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We propose the algorithms for performing multiway joins using a new type of coarse grain reconfigurable hardware accelerator~-- ``Plasticine~-- that, compared with other accelerators, emphasizes high compute capability and high on-chip communication bandwidth. Joining three or more relations in a single step, i.e. multiway join, is efficient when the join of any two relations yields too large an intermediate relation. We show at least 200X speedup for a sequence of binary hash joins execution on Plasticine over CPU. We further show that in some realistic cases, a Plasticine-like accelerator can make 3-way joins more efficient than a cascade of binary hash joins on the same hardware, by a factor of up to 45X.
We study the problem of computing similarity joins under edit distance on a set of strings. Edit similarity joins is a fundamental problem in databases, data mining and bioinformatics. It finds important applications in data cleaning and integration,
As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph.
We introduce and study the problem of computing the similarity self-join in a streaming context (SSSJ), where the input is an unbounded stream of items arriving continuously. The goal is to find all pairs of items in the stream whose similarity is gr
Given two collections of set objects $R$ and $S$, the $R bowtie_{subseteq} S$ set containment join returns all object pairs $(r, s) in R times S$ such that $r subseteq s$. Besides being a basic operator in all modern data management systems with a wi
Similarity join, which can find similar objects (e.g., products, names, addresses) across different sources, is powerful in dealing with variety in big data, especially web data. Threshold-driven similarity join, which has been extensively studied in