On the bottom-up evaluation of recursive queries

Chen, Yi Jen

doi:10.1002/(sici)1098-111x(199610)11:10<807::aid-int7>3.0.co;2-2

Cited by 7 publications

(6 citation statements)

References 16 publications

(30 reference statements)

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“…There are many well-known algorithms for calculating the transitive closure of a relation in traditional databases such as Naive [9], Semi-Naive [9], Magic-set [11], or Smart [16,18]. Afrati et al [3] outline how to compute a non-linear transitive closure on a computer cluster for a recursive query.…”

Section: Introductionmentioning

confidence: 99%

Recursive Join Processing in Big Data Environment

Phan¹,

Trieu²,

Phan³

2021

JCC

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In the era of information explosion, Big data is receiving increased attention as having important implications for growth, profitability, and survival of modern organizations. However, it also offers many challenges in the way data is processed and queried over time. A join operation is one of the most common operations appearing in many data queries. Specially, a recursive join is a join type used to query hierarchical data but it is more extremely complex and costly. The evaluation of the recursive join in MapReduce includes some iterations of two tasks of a join task and an incremental computation task. Those tasks are significantly expensive and reduce the performance of queries in large datasets because they generate plenty of intermediate data transmitting over the network. In this study, we thus propose a simple but efficient approach for Big recursive joins based on reducing by half the number of the required iterations in the Spark environment. This improvement leads to significantly reducing the number of the required tasks as well as the amount of the intermediate data generated and transferred over the network. Our experimental results show that an improved recursive join is more efficient and faster than a traditional one on large-scale datasets.

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Section: Introductionmentioning

confidence: 99%

Recursive Join Processing in Big Data Environment

Phan¹,

Trieu²,

Phan³

2021

JCC

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“…The query time is O(1). Finally, deductive databases can be considered as a quite different extension to handle this problem [12,14,15].…”

Section: Introductionmentioning

confidence: 99%

Decomposing DAGs into spanning trees: A new way to compress transitive closures

Chen

2011

2011 IEEE 27th International Conference on Data Engineering

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Let G(V, E) be a digraph (directed graph) with n nodes and e edges. Digraph G* = (V, E*) is the reflexive, transitive closure if (v, u) E* iff there is a path from v to u in G. Efficient storage of G* is important for supporting reachability queries which are not only common on graph databases, but also serve as fundamental operations used in many graph algorithms. A lot of strategies have been suggested based on the graph labeling, by which each node is assigned with certain labels such that the reachability of any two nodes through a path can be determined by their labels. Among them are interval labelling, chain decomposition, and 2-hop labeling. However, due to the very large size of many real world graphs, the computational cost and size of labels using existing methods would prove too expensive to be practical. In this paper, we propose a new approach to decompose a graph into a series of spanning trees which may share common edges, to transform a reachability query over a graph into a set of queries over trees. We demonstrate both analytically and empirically the efficiency and effectiveness of our method.

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“…An important matter of research in such systems is the efficient evaluation of recursive queries. Various strategies for processing recursive queries have been proposed (see [6], [7], [8], [9], [10], [11], [12], [19], [30]). These strategies include evaluation methods such as naive evaluation [6], [27], seminaive evaluation [2], query/subquery [31], RQA/ FQI [25], Henschen-Naqvi [20], and the methods used in compiling recursive queries [16], [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

“…Another class of strategies, called query optimization strategies, are used to transform queries into a form that is more amenable to the existing optimization techniques developed for relational databases. Several examples of this class of approaches are magic sets [3], counting [3], and their generalized versions [5], [11]. In this paper, we discuss a graph method which has been presented for handling a subset of recursive queries, the so-called binary-chain programs, by Grahne et al [14], [15].…”

Section: Introductionmentioning

confidence: 99%

On the graph traversal and linear binary-chain programs

Chen

2003

IEEE Trans. Knowl. Data Eng.

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Grahne et al. have presented a graph algorithm for evaluating a subset of recursive queries [14], [15]. This method consists of two phases. In the first phase, the method transforms a linear binary-chain program into a set of equations over expressions containing predicate symbols. In the second phase, a graph is constructed from the equations and the answers are produced by traversing the relevant paths. Here, we describe a new algorithm which requires less time than Grahne's. The key idea of the improvement is to reduce the search space that will be traversed when a query is invoked. Furthermore, we speed up the evaluation of cyclic data by generating most answers directly in terms of the answers already found and the associated "path information" instead of traversing the corresponding paths as usual. In this way, our algorithm achieves a linear time complexity for both acyclic and cyclic data.

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On the bottom-up evaluation of recursive queries

Cited by 7 publications

References 16 publications

Recursive Join Processing in Big Data Environment

Recursive Join Processing in Big Data Environment

Decomposing DAGs into spanning trees: A new way to compress transitive closures

On the graph traversal and linear binary-chain programs

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