This paper introduces bifocal sampling, a new technique for estimating the size of an equi-join of two relations. Bifocal sampling classi es tuples in each relation into two groups, sparse and dense, based on the number of tuples with the same join value. Distinct estimation procedures are employed

Abstract — In this work we tackle the open problem of self-join size (SJS) estimation in a large-scale Distributed Data System, where tuples of a relation are distributed over data nodes which comprise an overlay network. Our contributions include adaptations of five well-known SJS estimation

In many applications, data takes the form of a continuous stream rather than a persistent data set. Data stream processing is generally an on-line, one-pass process and is required to be time and space efficient too. In this paper, we develop a framework for estimating join size over the data

of this approach for matching twig patterns is that intermediate result sizes can get large, even when the input and output sizes are more manageable. In this paper, we propose a novel holistic twig join algorithm, TwigStack, for matching an XML query twig pattern. Our technique uses a chain of linked stacks

We propose a novel technique for estimating the size of set similarity join. The proposed technique relies on a succinct representation of sets using Min-Hash signatures. We exploit frequent patterns in the signatures for the Set Similarity Join (SSJoin) size estimation by counting their support

Many markets involve two groups of agents who interact via “platforms, ” where one group’s benefit from joining a platform depends on the size of the other group that joins the platform. I present three models of such markets: a monopoly platform; a model of competing platforms where agents join a

into a corresponding bandpass mosaic. In this step, component images are joined using a weighted average within a transition zone which is proportional in size to the wave lengths represented in the band. Finally, these band-pass mosaic images are summed to obtain the desired image mosaic. In this way

database is a core operation for XML query processing. In this paper, we develop two families of structural join algorithms for this task: tree-merge and stack-tree. The tree-merge algorithms are a natural extension of traditional merge joins and the recently proposed multi-predicate merge joins, while

yannisQcs.wise.edu Query optimizers of current relational database systems use several statistics maintained by the system on the contents of the database to decide on the most efficient access plan for a given query. These statistics contain errors that transitively affect many estimates derived by the optimizer. We present a formal framework based on which the principles of this error propagation can be studied. Within this framework, we obtain several ana-lytic results on how the error propagates in general, as well as in the extreme and average cases. We also pro-vide results on guarantees that the database system can make based on the statistics that it maintains. Finally, we discuss some promising approaches to controlling the error propagation and derive several interesting proper-ties of them. 1

. In addition to developing relatively straightforward solutions to finding such outliers based on the classical nestedloop join and index join algorithms, we develop a highly efficient partition-based algorithm for mining outliers. This algorithm first partitions the input data set into disjoint subsets