Browsing by Subject "Relational database systems"

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    A comparison of historical relational query languages
    (ASME, 1994-07) Tansel, Abdullah Uz; Tin, E.
    We introduce a historical relational data model in which N1NF relations are used and 1-level of nesting is allowed. Attributes can either be atomic or temporal atom. An atomic attribute represents a time invariant attribute. A temporal atom consists of two components, a value and a temporal set, which is a set of times denoting the validity period of the value. We define a relational tuple calculus for this model. We follow a comparative approach towards completeness of historical query languages.
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    Database research at Bilkent University
    (ACM, 2005) Ulusoy, Özgür
    The research activities of the Database Research Group of Bilkent University are discussed. The research is mainly focused on the topics of multimedia databases, Web databases, and mobile computing. The Ottoman Archive Content-Based Retrieval system is a Web-based program that provides electronic access to digitally stored Ottoman document images. The issues involved in adding a native score management system to object-relational databases, to be used in querying web metadata are also discussed.
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    Implications of non-volatile memory as primary storage for database management systems
    (IEEE, 2017) Mustafa, Naveed Ul; Armejach, A.; Öztürk, Özcan; Cristal, A.; Unsal, O. S.
    Traditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively. © 2016 IEEE.
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    On Roth, Korth, and Silberschatz's extended algebra and calculus for nested relational databases
    (1992) Tansel, A. U.; Garnett, Lucy
    We discuss the issues encountered in the extended algebra and calculus languages for nested relations defined by Roth, Korth, and Silberschatz. Their equivalence proof between algebra and calculus fails because of the keying problems and the use of extended set operations. Extended set operations also have unintended side effects. Furthermore, their calculus seems to allow the generation of power sets, thus making it more powerful than their algebra.
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    Transqlate: translating enriched natural language sentences to SQL queries using transformers
    (2022-09) Farshkar Azari, Mousa
    A large amount of the structured data owned by different enterprises is typically stored in Relational Database Management Systems, and a decent knowledge of Structured Language Query (SQL) is required to extract desired information from the relational databases. Many naive users need to access the information from databases, and they do not have the necessary skills or knowledge. Additionally, even some expert users might find it challenging to provide complex SQL queries when they do not know the schema underlying the database. To this end, a considerable amount of research has been conducted recently for the translation of queries formulated by users in a natural language to SQL queries to be processed by database systems. In this thesis, we provide some deep intelligent strategies to be used in natural language to SQL translation. We propose TranSQLate, a novel method to enrich the input sequences and provide more effective Natural Language Interface to Database (NLIDB) systems. We apply our strategies to the Vanilla transformer and T5 transformer models in three different ways. With enriched inputs, we achieve up to 16.7% improvement in translation accuracy, 6.5 points in SacreBLEU score, and 18 points in the n-gram precision, compared to not enriched versions. Our method surpasses the strategies used in the state-of-the-art systems NALIR, TEMPLAR, and DBTagger, in terms of translation accuracy over IMDB, scholar, and Yelp datasets.