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Journal articles on the topic 'Query languages (Computer science)'

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Published: 4 June 2021
Last updated: 28 July 2024

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1

Kanellakis, P. C., G. M. Kuper, and P. Z. Revesz. "Constraint Query Languages." Journal of Computer and System Sciences 51, no. 1 (August 1995): 26–52. http://dx.doi.org/10.1006/jcss.1995.1051.

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2

Dong, Guozhu, Leonid Libkin, and Limsoon Wong. "Local properties of query languages." Theoretical Computer Science 239, no. 2 (May 2000): 277–308. http://dx.doi.org/10.1016/s0304-3975(99)00223-6.

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3

Chan, D. K. C. "Evaluating object-oriented query languages." Computer Journal 37, no. 10 (October 1, 1994): 858–72. http://dx.doi.org/10.1093/comjnl/37.10.858.

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4

Gyssens, Marc, Jan Van den Bussche, and Dirk Van Gucht. "Complete Geometric Query Languages." Journal of Computer and System Sciences 58, no. 3 (June 1999): 483–511. http://dx.doi.org/10.1006/jcss.1999.1630.

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5

Dahlhaus, E., and J. A. Makowsky. "Query languages for hierarchic databases." Information and Computation 101, no. 1 (November 1992): 1–32. http://dx.doi.org/10.1016/0890-5401(92)90074-p.

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6

V, Arjun Atreya, Ashish Kankaria, Pushpak Bhattacharyya, and Ganesh Ramakrishnan. "Query Expansion in Resource-Scarce Languages." ACM Transactions on Asian and Low-Resource Language Information Processing 16, no. 2 (December 14, 2016): 1–17. http://dx.doi.org/10.1145/2997643.

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7

Ferri, Fernando, and Maurizio Rafanelli. "Resolution of Ambiguities in Query Interpretation for Geographical Pictorial Query Languages." Journal of Computing and Information Technology 12, no. 2 (2004): 119. http://dx.doi.org/10.2498/cit.2004.02.07.

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8

Telemala, Joseph P., and Hussein Suleman. "Exploring Topic-language Preferences in Multilingual Swahili Information Retrieval in Tanzania." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (November 30, 2021): 1–30. http://dx.doi.org/10.1145/3458671.

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Habitual switching of languages is a common behaviour among polyglots when searching for information on the Web. Studies in information retrieval (IR) and multilingual information retrieval (MLIR) suggest that part of the reason for such regular switching of languages is the topic of search. Unlike survey-based studies, this study uses query and click-through logs. It exploits the querying and results selection behaviour of Swahili MLIR system users to explore how topic of search (query) is associated with language preferences—topic-language preferences. This article is based on a carefully controlled study using Swahili-speaking Web users in Tanzania who interacted with a guided multilingual search engine. From the statistical analysis of queries and click-through logs, it was revealed that language preferences may be associated with the topics of search. The results also suggest that language preferences are not static; they vary along the course of Web search from query to results selection. In most of the topics, users either had significantly no language preference or preferred to query in Kiswahili and changed their preference to either English or no preference for language when selecting/clicking on the results. The findings of this study might provide researchers with more insights in developing better MLIR systems that support certain types of users and in certain scenarios.
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9

Zadorozhnyi, V. I., L. A. Kalinichenko, and N. S. Nikitchenko. "Mutually complementary formalizations of deductive query languages." Cybernetics and Systems Analysis 29, no. 2 (1993): 229–41. http://dx.doi.org/10.1007/bf01132784.

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10

Benedikt, Michael, and Leonid Libkin. "Aggregate Operators in Constraint Query Languages." Journal of Computer and System Sciences 64, no. 3 (May 2002): 628–54. http://dx.doi.org/10.1006/jcss.2001.1810.

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11

Bassiouni, M. A., M. J. Llewellyn, and A. Mukherjee. "Time-based operators for relational algebra query languages." Computer Languages 19, no. 4 (October 1993): 261–76. http://dx.doi.org/10.1016/0096-0551(93)90011-o.

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12

Tansel, Abdullah Uz. "Query languages for statistical databases." Statistics and Computing 5, no. 1 (March 1995): 59–72. http://dx.doi.org/10.1007/bf00140666.

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13

Ling, Xiang, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, and Shouling Ji. "Deep Graph Matching and Searching for Semantic Code Retrieval." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (June 26, 2021): 1–21. http://dx.doi.org/10.1145/3447571.

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Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however, neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this article, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets, but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.
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14

Almendros-Jiménez, Jesús M., and Antonio Becerra-Terón. "Database query languages and functional logic programming." New Generation Computing 24, no. 2 (June 2006): 129–84. http://dx.doi.org/10.1007/bf03037296.

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15

Grumbach, S., and V. Vianu. "Tractable Query Languages for Complex Object Databases." Journal of Computer and System Sciences 51, no. 2 (October 1995): 149–67. http://dx.doi.org/10.1006/jcss.1995.1058.

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16

Libkin, Leonid, and Limsoon Wong. "Query Languages for Bags and Aggregate Functions." Journal of Computer and System Sciences 55, no. 2 (October 1997): 241–72. http://dx.doi.org/10.1006/jcss.1997.1523.

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17

Bidoit, N., and M. Objois. "Fixpoint and While Temporal Query Languages." Journal of Logic and Computation 19, no. 2 (August 14, 2008): 369–404. http://dx.doi.org/10.1093/logcom/exn056.

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18

Ykhlef, Mourad, and Sarra Alqahtani. "A survey of graphical query languages for XML data." Journal of King Saud University - Computer and Information Sciences 23, no. 2 (July 2011): 59–70. http://dx.doi.org/10.1016/j.jksuci.2011.05.002.

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19

Chan, Hock C. "Visual query languages for entity relationship model databases." Journal of Network and Computer Applications 20, no. 2 (April 1997): 203–21. http://dx.doi.org/10.1006/jnca.1997.0044.

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20

Dietrich, Suzanne Wagner. "An Educational Tool for Formal Relational Database Query Languages." Computer Science Education 4, no. 2 (January 1993): 157–84. http://dx.doi.org/10.1080/0899340930040201.

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21

AFONIN, SERGEY, and ELENA KHAZOVA. "MEMBERSHIP AND FINITENESS PROBLEMS FOR RATIONAL SETS OF REGULAR LANGUAGES." International Journal of Foundations of Computer Science 17, no. 03 (June 2006): 493–506. http://dx.doi.org/10.1142/s0129054106003954.

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Let Σ be a finite alphabet. A set [Formula: see text] of regular languages over Σ is called rational if there exists a finite set [Formula: see text] of regular languages over Σ such that [Formula: see text] is a rational subset of the finitely generated semigroup [Formula: see text] with [Formula: see text] as the set of generators and language concatenation as a product. We prove that for any rational set [Formula: see text] and any regular language R ⊆ Σ* it is decidable (1) whether [Formula: see text] or not, and (2) whether [Formula: see text] is finite or not. Possible applications to semistructured databases query processing are discussed.
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22

Lassila, Ora, Michael Schmidt, Olaf Hartig, Brad Bebee, Dave Bechberger, Willem Broekema, Ankesh Khandelwal, et al. "The OneGraph vision: Challenges of breaking the graph model lock-in1." Semantic Web 14, no. 1 (November 30, 2022): 125–34. http://dx.doi.org/10.3233/sw-223273.

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Amazon Neptune is a graph database service that supports two graph models: W3C’s Resource Description Framework (RDF) and Labeled Property Graphs (LPG). Customers choose one or the other model. This choice determines which data modeling features can be used and – perhaps more importantly – which query languages are available. The choice between the two technology stacks is difficult and time consuming. It requires consideration of data modeling aspects, query language features, their adequacy for current and future use cases, as well as developer knowledge. Even in cases where customers evaluate the pros and cons and make a conscious choice that fits their use case, over time we often see requirements from new use cases emerge that could be addressed more easily with a different data model or query language. It is therefore highly desirable that the choice of the query language can be made without consideration of what graph model is chosen and can be easily revised or complemented at a later point. To this end, we advocate and explore the idea of OneGraph (“1G” for short), a single, unified graph data model that embraces both RDF and LPGs. The goal of 1G is to achieve interoperability at both data level, by supporting the co-existence of RDF and LPG in the same database, as well as query level, by enabling queries and updates over the unified data model with a query language of choice. In this paper, we sketch our vision and investigate technical challenges towards a unification of the two graph data models.
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23

Turner, R. "An Introduction to the Formal Specification of Relational Query Languages." Computer Journal 28, no. 2 (February 1, 1985): 162–69. http://dx.doi.org/10.1093/comjnl/28.2.162.

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24

Libkin, Leonid, and Limsoon Wong. "Semantic Representations and Query Languages for Or-Sets." Journal of Computer and System Sciences 52, no. 1 (February 1996): 125–42. http://dx.doi.org/10.1006/jcss.1996.0010.

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25

Vandeurzen, Luc, Marc Gyssens, and Dirk Van Gucht. "On the expressiveness of linear-constraint query languages for spatial databases." Theoretical Computer Science 254, no. 1-2 (March 2001): 423–63. http://dx.doi.org/10.1016/s0304-3975(99)00316-3.

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26

Hillebrand, Gerd G., Paris C. Kanellakis, and Harry G. Mairson. "Database Query Languages Embedded in the Typed Lambda Calculus." Information and Computation 127, no. 2 (June 1996): 117–44. http://dx.doi.org/10.1006/inco.1996.0055.

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27

Abdelkrim, Latreche, Lehireche Ahmed, and Kadda Benyahia. "Interrogation Based on Semantic Annotations." International Journal of Web Portals 9, no. 2 (July 2017): 47–67. http://dx.doi.org/10.4018/ijwp.2017070103.

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Traditional information search approaches do not explicitly capture the meaning of a keyword query, but provide a good way for the user to express his or her information needs based on the keywords. In principle, semantic search aims to produce better results than traditional keyword search, but its progression has retarded because of to the complexity of the query languages. In this article, the authors present an approach to adapt keyword queries to querying the semantic web based on semantic annotations: the approach automatically construct structured formal queries from keywords. The authors propose a new process where they introduce a novel context-based query autocompletion feature to help the users to construct their keywords query by suggesting queries given prefixes. They also address the problem of context-based generating formal queries by exploiting user's query history, where previous queries can be used as contextual information for generating a new query. With the first tests, the authors' approach achieved encouraging results.
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28

Mannino, M. V. "Extensions to query languages for graph traversal problems." IEEE Transactions on Knowledge and Data Engineering 2, no. 3 (1990): 353–63. http://dx.doi.org/10.1109/69.60798.

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29

Patnaik, L. M., and D. M. Chowdhary. "Generalized Query-By-Rule: a heterogeneous database query language." Computer Languages 10, no. 3-4 (January 1985): 165–78. http://dx.doi.org/10.1016/0096-0551(85)90014-1.

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30

Kumar Deb, Uttam, and Dencho N. Batanov . "Comparative Analysis of Three Promising XML Query Languages and Some Recommendations." Information Technology Journal 4, no. 4 (September 15, 2005): 439–44. http://dx.doi.org/10.3923/itj.2005.439.444.

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31

Kuijpers, Bart, and Walied Othman. "Trajectory databases: Data models, uncertainty and complete query languages." Journal of Computer and System Sciences 76, no. 7 (November 2010): 538–60. http://dx.doi.org/10.1016/j.jcss.2009.10.002.

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32

Giallorenzo, Saverio, Fabrizio Montesi, Larisa Safina, and Stefano Pio Zingaro. "Ephemeral data handling in microservices with Tquery." PeerJ Computer Science 8 (July 22, 2022): e1037. http://dx.doi.org/10.7717/peerj-cs.1037.

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The adoption of edge and fog systems, along with the introduction of privacy-preserving regulations, compel the usage of tools for expressing complex data queries in an ephemeral way. That is, queried data should not persist. Database engines partially address this need, as they provide domain-specific languages for querying data. Unfortunately, using a database in an ephemeral setting has inessential issues related to throughput bottlenecks, scalability, dependency management, and security (e.g., query injection). Moreover, databases can impose specific data structures and data formats, which can hinder the development of microservice architectures that integrate heterogeneous systems and handle semi-structured data. In this article, we present Jolie/Tquery, the first query framework designed for ephemeral data handling in microservices. Jolie/Tquery joins the benefits of a technology-agnostic, microservice-oriented programming language, Jolie, and of one of the most widely-used query languages for semi-structured data in microservices, the MongoDB aggregation framework. To make Jolie/Tquery reliable for the users, we follow a cleanroom software engineering process. First, we define Tquery, a theory for querying semi-structured data compatible with Jolie and inspired by a consistent variant of the key operators of the MongoDB aggregation framework. Then, we describe how we implemented Jolie/Tquery following Tquery and how the Jolie type system naturally captures the syntax of Tquery and helps to preserve its invariants. To both illustrate Tquery and Jolie/Tquery, we present the use case of a medical algorithm and build our way to a microservice that implements it using Jolie/Tquery. Finally, we report microbenchmarks that validate the expectation that, in the ephemeral case, using Jolie/Tquery outperforms using an external database (MongoDB, specifically).
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33

Prabhakar, Dinesh Kumar, Sukomal Pal, and Chiranjeev Kumar. "Query Expansion for Tansliterated Text Retrieval." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (January 7, 2021): 1–34. http://dx.doi.org/10.1145/3447649.

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With Web 2.0, there has been exponential growth in the number of Web users and the volume of Web content. Most of these users are not only consumers of the information but also generators of it. People express themselves here in colloquial languages, but using Roman script (transliteration). These texts are mostly informal and casual, and therefore seldom follow grammar rules. Also, there does not exist any prescribed set of spelling rules in transliterated text. This freedom leads to large-scale spelling variations, which is a major challenge in mixed script information processing. This article studies different existing phonetic algorithms to handle the issue of spelling variation, points out the limitations of them, and proposes a novel phonetic encoding approach with two different flavors in the light of Hindi transliteration. Experiments performed over Hindi song lyrics retrieval in mixed script domain with three different retrieval models show that proposed approaches outperform the existing techniques in a majority of the cases (sometimes statistically significantly) for a number of metrics like nDCG@1, nDCG@5, nDCG@10, MAP, MRR, and Recall.
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34

Grohe, Martin, and Nicole Schweikardt. "Comparing the succinctness of monadic query languages over finite trees." RAIRO - Theoretical Informatics and Applications 38, no. 4 (October 2004): 343–73. http://dx.doi.org/10.1051/ita:2004017.

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35

Fakas, Georgios John, Ben Cawley, and Zhi Cai. "Automated Generation of Personal Data Reports from Relational Databases." Journal of Information & Knowledge Management 10, no. 02 (June 2011): 193–208. http://dx.doi.org/10.1142/s0219649211002936.

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This paper presents a novel approach for extracting personal data and automatically generating Personal Data Reports (PDRs) from relational databases. Such PDRs can be used among other purposes for compliance with Subject Access Requests of Data Protection Acts. Two methodologies with different usability characteristics are introduced: (1) the GDSBased Method and (2) the By Schema Browsing Method. The proposed methdologies combine the use of graphs and query languages for the construction of PDRs. The novelty of these methodologies is that they do not require any prior knowledge of either the database schema or of any query language by the users. An optimisation algorithm is proposed that employs Hash Tables and reuses already found data. We conducted several queries on two standard benchmark databases (i.e. TPC-H and Microsoft Northwind) and we present the performance results.
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36

Bertino, E., M. Negri, G. Pelagatti, and L. Sbattella. "Object-oriented query languages: the notion and the issues." IEEE Transactions on Knowledge and Data Engineering 4, no. 3 (June 1992): 223–37. http://dx.doi.org/10.1109/69.142014.

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37

Patel, Chandrakant D., and Jayesh M. Patel. "Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization." International Journal of Intelligent Systems and Applications 13, no. 3 (June 8, 2021): 23–34. http://dx.doi.org/10.5815/ijisa.2021.03.03.

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With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing.
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38

Hull, Richard, and Jianwen Su. "Algebraic and calculus query languages for recursively typed complex objects." Journal of Computer and System Sciences 47, no. 1 (August 1993): 121–56. http://dx.doi.org/10.1016/0022-0000(93)90022-o.

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39

Andries, Marc, and Jan Paredaens. "On Instance-Completeness for Database Query Languages involving Object Creation." Journal of Computer and System Sciences 52, no. 2 (April 1996): 357–73. http://dx.doi.org/10.1006/jcss.1996.0027.

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40

Epstein, Richard G. "The TableTalk query language." Journal of Visual Languages & Computing 2, no. 2 (June 1991): 115–41. http://dx.doi.org/10.1016/s1045-926x(05)80026-6.

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41

RASCHID, LOUIQA, and YA-HUI CHANG. "INTEROPERABLE QUERY PROCESSING FROM OBJECT TO RELATIONAL SCHEMAS BASED ON A PARAMETERIZED CANONICAL REPRESENTATION." International Journal of Cooperative Information Systems 04, no. 01 (March 1995): 81–120. http://dx.doi.org/10.1142/s0218843095000044.

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In this paper, we develop techniques for interoperable query processing between object and relational schemas. The objective is to pose a query against a local object schema and be able to share information transparently from target relational databases. Our approach is a mapping approach (as opposed to a global schema approach) and is based on using canonical representations (CR). We use one CR for resolving heterogeneity based on the object and relational query languages. We use a second parameterized CR to resolve representational heterogeneity between object and relational schema, and to build a mapping knowledge dictionary. There is also a set of mapping rules, based on the parameters of the CR, which defines the appropriate mapping between schemas. A query posed against the local object schema is first represented in the CR for queries, and then transformed by the mapping rules, to an appropriate query for the target relational schema, using relevant information from the mapping knowledge dictionary. The use of the parameterized CR allows us to build the mapping knowledge dictionary easily, and allows reusability of the mapping rules.
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42

Formica, Anna, Elaheh Pourabbas, and Maurizio Rafanelli. "Constraint relaxation of the polygon-polyline topological relation for geographic pictorial query languages." Computer Science and Information Systems 10, no. 3 (2013): 1053–75. http://dx.doi.org/10.2298/csis120302017f.

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43

Drewes, Frank, and Johanna Högberg. "Query Learning of Regular Tree Languages: How to Avoid Dead States." Theory of Computing Systems 40, no. 2 (August 3, 2005): 163–85. http://dx.doi.org/10.1007/s00224-005-1233-3.

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44

Jabal, Amani Abu, and Elisa Bertino. "A Comprehensive Query Language for Provenance Information." International Journal of Cooperative Information Systems 27, no. 03 (September 2018): 1850007. http://dx.doi.org/10.1142/s0218843018500077.

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We design and implement a query language for a secure, interoperable, and multi-granular provenance framework, referred to as QL-SimP. Our language supports two provenance representations (relational and graph-based) due to its independence from the underlying provenance representation. It also supports various queries that can be utilized for difference provenance applications. We integrate Computational Research Infrastructure for Science (CRIS) — a real-world system for managing scientific data — with our provenance framework, thus making it possible to query CRIS provenance information by using our provenance language. We have also evaluated our provenance queries based on relational and graph databases.
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45

Bassiouni, M. A. "Design and Implementation of Extended Boolean and Comparison Operators for Time-Oriented Query Languages." Computer Journal 37, no. 7 (July 1, 1994): 576–87. http://dx.doi.org/10.1093/comjnl/37.7.576.

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46

EGENHOFER, MAX J. "Query Processing in Spatial-Query-by-Sketch." Journal of Visual Languages & Computing 8, no. 4 (August 1997): 403–24. http://dx.doi.org/10.1006/jvlc.1997.0054.

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47

Ozsoyoglu, G., and WA Abdul-Qader. "Human factors study of two screen-oriented query languages: STBE and QBE." Information and Software Technology 34, no. 1 (January 1992): 3–15. http://dx.doi.org/10.1016/0950-5849(92)90090-c.

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48

Tansel, A. U. "A Historical query language." Information Sciences 53, no. 1-2 (January 1991): 101–33. http://dx.doi.org/10.1016/0020-0255(91)90060-8.

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49

SEBILLO, MONICA, GENOVEFFA TORTORA, and GIULIANA VITIELLO. "The Metaphor GIS Query Language." Journal of Visual Languages & Computing 11, no. 4 (August 2000): 439–54. http://dx.doi.org/10.1006/jvlc.2000.0170.

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50

Almendros-Jiménez, Jesús M. "A Prolog-based Query Language for OWL." Electronic Notes in Theoretical Computer Science 271 (March 2011): 3–22. http://dx.doi.org/10.1016/j.entcs.2011.02.008.

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