Relevant bibliographies by topics / Relational pattern

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Relational pattern'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Relational pattern"

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Gatterbauer, Wolfgang, and Cody Dunne. "On The Reasonable Effectiveness of Relational Diagrams: Explaining Relational Query Patterns and the Pattern Expressiveness of Relational Languages." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–27. http://dx.doi.org/10.1145/3639316.

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Comparing relational languages by their logical expressiveness is well understood. Less well understood is how to compare relational languages by their ability to represent relational query patterns. Indeed, what are query patterns other than "a certain way of writing a query"? And how can query patterns be defined across procedural and declarative languages, irrespective of their syntax? To the best of our knowledge, we provide the first semantic definition of relational query patterns by using a variant of structure-preserving mappings between the relational tables of queries. This formalism allows us to analyze the relative pattern expressiveness of relational language fragments and create a hierarchy of languages with equal logical expressiveness yet different pattern expressiveness. Notably, for the non-disjunctive language fragment, we show that relational calculus can express a larger class of patterns than the basic operators of relational algebra. Our language-independent definition of query patterns opens novel paths for assisting database users. For example, these patterns could be leveraged to create visual query representations that faithfully represent query patterns, speed up interpretation, and provide visual feedback during query editing. As a concrete example, we propose Relational Diagrams, a complete and sound diagrammatic representation of safe relational calculus that is provably (i) unambiguous, (ii) relationally complete, and (iii) able to represent all query patterns for unions of non-disjunctive queries. Among all diagrammatic representations for relational queries that we are aware of, ours is the only one with these three properties. Furthermore, our anonymously preregistered user study shows that Relational Diagrams allow users to recognize patterns meaningfully faster and more accurately than SQL.
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Hońko, Piotr. "Relational pattern updating." Information Sciences 189 (April 2012): 208–18. http://dx.doi.org/10.1016/j.ins.2011.12.004.

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Gatterbauer, Wolfgang, and Cody Dunne. "Relational Diagrams and the Pattern Expressiveness of Relational Languages." ACM SIGMOD Record 54, no. 1 (2025): 80–88. https://doi.org/10.1145/3733620.3733637.

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Comparing relational languages by their logical expressiveness is well understood. Less understood is how to compare relational languages by their ability to represent relational query patterns. Indeed, what are query patterns other than ''a certain way of writing a query''? And how can query patterns be defined across procedural and declarative languages, irrespective of their syntax? Our SIGMOD 2024 paper proposes a semantic definition of relational query patterns that uses a variant of structurepreserving mappings between the relational tables of queries. This formalism allows us to analyze the relative pattern expressiveness of relational languages. Notably, for the nondisjunctive language fragment, we show that relational calculus (RC) can express a larger class of patterns than the basic operators of relational algebra (RA). We also propose Relational Diagrams, a complete and sound diagrammatic representation of safe relational calculus. These diagrams can represent all query patterns for unions of non-disjunctive queries, in contrast to visual query representations that derive visual marks from the basic operators of algebra. Our anonymously preregistered user study shows that Relational Diagrams allow users to recognize relational patterns meaningfully faster and more accurately than they can with SQL.
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Takan, Savas, and Gokmen Katipoglu. "Relational Logging Design Pattern." Computers, Materials & Continua 75, no. 1 (2023): 51–65. http://dx.doi.org/10.32604/cmc.2023.035282.

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Almagro-Blanco, Pedro, Fernando Sancho-Caparrini, and Joaquín Borrego-Díaz. "Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning." Mathematics 11, no. 22 (2023): 4672. http://dx.doi.org/10.3390/math11224672.

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Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this type of learning model suffers from two fundamental problems: the computational complexity arising from relational queries and the lack of a robust and general framework to serve as the basis for relational learning methods. In this paper, we propose an efficient graph query framework that allows for cyclic queries in polynomial time and is ready to be used in pattern-based learning methods. This solution uses logical predicates instead of graph isomorphisms for query evaluation, reducing complexity and allowing for query refinement through atomic operations. The main differences between our method and other previous pattern-based graph query approaches are the ability to evaluate arbitrary subgraphs instead of nodes or complete graphs, the fact that it is based on mathematical formalization that allows the study of refinements and their complementarity, and the ability to detect cyclic patterns in polynomial time. Application examples show that the proposed framework allows learning relational classifiers to be efficient in generating data with high expressiveness capacities. Specifically, relational decision trees are learned from sets of tagged subnetworks that provide both classifiers and characteristic patterns for the identified classes.
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Kamiya, Yohei, and Hirohisa Seki. "Distributed Mining of Closed Patterns from Multi-Relational Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 804–9. http://dx.doi.org/10.20965/jaciii.2015.p0804.

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In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from distributed multi-relational databases (MRDBs). Since the computation of MRDM is costly compared with the conventional itemset mining, we propose some efficient methods for computing closed patterns using the techniques studied in Inductive Logic Programming (ILP) and Formal Concept Analysis (FCA). Given a set oflocaldatabases, we first compute sets of their closed patterns (concepts) using a closed pattern mining algorithm tailored to MRDM, and then generate the set of closed patterns in the global database by utilizing themergeoperator. We also present some experimental results, which shows the effectiveness of the proposed methods.
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Ray, K. S., and T. K. Dinda. "Pattern classification using fuzzy relational calculus." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 33, no. 1 (2003): 1–16. http://dx.doi.org/10.1109/tsmcb.2002.804361.

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Huet, B., and E. R. Hancock. "Line pattern retrieval using relational histograms." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 12 (1999): 1363–70. http://dx.doi.org/10.1109/34.817414.

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Riedewald, Mirek. "Technical Perspective: Relational Diagrams and the Pattern Expressiveness of Relational Languages." ACM SIGMOD Record 54, no. 1 (2025): 79. https://doi.org/10.1145/3733620.3733636.

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When a database researcher or user hears the word ''expressiveness,'' they most likely think about what kind of query some language or formalism supports. For instance, can a given version of SQL express recursion or computation of the transitive closure of a graph? For what fragment of relational calculus can we decide query containment? Can relational algebra express every relational-calculus query and vice versa? The following paper by Gatterbauer and Dunne may forever change the discourse on expressiveness in the context of relational languages by prompting the follow-up question: ''Do you mean logical expressiveness or pattern expressiveness?''
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Fauzi, Arwin Happy Nur, and Masduki Masduki. "Student’s Anomaly Reasoning in Solving Number Pattern in terms of Gender." Jurnal Didaktik Matematika 9, no. 2 (2022): 328–42. http://dx.doi.org/10.24815/jdm.v9i2.27146.

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Relational reasoning plays an important role in helping students to understand mathematical concepts. The student's ability to distinguish patterns or objects is one of the understandings of mathematical concept indicators. The anomaly dimension is part of the relational reasoning that students need to be able to determine a pattern or object in mathematics. This study aims to reveal the student's relational reasoning ability of anomaly dimension in solving number pattern problems in terms of gender differences. The subjects of this study are 52 grade-8 students in one of Muhammadiyah Junior High Schools in Kartasura. We used two similar problems on number patterns to disclose the student's ability to identify the pattern deviation in solving problems. The two selected students had relatively similar in their mathematical abilities. The finding showed that female subject met the three anomaly dimension indicators: identification, interpretation, and adaptation. Conversely, male student cannot fulfill the anomaly indicators. He cannot recognize pattern deviation in the formed mathematical model. He also failed to identify a pattern different from the two problems. Although the subjects interviewed were limited, the finding provided the insightful into the differences in anomaly reasoning abilities in male and female students
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Dissertations / Theses on the topic "Relational pattern"

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Spyropoluou, Eirini. "Local pattern mining in multi-relational data." Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654116.

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Multi-relational data mining has so far been synonym to methods based on Inductive Logic Programming (ILP) , which discover frequent first-order logic rules in the data. This is due to the fact that ILP conveniently captures the multi-relational structure, while there has not been a suitable pattern syntax extension of an itemset for the case of multi-relational data. Local pattern mining methods have mostly focused on mining a single relation. A common strategy for mining multi-relational data (MRD) has been to apply frequent items et mining on the join of all database relations. However, when flattening the data in this way, important structural information is lost and itemsets do not capture all the associations in the data. This thesis describes our research that led to a new approach for local pattern mining in multi-relational data. The final result of this research is summarised as follows. We define the new pattern syntax of Maximal Complete Connected Subsets (MCCSs) for MRD with binary relations, which captures well the structure of the original data. We additionally propose the generalisation of MCCSs, called N-MCCSs, for MRD containing relations of any arity. We demonstrate how N-MCCSs contain tiles [27] and n-sets [16] as special cases. Furthermore, we propose RMiner, an efficient algorithm to mine MCCSs and N -RMiner an efficient algorithm to mine N-MCCSs. We show experimentally that N-RMiner, while applicable to MRD in general, when applied to a Single n -ary relation, considerably outperforms the state of the art algorithm for mining n-sets [16] on real world datasets. Finally, this work is incorporated into a general data mining framework for quantifying the subjective interestingness of patterns based the prior information of the user.
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Kanodia, Juveria. "Structural advances for pattern discovery in multi-relational databases /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/978.

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Dindar, Nihal. "Pattern matching over sequences of rows in a relational database system." Zürich : ETH, Swiss Federal Institute of Technology Zurich, Department of Computer Science, Systems Group, 2008. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=418.

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Miller, Kenyon Russell. "Convergent neural algorithms for pattern matching using high-order relational descriptions." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/8219.

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Costa, Mauro Sergio Figueiredo. "Object recognition and pose estimation using appearance-based features and relational indexing /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6055.

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Bigby, Janice A. "QEEG and LORETA findings in children with histories of relational trauma." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc28394/.

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Abuse and neglect occurring in childhood have been associated with a number of functional and physiological effects on the brain. This study extends previous research that investigated the quantitative electroencephalogram (qEEG) patterns in children with histories of relational trauma through the inclusion of additional participants and measures. As in previous studies, the relative power, absolute power, and coherence values in children with histories of abuse were compared to the Neuroguide database. Results did not show any significant differences in relative or absolute power in the theta range. Similarly, there were no significant coherence differences. Database comparisons were also made using low resolution electromagnetic tomography (LORETA) in order to determine which sub-cortical brain structures may be affected by abuse or trauma, though there were no significant differences in any frequency (0-30Hz). A review of the literature suggests that the prevalence of mu in normal adults and children ranges from 0 to 19%. The present study found a mu prevalence rate of 60.6% in the children who experienced abuse or neglect. Finally, comparisons were made between participants who demonstrate a mu pattern and those who do not to determine if this pattern is associated with certain behavioral and/or attention problems as assessed by the Child Behavior Checklist (CBCL) and the Tests of Variables of Attention (TOVA), respectively. There were no significant differences between children with a mu pattern versus children who did not exhibit a mu pattern on the Social Problems, Thought Problems, or Attention subscale scores on the CBCL or on the Commission subscale score on the TOVA.
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Van, Gijsen Rienier. "Defining a sub-Saharan fertility pattern and a standard for use with the relational Gompertz model." Master's thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/5887.

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Includes abstract.<br>Includes bibliographical references (leaves 122-124).<br>The relational Gompertz model is often used to obtain fertility estimates for sub-Saharan Africa populations. This indirect estimation technique is dependent on a fertility standard - the Booth standard. This standard was developed in 1979 using a selection of 33 Coale-Trussell schedules congruent with high fertility patterns. However, evidence from 61 Demographic and Health Surveys of sub-Saharan countries shows that fertility has decreased to levels that were considered medium fertility at the time the standard was developed. This raises concerns about the continued relevance of the (high fertility) Booth standard. In particular, the standard would appear to consistently underestimate fertility among African women aged 45-49.
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Ngo, Chi Thao. "ONTOGENY OF EPISODIC MEMORY: A COMPONENTIAL APPROACH." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/571412.

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Psychology<br>Ph.D.<br>Episodic memory binds together the people, objects, and locations that make up the specific events of our lives, and allows the recall of our past in the service of current and future goals. Recent models of memory have posited that the hippocampus instantiates computations critical for episodic memory including mnemonic discrimination, relational binding, and holistic retrieval. Collectively, this set of studies aim to chart the ontogeny of each key components of episodic memory. We found robust improvements in children’s abilities to form complex relational structures and to make fine-grained discrimination for individual items from age 4 to age 6. However, relational memory dependent on context discrimination appears to follow a more protracted development. Furthermore, relational binding and mnemonic discrimination (item and context levels) undergo age-related decrements in senescence. Despite relatively poor relational binding capabilities, children as young as age 4 are able to retrieve multi-element events holistically, such as successfully retrieving of one aspect of an event predicts the retrieval success of other aspects from the same event. Critically, the degree of holistic episodic retrieval increases from age 4 to young adulthood. This multi-process approach provides important theoretical insights into lifespan profile of episodic memory.<br>Temple University--Theses
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Kristensen, Johnstone Tonje. "Surface patterns, spatiality and pattern relations in textile design." Licentiate thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12987.

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This licentiate thesis focuses on surface patterns, spatiality, and pattern relations in textile design, and aims to explore surface patterns as spatial definers and what they mean in the context of surface patterns. A secondary focus relates to applying conceptual spatial determinations as alternative design variables in design processes, and exploring how these could be used to define and analyse pattern relations. Through a series of exploratory design experiments that used printed and projected surface patterns in a three-dimensional setting, which were documented using photographs and film, the notion of pattern relations, wherein scale was used as a design variable, was explored. The outcome of the experiments showed the expressional possibilities that surface patterns may provide in a defined space, and how these are connected to pattern relations. In order to encourage an accompanying discussion regarding alternative methods of analysing surface patterns, the construction of a theoretical model was initiated. Workshops with design students were used as another practical method in this work. The results showed that there is great potential in using conceptual spatial determinations to define pattern relations by viewing surface patterns as spatial definers, rather than taking a traditional perspective on their functions. Another outcome is the theoretical model, which proposes a specific approach to pattern relations. This research demonstrates how conceptual spatial determinations can benefit the textile design process, as well as design teaching, which could in turn provide the field with new expressions that may lead to a change in or fruitful addition to the practice.
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Buzo, Amir. "Intelligent Data Layer: : An approach to generating data layer from normalized database model." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-22170.

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Model View Controller (MVC) software architecture is widely spread and commonly used in application’s development. Therefore generation of data layer for the database model is able to reduce cost and time. After research on current Object Relational Mapping (ORM) tools, it was discovered that there are generating tools like Data Access Object (DAO) and Hibernate, however their usage causes problems like inefficiency and slow performance due to many connections with database and set up time. Most of these tools are trying to solve specific problems rather than generating a data layer which is an important component and the bottom layer of database centred applications. The proposed solution to the problem is an engineering approach where we have designed a tool named Generated Intelligent Data Layer (GIDL). GIDL tool generates small models which create the main data layer of the system according to the Database Model. The goal of this tool is to enable and allow software developers to work only with object without deep knowledge in SQL. The problem of transaction and commit is solved by the tool. Also filter objects are constructed for filtering the database. GIDL tool reduced the number of connections and also have a cache where to store object lists and modify them. The tool is compared under the same environment with Hibernate and showed a better performance in terms of time evaluations for the same functions. GIDL tool is beneficial for software developers, because it generates the entire data layer.
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Books on the topic "Relational pattern"

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Džeroski, Sašo. Relational Data Mining. Springer Berlin Heidelberg, 2001.

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1948-, Fox Bonnie, ed. Family patterns, gender relations. 3rd ed. Oxford University Press, 2009.

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1948-, Fox Bonnie J., ed. Family patterns, gender relations. Oxford University Press, 1993.

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Fox, Bonnie. Family patterns, gender relations. 3rd ed. Oxford University Press, 2009.

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Fox, Bonnie J. Family Patterns, Gender Relations. Oxford University Press, 1993.

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1948-, Fox Bonnie, ed. Family patterns, gender relations. 2nd ed. Oxford University Press, 2001.

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Petrucci, Alessandra, and Rosanna Verde, eds. SIS 2017. Statistics and Data Science: new challenges, new generations. Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-521-0.

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The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data.
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1962-, Parker Philip, ed. Changing patterns of employee relations. Harvester Wheatsheaf, 1990.

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Whiteman, Kaslow Florence, ed. Handbook of relational diagnosis and dysfunctional family patterns. Wiley, 1996.

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Nonneman, Gerd. EU-GCC relations: Dynamics, patterns & perspectives. Gulf Research Center, 2006.

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Book chapters on the topic "Relational pattern"

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Costa, Mauro S., and Linda G. Shapiro. "Relational indexing." In Advances in Structural and Syntactical Pattern Recognition. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61577-6_14.

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Foggia, Pasquale, Roberto Genna, and Mario Vento. "Prototype Learning with Attributed Relational Graphs." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44522-6_46.

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Kosarev, Dmitry, Petr Lozov, and Dmitry Boulytchev. "Relational Synthesis for Pattern Matching." In Programming Languages and Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64437-6_15.

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Pisanti, Nadia, Henry Soldano, and Mathilde Carpentier. "Incremental Inference of Relational Motifs with a Degenerate Alphabet." In Combinatorial Pattern Matching. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11496656_20.

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Calders, Toon, Bart Goethals, and Adriana Prado. "Integrating Pattern Mining in Relational Databases." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11871637_43.

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Giacometti, Arnaud, Patrick Marcel, and Arnaud Soulet. "A Relational View of Pattern Discovery." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20149-3_13.

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Rüping, Stefan. "Support Vector Machines in Relational Databases." In Pattern Recognition with Support Vector Machines. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45665-1_24.

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Mendoza-Castañeda, Efraín, Carlos A. Reyes-García, Hugo Jair Escalante, Wilfrido Moreno, and Alejandro Rosales-Pérez. "Enhanced Fuzzy-Relational Neural Network with Alternative Relational Products." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_81.

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El-Sonbaty, Yasser, and M. A. Ismail. "A New Error-Correcting Distance for Attributed Relational Graph Problems." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44522-6_28.

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Mustafa, Wail, Dirk Kraft, and Norbert Krüger. "Extracting Categories by Hierarchical Clustering Using Global Relational Features." In Pattern Recognition and Image Analysis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_61.

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Conference papers on the topic "Relational pattern"

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Wu, Wenhao, Hau-San Wong, Si Wu, and Tianyou Zhang. "Relational Matching for Weakly Semi-Supervised Oriented Object Detection." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02626.

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Zhang, Ziying, Xianglong Li, Qian Zhao, Xuehua Liao, and Zhousen Zhu. "A Study on Redis-Based Aided Caching Pattern for Relational Database Hotspot Data." In 2024 4th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE). IEEE, 2024. https://doi.org/10.1109/iarce64300.2024.00069.

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Askari, Farzaneh, Cyril Yared, Rohit Ramaprasad, Devin Garg, Anjun Hu, and James J. Clark. "Video Interaction Recognition using an Attention Augmented Relational Network and Skeleton Data." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00328.

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Zhang, Zhipeng. "Map-Free Trajectory Prediction in Traffic with Motion-Pattern-Enhanced Hyper-Relational Interaction Modeling." In 2025 IEEE 8th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2025. https://doi.org/10.1109/itoec63606.2025.10967808.

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Zhang, Yuqi, Han Luo, and Yinjie Lei. "Towards CLIP-Driven Language-Free 3D Visual Grounding via 2D-3D Relational Enhancement and Consistency." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01241.

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Lawal, Ahmad, Yingjie Yang, Nathanael L. Baisa, and Hongmei He. "A Novel Framework for Reservoir Permeability Prediction Using GPR with Grey Relational Grades and Uncertainty Quantification." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10827278.

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Kim, Dongmin, and Jaegi Son. "MDRM Pattern: Relational heterogeneous metadata management patterns in a microservice environment." In 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2023. http://dx.doi.org/10.1109/icufn57995.2023.10199729.

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Sun, Chen, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, and Cordelia Schmid. "Relational Action Forecasting." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00036.

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Park, Wonpyo, Dongju Kim, Yan Lu, and Minsu Cho. "Relational Knowledge Distillation." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00409.

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Sabale, Diandre Miguel, and Wolfgang Gatterbauer. "PatternVis: A Tool for Relational Pattern Visualization." In SIGMOD/PODS '25: International Conference on Management of Data. ACM, 2025. https://doi.org/10.1145/3722212.3725128.

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Reports on the topic "Relational pattern"

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Kaplan, I., G. Abdulla, S. Brugger, and S. Kohn. Implementing Graph Pattern Queries on a Relational Database. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/924192.

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Page, David, and Jude Shavlik. Knowledge-Intensive, Interactive and Efficient Relational Pattern Learning. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada457195.

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Koller, Daphne. Learning Statistical Patterns in Relational Data Using Probabilistic Relational Models. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada430268.

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Zheng, Jian. Relational Patterns Discovery in Climate with Deep Learning Model. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.01.05.

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Haury, Loren R. Zooplankton Patterns in Relation to Physical Processes and Behavior. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada262726.

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University of Manchester. Patterns and Prevalence of Adult Food Allergy. Food Standards Agency, 2024. http://dx.doi.org/10.46756/sci.fsa.ehu454.

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This project has been set up to provide a step-change in our understanding of food allergy in adulthood by determining its prevalence in the adult population. It provided data to allow the trajectories of the condition in relation to both persistent allergy from childhood and adult-onset food allergy to be described, together with adverse reactions to foods that are not mediated by IgE.
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Haan, Matthew M., James R. Russell, Daniel G. Morrical, and Daryl R. Strohbehn. Effect of Grazing Management on Cattle Distribution Patterns in Relation to Pasture Streams. Iowa State University, 2007. http://dx.doi.org/10.31274/ans_air-180814-17.

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Haan, Mathew M., James R. Russell, Daniel G. Morrical, and Daryl R. Strohbehn. Effect of Grazing Management on Cattle Distribution Patterns in Relation to Pasture Streams. Iowa State University, 2008. http://dx.doi.org/10.31274/ans_air-180814-590.

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Harrington, Cherise B. Patterns of diagnostic care in nonspecific low back pain: Relation to patient satisfaction and perceived health. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ad1013990.

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Brandenberg, Scott, Jonathan Stewart, Kenneth Hudson, Dong Youp Kwak, Paolo Zimmaro, and Quin Parker. Ground Failure of Hydraulic Fills in Chiba, Japan and Data Archival in Community Database. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2024. http://dx.doi.org/10.55461/amnh7013.

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This report describes analysis of ground failure and lack thereof observed in the Mihama Ward portion of Chiba, Japan following the 2011 M9.0 Tohoku Earthquake. In conjunction with this work, we have also significantly expanded the laboratory component of the Next Generation Liquefaction (NGL) relational database. The district referred to as Mihama Ward is on ground composed of hydraulic fill sluiced in by pipes, thereby resulting in a gradient of soil coarseness, with coarser soils deposited near the pipes and fine-grained soils carried further away. Observations from local researchers at Chiba University following the 2011 Tohoku Earthquake indicate that ground failure was observed closer to the locations where the pipes deposited the soil, and not further away. This ground failure consisted of extensive sand boiling and ground cracking, which led to building settlement and pipe breaks. Our hypothesis at the outset of the project was that liquefaction susceptibility might explain the pattern of ground failure. Specifically, soils deposited near the pipes are susceptible due to their coarser texture, while soils further from the pipes may be non-susceptible due to the presence of clay minerals and higher plasticity. Were this hypothesis borne out by evidence, soil in the transition zone would have provided important insights about liquefaction susceptibility. Based on testing of soils in our laboratory, we find this hypothesis to be only partially correct. We have confirmed that there are regions with high clay contents and no ground failure and other regions with predominantly granular soils and extensive surface manifestation of liquefaction. Where the hypothesis breaks down is in the transition zone, where we found that the fine-grained soils are non-plastic, and therefore they are susceptible to liquefaction. Our interpretation is that these silt materials likely liquefied during the earthquake, but did not manifest liquefaction. Two factors may have contributed to this lack of manifestation: (1) level ground conditions and lack of large driving static shear stresses (structures in the region are light residential construction) and (2) the silt is less likely to erode to the surface and form silt boils than the sandier soils that produced surface manifestations. This case history points to the importance of separating triggering (defined as the development of significant excess pore pressure and loss of strength) from manifestation (defined as observations of ground failure, including cracking, sand boils, and lateral spreading). The Mihama Ward case history involved laboratory tests performed by Tokyo Soil Research Co. Ltd. and the UCLA geotechnical laboratory. Given the importance of this data to the understanding of this case history, we recognized a need to incorporate laboratory tests in the NGL database alongside field tests and liquefaction observations. We therefore developed an organizational structure for laboratory tests, including direct simple shear, triaxial compression, and consolidation, and implemented the schema in the NGL database. We then uploaded data from tests performed by Tokyo Soil and UCLA. Furthermore, numerous other researchers have also uploaded laboratory test data for other sites. This report describes the organizational structure of the laboratory component of the database, and a tool for interacting with laboratory data.
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