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.

Computing cores for data exchange: new algorithms and practical solutions

Abstract: Data Exchange is the problem of inserting data structured under a source schema into a target schema of different structure (possibly with integrity constraints), while reflecting the source data as accurately as possible. We study computational issues related to data exchange in the setting of Fagin, Kolaitis, and Popa(PODS'03). We use the technique of hypertree decompositions to derive improved algorithms for computing the core of a relational instance with labeled nulls, a problem we show to be fixed-parameter intractable with respect to the block size of the input instances. We show that computing the core of a data exchange problem is tractable for two large and useful classes of target constraints. The first class includes functional dependencies and weakly acyclic inclusion dependencies. The second class consists of full tuple generating dependencies and arbitrary equation generating dependencies. Finally, we show that computing cores is NP-hard in presence of a system-predicate NULL(x), which is true iff x is a null value.

Predicate-based Filtering of XPath Expressions

Abstract: The XML/XPath filtering problem has found wide-spread interest. In this paper, we propose a novel algorithm for solving it. Our approach encodes XPath expressions (XPEs) as ordered sets of predicates and translates XML documents into sets of tuples, which are evaluated over these predicates. Predicates representing overlapping portions of XPEs are stored and processed once, thus fully exploiting potential overlap in XPEs. We experimentally evaluate the performance of our algorithm, demonstrating its scalability to millions of XPEs, with matching performance in the millisecond range. We show interesting trade-offs to alternative approaches.

Space decomposition in data mining - a clustering approach

Abstract: Decomposition may divide the database horizontally (subsets of rows or tuples) or vertically. It may be aimed at minimizing space and time needed for the classification of a dataset (e.g. sampling, windowing) or rather attempt to improve accuracy (e.g. bagging, boosting). This paper presents a horizontal space-decomposition algorithm, exploiting the K-means clustering algorithm. It is aimed at decreasing error rate compared to the simple classifier embedded in it while being rather understandable.

High-throughput extract-transform-load (etl) of program events for subsequent analysis

Abstract: An event tap associated with a server, such as a Web server, at a machine can transform a server event into a tuple, select a database node for the tuple, and place the tuple in a queue for that database node, and then flush the queue periodically directly into database notes. The use of an event tap can thus reduce the computational burden on the database while keeping the server event data in the database relatively fresh.