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Journal articles on the topic 'Query formulation'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Wacholder, Nina. "Interactive query formulation." Annual Review of Information Science and Technology 45, no. 1 (2011): 157–96. http://dx.doi.org/10.1002/aris.2011.1440450111.

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2

Cheung, Alvin, and Armando Solar-Lezama. "Computer-Assisted Query Formulation." Foundations and Trends® in Programming Languages 3, no. 1 (2016): 1–94. http://dx.doi.org/10.1561/2500000018.

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3

Dr.A., Mummoorthy, Bhasker B., and Karthik Deep Yadav S.J. "Query Formulation Technique Using of Data Web Mining." Bonfring International Journal of Networking Technologies and Applications 5, no. 1 (March 30, 2018): 06–08. http://dx.doi.org/10.9756/bijnta.8368.

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4

Efthimiadis, Efthimis N., Jeff Huang, Amanda Spink, and Jim Jansen. "Query formulation in web search." Proceedings of the American Society for Information Science and Technology 46, no. 1 (2009): 1–3. http://dx.doi.org/10.1002/meet.2009.1450460131.

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Bhatia, Sanjiv K., Jitender S. Deogun, and Vijay V. Raghavan. "Conceptual query formulation and retrieval." Journal of Intelligent Information Systems 5, no. 3 (November 1995): 183–209. http://dx.doi.org/10.1007/bf00962233.

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6

Silverstein, Steven H. "An Index Model for Query Formulation." Legal Reference Services Quarterly 10, no. 3 (December 5, 1990): 115–24. http://dx.doi.org/10.1300/j113v10n03_06.

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7

Wong, S. K. M., and Y. Y. Yao. "Query formulation in linear retrieval models." Journal of the American Society for Information Science 41, no. 5 (July 1990): 334–41. http://dx.doi.org/10.1002/(sici)1097-4571(199007)41:5<334::aid-asi4>3.0.co;2-2.

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8

Niemi, Timo, and Kalervo Järvelin. "Advanced query formulation in deductive databases." Information Processing & Management 28, no. 2 (January 1992): 181–99. http://dx.doi.org/10.1016/0306-4573(92)90045-2.

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9

Culpepper, J. Shane, Guglielmo Faggioli, Nicola Ferro, and Oren Kurland. "Topic Difficulty: Collection and Query Formulation Effects." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–36. http://dx.doi.org/10.1145/3470563.

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Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has been renewed interest in the notion of “query variations” which are essentially multiple user formulations for an information need. Like many retrieval models, some queries are highly effective while others are not. This is often an artifact of the collection being searched which might be more or less sensitive to word choice. Users rarely have perfect knowledge about the underlying collection, and so finding queries that work is often a trial-and-error process. In this work, we explore the fundamental problem of system interaction effects between collections, ranking models, and queries. To answer this important question, we formalize the analysis using ANalysis Of VAriance (ANOVA) models to measure multiple components effects across collections and topics by nesting multiple query variations within each topic. Our findings show that query formulations have a comparable effect size of the topic factor itself, which is known to be the factor with the greatest effect size in prior ANOVA studies. Both topic and formulation have a substantially larger effect size than any other factor, including the ranking algorithms and, surprisingly, even query expansion. This finding reinforces the importance of further research in understanding the role of query rewriting in IR related tasks.
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10

ter Hofstede, A. H. M. "Query Formulation as an Information Retrieval Problem." Computer Journal 39, no. 4 (April 1, 1996): 255–74. http://dx.doi.org/10.1093/comjnl/39.4.255.

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Taipalus, Toni, Mikko Siponen, and Tero Vartiainen. "Errors and Complications in SQL Query Formulation." ACM Transactions on Computing Education 18, no. 3 (September 7, 2018): 1–29. http://dx.doi.org/10.1145/3231712.

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12

Jarrar, M., and M. D. Dikaiakos. "A Query Formulation Language for the Data Web." IEEE Transactions on Knowledge and Data Engineering 24, no. 5 (May 2012): 783–98. http://dx.doi.org/10.1109/tkde.2011.41.

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13

MONZ, CHRISTOF. "Machine learning for query formulation in question answering." Natural Language Engineering 17, no. 4 (January 5, 2011): 425–54. http://dx.doi.org/10.1017/s1351324910000276.

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AbstractResearch on question answering dates back to the 1960s but has more recently been revisited as part of TREC's evaluation campaigns, where question answering is addressed as a subarea of information retrieval that focuses on specific answers to a user's information need. Whereas document retrieval systems aim to return the documents that are most relevant to a user's query, question answering systems aim to return actual answers to a users question. Despite this difference, question answering systems rely on information retrieval components to identify documents that contain an answer to a user's question. The computationally more expensive answer extraction methods are then applied only to this subset of documents that are likely to contain an answer. As information retrieval methods are used to filter the documents in the collection, the performance of this component is critical as documents that are not retrieved are not analyzed by the answer extraction component. The formulation of queries that are used for retrieving those documents has a strong impact on the effectiveness of the retrieval component. In this paper, we focus on predicting the importance of terms from the original question. We use model tree machine learning techniques in order to assign weights to query terms according to their usefulness for identifying documents that contain an answer. Term weights are learned by inspecting a large number of query formulation variations and their respective accuracy in identifying documents containing an answer. Several linguistic features are used for building the models, including part-of-speech tags, degree of connectivity in the dependency parse tree of the question, and ontological information. All of these features are extracted automatically by using several natural language processing tools. Incorporating the learned weights into a state-of-the-art retrieval system results in statistically significant improvements in identifying answer-bearing documents.
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14

Park, Uchang. "An algebraic formulation of the aggregative closure query." Theoretical Computer Science 166, no. 1-2 (October 1996): 49–62. http://dx.doi.org/10.1016/0304-3975(95)00081-x.

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15

Campbell, Douglas M., David W. Embley, and Bogdan Czejdo. "Graphical query formulation for an entity-relationship model." Data & Knowledge Engineering 2, no. 2 (June 1987): 89–121. http://dx.doi.org/10.1016/0169-023x(87)90017-6.

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16

Hsu, Yen-Chi, Cheng-Yao Hong, Ming-Sui Lee, and Tyng-Luh Liu. "Query-Driven Multi-Instance Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4158–65. http://dx.doi.org/10.1609/aaai.v34i04.5836.

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We introduce a query-driven approach (qMIL) to multi-instance learning where the queries aim to uncover the class labels embodied in a given bag of instances. Specifically, it solves a multi-instance multi-label learning (MIML) problem with a more challenging setting than the conventional one. Each MIML bag in our formulation is annotated only with a binary label indicating whether the bag contains the instance of a certain class and the query is specified by the word2vec of a class label/name. To learn a deep-net model for qMIL, we construct a network component that achieves a generalized compatibility measure for query-visual co-embedding and yields proper instance attentions to the given query. The bag representation is then formed as the attention-weighted sum of the instances' weights, and passed to the classification layer at the end of the network. In addition, the qMIL formulation is flexible for extending the network to classify unseen class labels, leading to a new technique to solve the zero-shot MIML task through an iterative querying process. Experimental results on action classification over video clips and three MIML datasets from MNIST, CIFAR10 and Scene are provided to demonstrate the effectiveness of our method.
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17

Saeed, Muhammad Rizwan, Charalampos Chelmis, and Viktor K. Prasanna. "ASQFor: Automatic SPARQL query formulation for the non-expert." AI Communications 31, no. 1 (February 1, 2018): 19–32. http://dx.doi.org/10.3233/aic-170746.

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18

Taipalus, Toni. "The effects of database complexity on SQL query formulation." Journal of Systems and Software 165 (July 2020): 110576. http://dx.doi.org/10.1016/j.jss.2020.110576.

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19

Hansen, Preben, Anni Järvelin, and Antti Järvelin. "Exploring manual and automatic query formulation in patent IR." Journal of Documentation 69, no. 6 (October 14, 2013): 873–98. http://dx.doi.org/10.1108/jd-12-2012-0166.

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20

Nakkouzi, Ziad S., and Caroline M. Eastman. "Query formulation for handling negation in information retrieval systems." Journal of the American Society for Information Science 41, no. 3 (April 1990): 171–82. http://dx.doi.org/10.1002/(sici)1097-4571(199004)41:3<171::aid-asi3>3.0.co;2-p.

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21

Kajanan, Sangaralingam, Yang Bao, Anindya Datta, Debra VanderMeer, and Kaushik Dutta. "Efficient automatic search query formulation using phrase-level analysis." Journal of the Association for Information Science and Technology 65, no. 5 (January 7, 2014): 1058–75. http://dx.doi.org/10.1002/asi.23022.

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22

Lim, Joo-Hwee. "Building Visual Vocabulary for Image Indexation and Query Formulation." Pattern Analysis & Applications 4, no. 2-3 (June 2001): 125–39. http://dx.doi.org/10.1007/pl00014574.

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23

Baader, Franz, Meghyn Bienvenu, Carsten Lutz, and Frank Wolter. "Query and Predicate Emptiness in Ontology-Based Data Access." Journal of Artificial Intelligence Research 56 (May 30, 2016): 1–59. http://dx.doi.org/10.1613/jair.4866.

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In ontology-based data access (OBDA), database querying is enriched with an ontology that provides domain knowledge and additional vocabulary for query formulation. We identify query emptiness and predicate emptiness as two central reasoning services in this context. Query emptiness asks whether a given query has an empty answer over all databases formulated in a given vocabulary. Predicate emptiness is defined analogously, but quantifies universally over all queries that contain a given predicate. In this paper, we determine the computational complexity of query emptiness and predicate emptiness in the EL, DL-Lite, and ALC-families of description logics, investigate the connection to ontology modules, and perform a practical case study to evaluate the new reasoning services.
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24

de Souza, Solange Nice Alves, Edit Grassiani Lino de Campos, and Andre Roberto Doreto dos Santos. "A Tool for Query Formulation based on Entities and Roles." IEEE Latin America Transactions 4, no. 4 (June 2006): 279–84. http://dx.doi.org/10.1109/tla.2006.4472124.

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25

Shironoshita, E. P., Y. R. Jean-Mary, R. M. Bradley, and M. R. Kabuka. "semCDI: A Query Formulation for Semantic Data Integration in caBIG." Journal of the American Medical Informatics Association 15, no. 4 (July 1, 2008): 559–68. http://dx.doi.org/10.1197/jamia.m2732.

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26

Mahdabi, Parvaz, and Fabio Crestani. "Patent Query Formulation by Synthesizing Multiple Sources of Relevance Evidence." ACM Transactions on Information Systems 32, no. 4 (October 28, 2014): 1–30. http://dx.doi.org/10.1145/2651363.

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27

EGYHAZY, C. J. "FROM SOFTWARE REUSE TO DATABASE REUSE." International Journal of Software Engineering and Knowledge Engineering 10, no. 02 (April 2000): 227–49. http://dx.doi.org/10.1142/s0218194000000134.

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Our principal objective in this paper is to show how we have adapted a domain analysis methodology, used previously in software reuse, to the formulation of a single query against federated databases. Currently, we query federated databases by writing queries against each of the legacy databases, merge the responses, and resolve syntactic and semantic differences manually. Such a solution places unacceptable expectations on most users, thus being impractical. In this paper, we propose an approach that requires the instantiation of a single query_class specification against federated databases, that also resolves simple syntactic and semantic differences on demand, without using a global schema for member databases. We illustrate the approach by instantiating a single query_class specification built against three legacy database schemata. In summary, domain analysis tools not only facilitate the formulation of query specifications, but they also help to resolve the database heterogeneity problem.
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28

Sengupta, Arijit, and Ramesh Venkataraman. "DocBase." Journal of Database Management 22, no. 4 (October 2011): 30–56. http://dx.doi.org/10.4018/jdm.2011100102.

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This article introduces a complete storage and retrieval architecture for a database environment for XML documents. DocBase, a prototype system based on this architecture, uses a flexible storage and indexing technique to allow highly expressive queries without the necessity of mapping documents to other database formats. DocBase is an integration of several techniques that include (i) a formal model called Heterogeneous Nested Relations (HNR), (ii) a conceptual model XER (Extensible Entity Relationship), (ii) formal query languages (Document Algebra and Calculus), (iii) a practical query language (Document SQL or DSQL), (iv) a visual query formulation method with QBT (Query By Templates), and (v) the DocBase query processing architecture. This paper focuses on the overall architecture of DocBase including implementation details, describes the details of the query-processing framework, and presents results from various performance tests. The paper summarizes experimental and usability analyses to demonstrate its feasibility as a general architecture for native as well as embedded document manipulation methods.
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29

Dadashzadeh, Mohammad. "Converting Paradoxs QBE Set Queries Into Access 2000 SQL." Review of Business Information Systems (RBIS) 6, no. 2 (April 1, 2002): 43–56. http://dx.doi.org/10.19030/rbis.v6i2.4570.

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One of the most important promises of the move to an SQL-based accounting software package has been that it frees the accountant from the necessity of resorting to a programmer when retrieving information from the organization's database in response to unanticipated managerial needs. That promise is founded, in part, on the availability of a very high-level, visual relational query language interface known as Query By Example (QBE). Unfortunately, the implementation of QBE in Microsoft Access 2000 fails to support users in formulating complex queries involving set comparison that tend to arise in on-line analytical processing (OLAP) situations. And, while Paradoxs implementation of QBE makes the formulation of such queries quite intuitive, its built-in SQL translation feature fails to provide a clue on how to convert such queries into SQL. This paper presents a systematic approach based on formulating complex set queries in Paradoxs richer QBE notation and translating them into SQL queries that can be handled by Access 2000.
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30

Alencar, Andreza Leite de, and Ana Carolina Salgado. "Improving User Interaction on Ontology-based Peer Data Management Systems." iSys - Brazilian Journal of Information Systems 7, no. 2 (November 15, 2014): 67–85. http://dx.doi.org/10.5753/isys.2014.252.

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The issue of user interaction for query formulation and execution has been investigated for distributed and dynamic environments, such as Peer Data Management System (PDMS). Many of these PDMS are semantic based and composed by data peers which export schemas that are represented by ontologies. In the literature we can find some proposed PDMS interfaces, but none of them addresses, in a general way, the needs of a PDMS for user interaction. In this work we propose a visual user query interface for ontology-based PDMS. It provides a simple and straightforward interaction with this type of system. It aims not only providing a natural visual query interface but also supporting a precise and direct manipulation of the data schemas for query generation.
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31

Spavold, J. "Children and databases: an analysis of data entry and query formulation." Journal of Computer Assisted Learning 5, no. 3 (September 1989): 145–60. http://dx.doi.org/10.1111/j.1365-2729.1989.tb00153.x.

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32

Güner, Osman F., Douglas R. Henry, Thomas E. Moock, and Robert S. Pearlman. "Flexible queries in 3D searching. 2. Techniques in 3D query formulation." Tetrahedron Computer Methodology 3, no. 6 (January 1990): 557–63. http://dx.doi.org/10.1016/0898-5529(90)90158-5.

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33

Kurylev, V. A. "Formulation of an indirect query processing method for a database computer." Cybernetics 24, no. 6 (1989): 696–706. http://dx.doi.org/10.1007/bf01079141.

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34

Fowler, Richard H., Wendy A. L. Fowler, and Jorge L. Williams. "3D visualization of WWW semantic content for browsing and query formulation." Journal of Network and Computer Applications 20, no. 2 (April 1997): 135–49. http://dx.doi.org/10.1006/jnca.1997.0042.

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35

Azad, Hiteshwar Kumar, Akshay Deepak, and Kumar Abhishek. "Query Expansion for Improving Web Search." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 101–8. http://dx.doi.org/10.1166/jctn.2020.8635.

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The aim of this paper is to retrieve the most relevant expansion words for expanding the initial query of the user in order to enhance the outcomes of web search results. Query expansion plays a major role in reformulating a user’s initial query to a one more pertinent to the user’s intended meaning. The reformulated query is then used to obtain more appropriate outcomes from a large amount of information on the web. The proposed semantic query expansion technique uses Wikipedia and WordNet as data sources. Wikipedia is taken as a base for all query expansions because it is one of the most diversified and relevant databases available on the web. To further improve the proposed query expansion technique,WordNet—a lexical database—is used as the as another data source because the synonyms (synsets) of the query term provided by it can be quite useful for query expansion. The proposed expansion technique successfully combines the two data sources to retrieve the most relevant expansion terms from the data sources in response to the user’s original query. The proposed work has been divided into four phases: (1) extraction of relevant words from Wikipedia (2) extraction of relevant words from WordNet (3) merging of the expansion terms obtained from Wikipedia and WordNet, and (4) query formulation by combining the expansion terms using Boolean operators. This reformulated query is then fired on the web to find the desired result. The Experimental result shows a significant improvement in information retrieval using query expansion.
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36

Power, Robert A. "Large Catalogue Query Performance in Relational Databases." Publications of the Astronomical Society of Australia 24, no. 1 (2007): 13–20. http://dx.doi.org/10.1071/as06026.

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AbstractThe performance of the mysql and oracle database systems have been compared for a selection of astronomy queries using large catalogues of up to a billion objects. The queries tested are those expected from the astronomy community: general database queries, cone searches, neighbour finding and cross matching. The catalogue preparation, sql query formulation and database performance is presented. Most of the general queries perform adequately when appropriate indexes are present in the database. Each system performs well for cone search queries when the Hierarchical Triangular Mesh spatial index is used. Neighbour finding and cross matching are not well supported in a database environment when compared to software specifically developed to solve these problems.
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37

COSTANTINI, STEFANIA, and ANDREA FORMISANO. "Query answering in resource-based answer set semantics." Theory and Practice of Logic Programming 16, no. 5-6 (September 2016): 619–35. http://dx.doi.org/10.1017/s1471068416000478.

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AbstractIn recent work we defined resource-based answer set semantics, which is an extension to answer set semantics stemming from the study of its relationship with linear logic. In fact, the name of the new semantics comes from the fact that in the linear-logic formulation every literal (including negative ones) were considered as a resource. In this paper, we propose a query-answering procedure reminiscent of Prolog for answer set programs under this extended semantics as an extension of XSB-resolution for logic programs with negation.1We prove formal properties of the proposed procedure. Under consideration for acceptance in TPLP.
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38

Sridharan. "TRUST BASED AUTOMATIC QUERY FORMULATION SEARCH ON EXPERT AND KNOWLEDGE USERS SYSTEMS." Journal of Computer Science 10, no. 7 (July 1, 2014): 1174–85. http://dx.doi.org/10.3844/jcssp.2014.1174.1185.

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39

Vijayasarathy, Leo, and Gretchen Casterella. "The Effects of Information Request Language and Template Usage on Query Formulation." Journal of the Association for Information Systems 17, no. 10 (October 2016): 674–707. http://dx.doi.org/10.17705/1jais.00440.

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40

Serpen, Gursel, and Michael Riesen. "Knowledge Discovery for Query Formulation for Validation of a Bayesian Belief Network." Journal of Intelligent Learning Systems and Applications 02, no. 03 (2010): 156–66. http://dx.doi.org/10.4236/jilsa.2010.23019.

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41

Kumar, Aarti, and Sujoy Das. "Query Formulation for Heuristic Retrieval in Obfuscated and Translated Partially Derived Text." Journal of Information Science Theory and Practice 3, no. 1 (March 30, 2015): 24–39. http://dx.doi.org/10.1633/jistap.2015.3.1.2.

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42

Bhowmick, Sourav S., Huey Eng Chua, Byron Choi, and Curtis Dyreson. "VISUAL: Simulation of Visual Subgraph Query Formulation to Enable Automated Performance Benchmarking." IEEE Transactions on Knowledge and Data Engineering 29, no. 8 (August 1, 2017): 1765–78. http://dx.doi.org/10.1109/tkde.2017.2690392.

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43

Owei, Vesper, and Shamkant B. Navathe. "Enriching the conceptual basis for query formulation through relationship semantics in databases." Information Systems 26, no. 6 (September 2001): 445–75. http://dx.doi.org/10.1016/s0306-4379(01)00029-1.

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44

Barsky, Elena, and Judit Bar‐Ilan. "From the search problem through query formulation to results on the web." Online Information Review 29, no. 1 (February 2005): 75–89. http://dx.doi.org/10.1108/14684520510583954.

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45

Smith, Catherine L. "Domain-independent search expertise: Gaining knowledge in query formulation through guided practice." Journal of the Association for Information Science and Technology 68, no. 6 (March 7, 2017): 1462–79. http://dx.doi.org/10.1002/asi.23776.

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46

Mahlknecht, Giovanni, Anton Dignös, and Natalija Kozmina. "Modeling and querying facts with period timestamps in data warehouses." International Journal of Applied Mathematics and Computer Science 29, no. 1 (March 1, 2019): 31–49. http://dx.doi.org/10.2478/amcs-2019-0003.

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Abstract In this paper, we study various ways of representing and querying fact data that are time-stamped with a time period in a data warehouse. The main focus is on how to represent the time periods that are associated with the facts in order to support convenient and efficient aggregations over time. We propose three distinct logical models that represent time periods as sets of all time points in a period (instant model), as pairs of start and end time points of a period (period model), and as atomic units that are explicitly stored in a new period dimension (period∗ model). The period dimension is enriched with information about the days of each period, thereby combining the former two models. We use four different classes of aggregation queries to analyze query formulation, query execution, and query performance over the three models. An extensive empirical evaluation on synthetic and real-world datasets and the analysis of the query execution plans reveal that the period model is the best choice in terms of runtime and space for all four query classes.
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47

Bubnov, Yakov. "DNS Data Exfiltration Detection Using Online Planning for POMDP." European Journal of Engineering Research and Science 4, no. 9 (September 10, 2019): 22–25. http://dx.doi.org/10.24018/ejers.2019.4.9.1500.

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This paper addresses a problem of blocking Domain Name System (DNS) exfiltration in a computer network. DNS exfiltration implies unauthorized transfer of sensitive data from the organization network to the remote adversary. Given detector of data exfiltration in DNS lookup queries this paper proposes an approach to automate query blocking decisions. More precisely, it defines an L-parametric Partially Observable Markov Decision Process (POMDP) formulation to enforce query blocking strategy on each network egress point, where L is a hyper-parameter that defines necessary level of the network security. The efficiency of the approach is based on (i) absence of interactions between distributed detectors, blocking decisions are taken individually by each detector; (ii) blocking strategy is applied to each particular query, therefore minimizing potentially incorrect blocking decisions.
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48

Kang, Hyun-Kyu. "Design and Implementation of “Concept Wizard” Supporting Query Formulation with Concept Term Expansion." KIPS Transactions:PartB 9B, no. 4 (August 1, 2002): 437–44. http://dx.doi.org/10.3745/kipstb.2002.9b.4.437.

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49

Pan, Bing, Stephen W. Litvin, and Thomas E. O'Donnell. "Understanding accommodation search query formulation: The first step in putting `heads in beds'." Journal of Vacation Marketing 13, no. 4 (October 2007): 371–81. http://dx.doi.org/10.1177/1356766707081013.

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Yang, Ping-Jing, Hao-Chuan Wang, and Yu-Hsuan Liu. "Click-Search: Supporting information search with crowd-powered image-to-keyword query formulation." Journal of Visual Languages & Computing 46 (June 2018): 12–19. http://dx.doi.org/10.1016/j.jvlc.2016.09.002.

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