Browsing by Subject "Data models"

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    An extended relational algebra for nested relations
    (1993) Sükan, Eser
    In this study the database models of Roth-Korth-Silberschatz (RKS) [cf. ACM TODS 13(4): 389—417, 1988] and Abiteboul-Bidoit (AB) [cf. Journal of Computer and System Sciences 33(4): 361—393, 1986] to formalize non-firstnormal-form relations are presented along with their extended relational algebra. We show that the extended set operators union and difference of RKS and AB are not information equivalent Using the model of RKS and restricting ourselves to union and difference, we define our extended set operators and show that these two operators and the extended intersection of RKS are information equivalent.
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    FAME: Face association through model evolution
    (IEEE, 2015-06) Gölge, Eren; Duygulu, Pınar
    We attack the problem of building classifiers for public faces from web images collected through querying a name. The search results are very noisy even after face detection, with several irrelevant faces corresponding to other people. Moreover, the photographs are taken in the wild with large variety in poses and expressions. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the models associated to a name to evolve. The idea is based on capturing discriminative and representative properties of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with different characteristics. On benchmark datasets, our results are comparable to or better than the state-of-the-art studies for the task of face identification. © 2015 IEEE.
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    Multivariate time series imputation with transformers
    (IEEE, 2022-11-25) Yıldız, A. Yarkın; Koç, Emirhan; Koç, Aykut
    Processing time series with missing segments is a fundamental challenge that puts obstacles to advanced analysis in various disciplines such as engineering, medicine, and economics. One of the remedies is imputation to fill the missing values based on observed values properly without undermining performance. We propose the Multivariate Time-Series Imputation with Transformers (MTSIT), a novel method that uses transformer architecture in an unsupervised manner for missing value imputation. Unlike the existing transformer architectures, this model only uses the encoder part of the transformer due to computational benefits. Crucially, MTSIT trains the autoencoder by jointly reconstructing and imputing stochastically-masked inputs via an objective designed for multivariate time-series data. The trained autoencoder is then evaluated for imputing both simulated and real missing values. Experiments show that MTSIT outperforms state-of-the-art imputation methods over benchmark datasets.
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    A natural language-based interface for querying a video database
    (Institute of Electrical and Electronics Engineers, 2007-01) Küçüktunç, O.; Güdükbay, U.; Ulusoy, Özgür
    The authors developed a video database system called BilVideo that provides integrated support for spatiotemporal, semantic, and low-level feature queries. As a further development for this system, the authors present a natural language processing-based interface that lets users formulate queries in English and discuss the advantage of using such an interface. © 2007 IEEE.