not surprising that stream join has also been the focus of much research on streams. Recall that relational (theta) join between two non-streaming relations R1 and R2, denoted R 1⋈θ R 2, returns the set of all pairs (r 1, r 1), where r 1 ∈ R 1, r 2 ∈ R 2, and the join condition θ(r 1, r 2) evaluates to true. A straightforward extension of join to streams gives the following semantics (in rough terms): At any time t, the set of output tuples generated thus far by the join between two streams S 1 and S 2 should be the same as the result of the relational (non- streaming) join between the sets of input tuples that have arrived thus far in S 1 and S 2.

Data Cube Materialization and Mining over MapReduce

Abstract: Computing interesting measures for data cubes and subsequent mining of interesting cube groups over massive data sets are critical for many important analyses done in the real world. Previous studies have focused on algebraic measures such as SUM that are amenable to parallel computation and can easily benefit from the recent advancement of parallel computing infrastructure such as MapReduce. Dealing with holistic measures such as TOP-K, however, is nontrivial. In this paper, we detail real-world challenges in cube materialization and mining tasks on web-scale data sets. Specifically, we identify an important subset of holistic measures and introduce MR-Cube, a MapReduce-based framework for efficient cube computation and identification of interesting cube groups on holistic measures. We provide extensive experimental analyses over both real and synthetic data. We demonstrate that, unlike existing techniques which cannot scale to the 100 million tuple mark for our data sets, MR-Cube successfully and efficiently computes cubes with holistic measures over billion-tuple data sets.

Distribution Models Of Relations

Abstract: We show how relations can be modelled in fast memory by a distribution of tuples in a multidimensional space. Given distributions for operand relations we derive distributions for the relations that result from applying the relational algebra. We apply the result for the natural join to optimize the evaluation of an expression involving two joins. We suggest further applications. The analysis for division leads to a generalization of that operator.

Deep middleware for the divergent Grid

Abstract: Next-generation Grid applications will be highly heterogeneous in nature, will run on many types of computer and device, will operate within and across many heterogeneous network types, and must be explicitly configurable and runtime reconfigurable. We refer to this future Grid environment as the "divergent Grid". In this paper, we propose a "deep middleware" approach to meeting key requirements of the divergent Grid. Deep middleware reaches down into the network to provide highly flexible network support that underpins a rich, extensible and reconfigurable set of application-level "interaction paradigms" (such as publish-subscribe, multicast, tuple spaces etc.). In our Gridkit middleware platform, these facilities are encapsulated in two key component frameworks: the interaction framework and the overlay framework, which are the subject of this paper. The paper also evaluates the two frameworks in terms of their configurability (e.g. ability to be profiled for different device types) and reconfigurability (e.g. to self-optimise as the environment changes).