33,304 research outputs found

    Constructing Optimal Bushy Processing Trees for Join Queries is NP-hard

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    We show that constructing optimal bushy processing trees for join queriesis NP-hard. More specifically, we show that even the construction of optimal bushy trees for computing the cross product for a set of relations is NP-hard

    Data-Oriented Language Processing. An Overview

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    During the last few years, a new approach to language processing has started to emerge, which has become known under various labels such as "data-oriented parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak 1996a/b; Goodman 1996; Kaplan 1996; Rajman 1995a/b; Scha 1990-92; Sekine & Grishman 1995; Sima'an et al. 1994; Sima'an 1995-96; Tugwell 1995). This approach, which we will call "data-oriented processing" or "DOP", embodies the assumption that human language perception and production works with representations of concrete past language experiences, rather than with abstract linguistic rules. The models that instantiate this approach therefore maintain large corpora of linguistic representations of previously occurring utterances. When processing a new input utterance, analyses of this utterance are constructed by combining fragments from the corpus; the occurrence-frequencies of the fragments are used to estimate which analysis is the most probable one. In this paper we give an in-depth discussion of a data-oriented processing model which employs a corpus of labelled phrase-structure trees. Then we review some other models that instantiate the DOP approach. Many of these models also employ labelled phrase-structure trees, but use different criteria for extracting fragments from the corpus or employ different disambiguation strategies (Bod 1996b; Charniak 1996a/b; Goodman 1996; Rajman 1995a/b; Sekine & Grishman 1995; Sima'an 1995-96); other models use richer formalisms for their corpus annotations (van den Berg et al. 1994; Bod et al., 1996a/b; Bonnema 1996; Kaplan 1996; Tugwell 1995).Comment: 34 pages, Postscrip

    Report from the Tri-Agency Cosmological Simulation Task Force

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    The Tri-Agency Cosmological Simulations (TACS) Task Force was formed when Program Managers from the Department of Energy (DOE), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF) expressed an interest in receiving input into the cosmological simulations landscape related to the upcoming DOE/NSF Vera Rubin Observatory (Rubin), NASA/ESA's Euclid, and NASA's Wide Field Infrared Survey Telescope (WFIRST). The Co-Chairs of TACS, Katrin Heitmann and Alina Kiessling, invited community scientists from the USA and Europe who are each subject matter experts and are also members of one or more of the surveys to contribute. The following report represents the input from TACS that was delivered to the Agencies in December 2018.Comment: 36 pages, 3 figures. Delivered to NASA, NSF, and DOE in Dec 201

    Constructing Optimal Bushy Trees Possibly Containing Cross Products for Order Preserving Joins is in P

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    One of the main features of XQuery compared to traditional query languages like SQL, is that it preserves the input order - unless specified otherwise. As a consequence, order-preserving algebraic operators are needed to capture the semantics of XQuery correctly. One important algebraic operator is the order-preserving join. The order-preserving join is associative but, in contrast to the traditional join operator, not commutative. Since join ordering (i.e. finding the optimal execution plan for a given set of join operators) has been an important topic of query optimization for SQL, it is expected that it will also play a major role in optimizing XQuery. The search space for ordering traditional joins is exponential in size. Although the lack of commutativity reduces the search space for ordering order-preserving joins, we show that it is still exponential. This raises the question whether the join ordering problem is also NP-hard, as in the traditional setting. We answer this question by introducing the first polynomial algorithm that produces optimal bushy trees possibly containing cross products

    Fourteenth Biennial Status Report: März 2017 - February 2019

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    Denoising Autoencoders for fast Combinatorial Black Box Optimization

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    Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.Comment: corrected typos and small inconsistencie
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