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Academic literature on the topic 'Rules database'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Rules database"

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Bonam, Janakiramaiah, and Ramamohan Reddy. "Balanced Approach for Hiding Sensitive Association Rules in Data Sharing Environment." International Journal of Information Security and Privacy 8, no. 3 (July 2014): 39–62. http://dx.doi.org/10.4018/ijisp.2014070103.

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Privacy preserving association rule mining protects the sensitive association rules specified by the owner of the data by sanitizing the original database so that the sensitive rules are hidden. In this paper, the authors study a problem of hiding sensitive association rules by carefully modifying the transactions in the database. The algorithm BHPSP calculates the impact factor of items in the sensitive association rules. Then it selects a rule which contains an item with minimum impact factor. The algorithm alters the transactions of the database to hide the sensitive association rule by reducing the loss of other non-sensitive association rules. The quality of a database can be well maintained by greedily selecting the alterations in the database with negligible side effects. The BHPSP algorithm is experimentally compared with a HCSRIL algorithm with respect to the performance measures misses cost and difference between original and sanitized databases. Experimental results are also mentioned demonstrating the effectiveness of the proposed approach.
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Ceri, Stefano, and Raghu Ramakrishnan. "Rules in database systems." ACM Computing Surveys 28, no. 1 (March 1996): 109–11. http://dx.doi.org/10.1145/234313.234362.

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B., Suma, and Shobha G. "Privacy preserving association rule hiding using border based approach." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 2021): 1137. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.

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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>
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Alotaibi, Obaid, and Eric Pardede. "Transformation of Schema from Relational Database (RDB) to NoSQL Databases." Data 4, no. 4 (November 27, 2019): 148. http://dx.doi.org/10.3390/data4040148.

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Relational database has been the de-facto database choice in most IT applications. In the last decade there has been increasing demand for applications that have to deal with massive and un-normalized data. To satisfy the demand, there is a big shift to use more relaxed databases in the form of NoSQL databases. Alongside with this shift, there is a need to have a structured methodology to transform existing data in relational database (RDB) to NoSQL database. The transformation from RDB to NoSQL database has become more challenging because there is no current standard on NoSQL database. The aim of this paper is to propose transformation rules of RDB Schema to various NoSQL database schema, namely document-based, column-based and graph-based databases. The rules are applied based on the type of relationships that can appear in data within a database. As a proof of concept, we apply the rules into a case study using three NoSQL databases, namely MongoDB, Cassandra, and Neo4j. A set of queries is run in these databases to demonstrate the correctness of the transformation results. In addition, the completeness of our transformation rules are compared against existing work.
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Hanson, Eric N., and Jennifer Widom. "An overview of production rules in database systems." Knowledge Engineering Review 8, no. 2 (June 1993): 121–43. http://dx.doi.org/10.1017/s0269888900000126.

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AbstractDatabase researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.
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Al-Khafaji, Hussien, Alaa Al-Hamami, and Abbas F. Abdul-Kader. "Design and Implementation of a Generator of Large , Dense ,or Sparse Databases to Test Association Rules Miner." Iraqi Journal for Computers and Informatics 40, no. 1 (December 31, 2002): 25–31. http://dx.doi.org/10.25195/ijci.v40i1.223.

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Association rules discovery has emerged as a very important problem in knowledge discovery in database and data mining. A number of algorithms is presented to mine association rules. There are many factors that affect the efficiency of rules mining algorithms, such as largeness, denances, and sparseness of databases used to be mined, in addition to number of items, number and average sizes of transactions, number and average sizes of frequent itemscts, and number and average sizes of potentially maximal itemsets. It is impossible to change present realworld catabase's characteristics to fairly test and determine the best and wurst cases of rule-mining algorithms. to be efficiently used for present and future databases. So the researchers attend to construct artificial database to qualitative and quantitative presence of the above mentioned factors to test the efficiency of rule mining algorithms and programs. The construction of such databases CATmes very large amount of the and efforts. This resent presents a software system, generator, to construct artificial databases.
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Bai, Yi Ming, Xian Yao Meng, and Xin Jie Han. "Mining Fuzzy Association Rules in Quantitative Databases." Applied Mechanics and Materials 182-183 (June 2012): 2003–7. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.2003.

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In this paper, we introduce a novel technique for mining fuzzy association rules in quantitative databases. Unlike other data mining techniques who can only discover association rules in discrete values, the algorithm reveals the relationships among different quantitative values by traversing through the partition grids and produces the corresponding Fuzzy Association Rules. Fuzzy Association Rules employs linguistic terms to represent the revealed regularities and exceptions in quantitative databases. After the fuzzy rule base is built, we utilize the definition of Support Degree in data mining to reduce the rule number and save the useful rules. Throughout this paper, we will use a set of real data from a wine database to demonstrate the ideas and test the models.
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Aiken, Alexander, Jennifer Widom, and Joseph M. Hellerstein. "Behavior of database production rules." ACM SIGMOD Record 21, no. 2 (June 1992): 59–68. http://dx.doi.org/10.1145/141484.130296.

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Gopagoni, Praveen Kumar, and Mohan Rao S K. "Distributed elephant herding optimization for grid-based privacy association rule mining." Data Technologies and Applications 54, no. 3 (May 15, 2020): 365–82. http://dx.doi.org/10.1108/dta-07-2019-0104.

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PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.
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Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.

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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.
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Dissertations / Theses on the topic "Rules database"

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Zhang, Heng. "Efficient database management based on complex association rules." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31917.

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The large amount of data accumulated by applications is stored in a database. Because of the large amount, name conflicts or missing values sometimes occur. This prevents certain types of analysis. In this work, we solve the name conflict problem by comparing the similarity of the data, and changing the test data into the form of a given template dataset. Studies on data use many methods to discover knowledge from a given dataset. One popular method is association rules mining, which can find associations between items. This study unifies the incomplete data based on association rules. However, most rules based on traditional association rules mining are item-to-item rules, which is a less than perfect solution to the problem. The data recovery system is based on complex association rules able to find two more types of association rules, prefix pattern-to-item, and suffix pattern-to-item rules. Using complex association rules, several missing values are filled in. In order to find the frequent prefixes and frequent suffixes, this system used FP-tree to reduce the time, cost and redundancy. The segment phrases method can also be used for this system, which is a method based on the viscosity of two words to split a sentence into several phrases. Additionally, methods like data compression and hash map were used to speed up the search.
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Butylin, Sergei. "Predictive Maintenance Framework for a Vehicular IoT Gateway Node Using Active Database Rules." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38568.

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This thesis describes a proposed design and implementation of a predictive maintenance engine developed to fulfill the requirements of the STO Company (Societe de transport de l'Outaouais) for maintaining vehicles in the fleet. Predictive maintenance is proven to be an effective approach and has become an industry standard in many fields. However, in the transportation industry, it is still in the stages of development due to the complexity of moving systems and the high level dimensions of involved parameters. Because it is almost impossible to cover all use cases of the vehicle operational process using one particular approach to predictive maintenance, in our work we take a systematic approach to designing a predictive maintenance system in several steps. Each step is implemented at the corresponding development stage based on the available data accumulated during system funсtioning cycle. % by dividing the entire system into modules and implementing different approaches. This thesis delves into the process of designing the general infrastructural model of the fleet management system (FMS), while focusing on the edge gateway module located on the vehicle and its function of detecting maintenance events based on current vehicle status. Several approaches may be used to detect maintenance events, such as a machine learning approach or an expert system-based approach. While the final version of fleet management system will use a hybrid approach, in this thesis paper we chose to focus on the second option based on expert knowledge, while machine learning has been left for future implementation since it requires extensive training data to be gathered prior to conducting experiments and actualizing operations. Inspired by the IDEA methodology which promotes mapping business rules as software classes and using the object-relational model for mapping objects to database entities, we take active database features as a base for developing a rule engine implementation. However, in contrast to the IDEA methodology which seeks to describe the specific system and its sub-modules, then build active rules based on the interaction between sub-systems, we are not aware of the functional structure of the vehicle due to its complexity. Instead, we develop a framework for creating specific active rules based on abstract classifications structured as ECA rules (event-condition-action), but with some expansions made due to the specifics of vehicle maintenance. The thesis describes an attempt to implement such a framework, and particularly the rule engine module, using active database features making it possible to encapsulate the active behaviour inside the database and decouple event detection from other functionalities. We provide the system with a set of example rules and then conduct a series of experiments analyzing the system for performance and correctness of events detection.
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Savasere, Ashok. "Efficient algorithms for mining association rules in large databases of cutomer transactions." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/8260.

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Visavapattamawon, Suwanna. "Application of active rules to support database integrity constraints and view management." CSUSB ScholarWorks, 2001. https://scholarworks.lib.csusb.edu/etd-project/1981.

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The project demonstrates the enforcement of integrity constraints in both the conventional and active database systems. The project implements a more complex user-defined constraint, a complicated view and more detailed database auditing on the active database system.
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李守敦 and Sau-dan Lee. "Maintenance of association rules in large databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31215531.

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Lee, Sau-dan. "Maintenance of association rules in large databases /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19003250.

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Singh, Rohit Ph D. Massachusetts Institute of Technology. "Automatically learning optimal formula simplifiers and database entity matching rules." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113938.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 153-161).
Traditionally, machine learning (ML) is used to find a function from data to optimize a numerical score. On the other hand, synthesis is traditionally used to find a function (or a program) that can be derived from a grammar and satisfies a logical specification. The boundary between ML and synthesis has been blurred by some recent work [56,90]. However, this interaction between ML and synthesis has not been fully explored. In this thesis, we focus on the problem of finding a function given large amounts of data such that the function satisfies a logical specification and also optimizes a numerical score over the input data. We present a framework to solve this problem in two impactful application domains: formula simplification in constraint solvers and database entity matching (EM). First, we present a system called Swapper based on our framework that can automatically generate code for efficient formula simplifiers specialized to a class of problems. Formula simplification is an important part of modern constraint solvers, and writing efficient simplifiers has largely been an arduous manual task. Evaluation of Swapper on multiple applications of the Sketch constraint solver showed 15-60% improvement over the existing hand-crafted simplifier in Sketch. Second, we present a system called EM-Synth based on our framework that generates as effective and more interpretable EM rules than the state-of-the-art techniques. Database entity matching is a critical part of data integration and cleaning, and it usually involves learning rules or classifiers from labeled examples. Evaluation of EM-Synth on multiple real-world datasets against other interpretable (shallow decision trees, SIFI [116]) and noninterpretable (SVM, deep decision trees) methods showed that EM-Synth generates more concise and interpretable rules without sacrificing too much accuracy.
by Rohit Singh.
Ph. D.
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Dudgikar, Mahesh. "A layered optimizer for mining association rules over relational database management systems." [Florida] : State University System of Florida, 2000. http://etd.fcla.edu/etd/uf/2000/ana6135/Master.pdf.

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Thesis (M.S.)--University of Florida, 2000.
Title from first page of PDF file. Document formatted into pages; contains xiii, 94 p.; also contains graphics. Vita. Includes bibliographical references (p. 92-93).
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LOPES, CARLOS HENRIQUE PEREIRA. "CLASSIFICATION OF DATABASE REGISTERS THROUGH EVOLUTION OF ASSOCIATION RULES USING GENETIC ALGORITHMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7297@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esta dissertação investiga a utilização de Algoritmos Genéticos (AG) no processo de descoberta de conhecimento implícito em Banco de Dados (KDD - Knowledge Discovery Database). O objetivo do trabalho foi avaliar o desempenho de Algoritmos Genéticos no processo de classificação de registros em Bancos de Dados (BD). O processo de classificação no contexto de Algoritmos Genéticos consiste na evolução de regras de associação que melhor caracterizem, através de sua acurácia e abrangência, um determinado grupo de registros do BD. O trabalho consistiu de 4 etapas principais: um estudo sobre a área de Knowledge Discovery Database (KDD); a definição de um modelo de AG aplicado à Mineração de Dados (Data Mining); a implementação de uma ferramenta (Rule-Evolver) de Mineração de Dados; e o estudo de casos. O estudo sobre a área de KDD envolveu todo o processo de descoberta de conhecimento útil em banco de dados: definição do problema; seleção dos dados; limpeza dos dados; pré-processamento dos dados; codificação dos dados; enriquecimento dos dados; mineração dos dados e a interpretação dos resultados. Em particular, o estudo destacou a fase de Mineração de Dados e os algoritmos e técnicas empregadas (Redes Neurais, Indução de regras, Modelos Estatísticos e Algoritmos Genéticos). Deste estudo resultou um survey sobre os principais projetos de pesquisa na área. A modelagem do Algoritmo Genético consistiu fundamentalmente na definição de uma representação dos cromossomas, da função de avaliação e dos operadores genéticos. Em mineração de dados por regras de associação é necessário considerar-se atributos quantitativos e categóricos. Atributos quantitativos representam variáveis contínuas (faixa de valores) e atributos categóricos variáveis discretas. Na representação definida, cada cromossoma representa uma regra e cada gene corresponde a um atributo do BD, que pode ser quantitativo ou categórico conforme a aplicação. A função de avaliação associa um valor numérico à regra encontrada, refletindo assim uma medida da qualidade desta solução. A Mineração de Dados por AG é um problema de otimização onde a função de avaliação deve apontar para as melhores regras de associação. A acurácia e a abrangência são medidas de desempenho e, em alguns casos, se mantém nulas durante parte da evolução. Assim, a função de avaliação deve ser uma medida que destaca cromossomas contendo regras promissoras em apresentar acurácia e abrangência diferentes de zero. Foram implementadas 10 funções de avaliação. Os operadores genéticos utilizados (crossover e mutação) buscam recombinar as cláusulas das regras, de modo a procurar obter novas regras com maior acurácia e abrangência dentre as já encontradas. Foram implementados e testados 4 operadores de cruzamento e 2 de mutação. A implementação de uma ferramenta de modelagem de AG aplicada à Mineração de Dados, denominada Rule-Evolver, avaliou o modelo proposto para o problema de classificação de registros. O Rule-Evolver analisa um Banco de Dados e extrai as regras de associação que melhor diferenciem um grupo de registros em relação a todos os registros do Banco de Dados. Suas características principais são: seleção de atributos do BD; informações estatísticas dos atributos; escolha de uma função de avaliação entre as 10 implementadas; escolha dos operadores genéticos; visualização gráfica de desempenho do sistema; e interpretação de regras. Um operador genético é escolhido a cada reprodução em função de uma taxa preestabelecida pelo usuário. Esta taxa pode permanecer fixa ou variar durante o processo evolutivo. As funções de avaliação também podem ser alteradas (acrescidas de uma recompensa) em função da abrangência e da acurácia da regra. O Rule- Evolver possui uma interface entre o BD e o AG, necessária para tor
This dissertation investigates the application of Genetic Algorithms (GAs) to the process of implicit knowledge discovery over databases (KDD - Knowledge Discovery Database). The objective of the work has been the assessment of the Genetic Algorithms (GA) performance in the classification process of database registers. In the context of Genetic Algorithms, this classification process consists in the evolution of association rules that characterise, through its accuracy and range, a particular group of database registers. This work has encompassed four main steps: a study over the area of Knowledge Discovery Databases; the GA model definition applied to Data Mining; the implementation of the Data Mining Rule Evolver; and the case studies. The study over the KDD area included the overall process of useful knowledge discovery; the problem definition; data organisation; data pre-processing; data encoding; data improvement; data mining; and results´ interpretation. Particularly, the investigation emphasied the data mining procedure, techniques and algorithms (neural Networks, rule Induction, Statistics Models and Genetic Algorithms). A survey over the mais research projects in this area was developed from this work. The Genetic Algorithm modelling encompassed fundamentally, the definition of the chromosome representation, the fitness evaluation function and the genetic operators. Quantitative and categorical attributes must be taken into account within data mining through association rules. Quantitative attribites represent continuous variables (range of values), whereas categorical attributes are discrete variable. In the representation employed in this work, each chromosome represents a rule and each gene corresponds to a database attribute, which can be quantitative or categorical, depending on the application. The evaluation function associates a numerical value to the discovered rule, reflecting, therefore, the fitness evaluation function should drive the process towards the best association rules. The accuracy and range are performance statistics and, in some cases, their values stay nil during part of the evolutionary process. Therefore, the fitness evaluation function should reward chromosomes containing promising rules, which present accuracy and range different of zero. Ten fitness evaluation functions have been implemented. The genetic operators used in this work, crossover and mutation, seek to recombine rules´clauses in such a way to achieve rules of more accuracy and broader range when comparing the ones already sampled. Four splicing operators and two mutation operators have been experimented. The GA modeling tool implementation applied to Data Mining called Rule Evolever, evaluated the proposed model to the problem of register classification. The Rule Evolver analyses the database and extracts association rules that can better differentiate a group of registers comparing to the overall database registers. Its main features are: database attributes selection; attributes statistical information; evaluation function selection among ten implemented ones; genetic operators selection; graphical visualization of the system performance; and rules interpretation. A particular genetic operator is selected at each reproduction step, according to a previously defined rate set by the user. This rate may be kept fix or may very along the evolutionary process. The evolutionary process. The evaluation functions may also be changed (a rewarding may be included) according to the rule´s range and accuracy. The Rule Evolver implements as interface between the database and the GA, endowing the KDD process and the Data Mining phase with flexibility. In order to optimise the rules´ search process and to achieve better quality rules, some evolutionary techniques have been implemented (linear rank and elitism), and different random initialisation methods have been used as well; global averag
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Alsalama, Ahmed. "A Hybrid Recommendation System Based on Association Rules." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1250.

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Recommendation systems are widely used in e-commerce applications. Theengine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of users and a small number of items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users.
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Books on the topic "Rules database"

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Paton, Norman W., and M. Howard Williams, eds. Rules in Database Systems. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3225-7.

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Geppert, Andreas, and Mikael Berndtsson, eds. Rules in Database Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63516-5.

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Sellis, Timos, ed. Rules in Database Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60365-4.

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Paton, Norman W., ed. Active Rules in Database Systems. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4419-8656-6.

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International Workshop on Rules in Database Systems (1st 1993 Edinburgh, Scotland). Rules in database systems: Proceedings of the 1st International Workshop on Rules in Database Systems, Edinburgh, Scotland, 30 August-1 September 1993. London: Springer-Verlag, 1994.

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Paton, Norman W. Rules in Database Systems: Proceedings of the 1st International Workshop on Rules in Database Systems, Edinburgh, Scotland, 30 August-1 September 1993. London: Springer London, 1994.

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Moran, Patrick. Capt uring distributed database design rules in an expert shell. London: University of East London, 1993.

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Moran, Patrick. Capt uring distributed database design rules in an expert shell. London: University of East London, 1993.

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Andreas, Geppert, and Berndtsson Mikael 1967-, eds. Rules in database systems: Third international workshop, RIDS '97, Skövde, Sweden, June 26-28, 1997 : proceedings. Berlin: Springer, 1997.

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Chisholm, Malcolm. How to build a business rules engine: Extending application functionality through metadata engineering. Amsterdam: Morgan Kaufmann, 2004.

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Book chapters on the topic "Rules database"

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Léonard, Michel. "Integrity rules." In Database Design Theory, 31–82. London: Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-11979-0_2.

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Foster, Elvis C., and Shripad V. Godbole. "Integrity Rules and Normalization." In Database Systems, 57–81. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0877-9_4.

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Foster, Elvis C., and Shripad Godbole. "Integrity Rules and Normalization." In Database Systems, 73–100. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1191-5_4.

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Pei, Jian. "Association Rules." In Encyclopedia of Database Systems, 182–84. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_26.

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Berndtsson, Mikael, and Jonas Mellin. "ECA Rules." In Encyclopedia of Database Systems, 1263–64. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_504.

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Berndtsson, Mikael, and Jonas Mellin. "ECA Rules." In Encyclopedia of Database Systems, 959–60. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_504.

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Pei, Jian. "Association Rules." In Encyclopedia of Database Systems, 140–42. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_26.

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Pei, Jian. "Association Rules." In Encyclopedia of Database Systems, 1–3. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_26-2.

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Berndtsson, Mikael, and Jonas Mellin. "ECA Rules." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_504-2.

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Embury, Suzanne M., and Peter M. D. Gray. "Database Internal Applications." In Active Rules in Database Systems, 339–66. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4419-8656-6_19.

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Conference papers on the topic "Rules database"

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Aiken, Alexander, Jennifer Widom, and Joseph M. Hellerstein. "Behavior of database production rules." In the 1992 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/130283.130296.

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"PREFERENCE RULES IN DATABASE QUERYING." In 9th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002389901190124.

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Fagin, Ronald, Benny Kimelfeld, Yunyao Li, Sriram Raghavan, and Shivakumar Vaithyanathan. "Rewrite rules for search database systems." In the 30th symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1989284.1989322.

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Motakis, Iakovos, and Carlo Zaniolo. "Temporal aggregation in active database rules." In the 1997 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/253260.253359.

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TANG, HONGXIA, ZHENG PEI, LIANGZHONG YI, and ZUNWEI ZHANG. "MINING FUZZY ASSOCIATION RULES FROM DATABASE." In Proceedings of the 4th International ISKE Conference on Intelligent Systems and Knowledge Engineering. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814295062_0038.

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Dong, Xiangjun, Shiju Shang, Jie Li, and He Jiang. "Mining Global Exceptional Rules in Multi-database." In 2009 International Forum on Information Technology and Applications (IFITA). IEEE, 2009. http://dx.doi.org/10.1109/ifita.2009.445.

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Duan, Qiaoling, Huiwen Fu, Dingrong Yuan, and Xiaomeng Huang. "Mining indirect association rules in multi-database." In 2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM). IEEE, 2012. http://dx.doi.org/10.1109/icssem.2012.6340824.

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Ma, Lijun. "The integrity rules and constraints of database." In Mechanical Engineering and Information Technology (EMEIT). IEEE, 2011. http://dx.doi.org/10.1109/emeit.2011.6023085.

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Ye, Feiyue, Mingxia Chen, and Jin Qian. "Mining Short Association Rules from Large Database." In 2009 Asia-Pacific Conference on Information Processing, APCIP. IEEE, 2009. http://dx.doi.org/10.1109/apcip.2009.98.

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Shang, Shiju, Xiangjun Dong, Runian Geng, and Long Zhao. "Mining Negative Association Rules in Multi-database." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.120.

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Reports on the topic "Rules database"

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Hanson, Eric N. Ariel Database Rule System Project. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada250443.

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Anderson, Andrew, and Mark Yacucci. Inventory and Statistical Characterization of Inorganic Soil Constituents in Illinois. Illinois Center for Transportation, June 2021. http://dx.doi.org/10.36501/0197-9191/21-006.

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This report presents a statistical analysis of the Regulated Substances Library (RSL) developed by the Illinois Department of Transportation. The RSL is comprised of surficial soil chemistry data obtained from rights-of-way subsurface soil sampling conducted for routine preliminary site investigations. The 3.7-million-record RSL database is compared with four independent studies of inorganic soil constituents of naturally occurring soils in Illinois. A selection of 22 inorganic soil analytes are examined in this study: Al, Sb, As, Ba, Be, Cd, Ca, Cr, Co, Cu, Fe, Pb, Mg, Mn, Hg, Ni, K, Se, Na, Tl, V, and Zn. RSL database summary statistics, mean, median, minimum, maximum, 5th percentile, and 95th percentile, are determined for Illinois counties and for recognized environmental concern, non-recognized environmental concern, and de minimis site contamination classifications. The RSL database at a 95% confidence level is compared with current and proposed thresholds for defining naturally occurring soil concentrations for the selected analytes. The revised thresholds proposed by Cahill in 2017 are predominantly larger than the current standards found in the Tiered Approach to Corrective Action Objectives rules and are in better agreement with observed distributions of soil concentrations for both naturally occurring and RSL soils. A notable exception is antimony (Sb), for which Cahill proposed a reduced threshold similar in magnitude to the median for many Illinois Department of Transportation districts.
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Chu, esley W. Enhancing Rules in Active Databases via Relaxation Techniques. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada346919.

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Decleir, Cyril, Mohand-Saïd Hacid, and Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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Arctur, David K., Emaan Anwar, Sharma Chakravarthy, Maria Cobb, and Miyi Chung. Implementation of a Rule-Based Framework for Managing Updates in an Object-Oriented VPF Database,. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada308276.

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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, Manlio De Domenico, Giuseppe Mangioni, Simone Righi, and Annamaria Simonazzi. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, January 2022. http://dx.doi.org/10.36687/inetwp173.

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In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this industry – the acceler-ated rise of electric vehicles – also occurred in 2020: while the COVID-19 pandemic largely halted most plants in the automotive value chain all over the world, at the reopen-ing, the tide is now running against internal combustion engine vehicles, at least in the an-nouncements and in some large investments planned in Europe, Asia and the US. The definition of the pre-pandemic situation is a very helpful starting point for the analysis of the possible repercussions of the technological and geo-political transition, which has been accelerated by the epidemic, on geographical clusters and sectorial special-isations of the main regions and countries. This paper analyses the trade networks emerg-ing in the past 25 years in a new analytical framework. In the economic literature on inter-national trade, the study of the automotive global value chains has been addressed by us-ing network analysis, focusing on the centrality of geographical regions and countries while largely overlooking the contribution of countries' bilateral trading in components and parts as structuring forces of the subnetwork of countries and their specific position in the overall trade network. The paper focuses on such subnetworks as meso-level structures emerging in trade network over the last 25 years. Using the Infomap multilayer clustering algorithm, we are able to identify clusters of countries and their specific trades in the automotive internation-al trade network and to highlight the relative importance of each cluster, the interconnec-tions between them, and the contribution of countries and of components and parts in the clusters. We draw the data from the UN Comtrade database of directed export and import flows of 30 automotive components and parts among 42 countries (accounting for 98% of world trade flows of those items). The paper highlights the changes that occurred over 25 years in the geography of the trade relations, with particular with regard to denser and more hierarchical network gener-ated by Germany’s trade relations within EU countries and by the US preferential trade agreements with Canada and Mexico, and the upsurge of China. With a similar overall va-riety of traded components and parts within the main clusters (dominated respectively by Germany, US and Japan-China), the Infomap multilayer analysis singles out which com-ponents and parts determined the relative positions of countries in the various clusters and the changes over time in the relative positions of countries and their specialisations in mul-tilateral trades. Connections between clusters increase over time, while the relative im-portance of the main clusters and of some individual countries change significantly. The focus on US and Mexico and on Germany and Central Eastern European countries (Czech Republic, Hungary, Poland, Slovakia) will drive the comparative analysis.
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Ruiz, Pablo, Craig Perry, Alejando Garcia, Magali Guichardot, Michael Foguer, Joseph Ingram, Michelle Prats, Carlos Pulido, Robert Shamblin, and Kevin Whelan. The Everglades National Park and Big Cypress National Preserve vegetation mapping project: Interim report—Northwest Coastal Everglades (Region 4), Everglades National Park (revised with costs). National Park Service, November 2020. http://dx.doi.org/10.36967/nrr-2279586.

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The Everglades National Park and Big Cypress National Preserve vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service’s (NPS) Vegetation Mapping Inventory Program (VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park and Big Cypress National Preserve prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within these two federally-managed units as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in park management, resource management, research, and monitoring. This mapping project covers an area of approximately 7,400 square kilometers (1.84 million acres [ac]) and consists of seven mapping regions: four regions in Everglades National Park, Regions 1–4, and three in Big Cypress National Preserve, Regions 5–7. The report focuses on the mapping effort associated with the Northwest Coastal Everglades (NWCE), Region 4 , in Everglades National Park. The NWCE encompasses a total area of 1,278 square kilometers (493.7 square miles [sq mi], or 315,955 ac) and is geographically located to the south of Big Cypress National Preserve, west of Shark River Slough (Region 1), and north of the Southwest Coastal Everglades (Region 3). Photo-interpretation was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed specifically for this project. Additionally, photo-interpreters categorized the absolute cover of cattail (Typha sp.) and any invasive species detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%). A total of 178 thematic classes were used to map the NWCE. The most common vegetation classes are Mixed Mangrove Forest-Mixed and Transitional Bayhead Shrubland. These two communities accounted for about 10%, each, of the mapping area. Other notable classes include Short Sawgrass Marsh-Dense (8.1% of the map area), Mixed Graminoid Freshwater Marsh (4.7% of the map area), and Black Mangrove Forest (4.5% of the map area). The NWCE vegetation map has a thematic class accuracy of 88.4% with a lower 90th Percentile Confidence Interval of 84.5%.
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Viswanathan, Meera, Jennifer Cook Middleton, Alison Stuebe, Nancy Berkman, Alison N. Goulding, Skyler McLaurin-Jiang, Andrea B. Dotson, et al. Maternal, Fetal, and Child Outcomes of Mental Health Treatments in Women: A Systematic Review of Perinatal Pharmacologic Interventions. Agency for Healthcare Research and Quality (AHRQ), April 2021. http://dx.doi.org/10.23970/ahrqepccer236.

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Background. Untreated maternal mental health disorders can have devastating sequelae for the mother and child. For women who are currently or planning to become pregnant or are breastfeeding, a critical question is whether the benefits of treating psychiatric illness with pharmacologic interventions outweigh the harms for mother and child. Methods. We conducted a systematic review to assess the benefits and harms of pharmacologic interventions compared with placebo, no treatment, or other pharmacologic interventions for pregnant and postpartum women with mental health disorders. We searched four databases and other sources for evidence available from inception through June 5, 2020 and surveilled the literature through March 2, 2021; dually screened the results; and analyzed eligible studies. We included studies of pregnant, postpartum, or reproductive-age women with a new or preexisting diagnosis of a mental health disorder treated with pharmacotherapy; we excluded psychotherapy. Eligible comparators included women with the disorder but no pharmacotherapy or women who discontinued the pharmacotherapy before pregnancy. Results. A total of 164 studies (168 articles) met eligibility criteria. Brexanolone for depression onset in the third trimester or in the postpartum period probably improves depressive symptoms at 30 days (least square mean difference in the Hamilton Rating Scale for Depression, -2.6; p=0.02; N=209) when compared with placebo. Sertraline for postpartum depression may improve response (calculated relative risk [RR], 2.24; 95% confidence interval [CI], 0.95 to 5.24; N=36), remission (calculated RR, 2.51; 95% CI, 0.94 to 6.70; N=36), and depressive symptoms (p-values ranging from 0.01 to 0.05) when compared with placebo. Discontinuing use of mood stabilizers during pregnancy may increase recurrence (adjusted hazard ratio [AHR], 2.2; 95% CI, 1.2 to 4.2; N=89) and reduce time to recurrence of mood disorders (2 vs. 28 weeks, AHR, 12.1; 95% CI, 1.6 to 91; N=26) for bipolar disorder when compared with continued use. Brexanolone for depression onset in the third trimester or in the postpartum period may increase the risk of sedation or somnolence, leading to dose interruption or reduction when compared with placebo (5% vs. 0%). More than 95 percent of studies reporting on harms were observational in design and unable to fully account for confounding. These studies suggested some associations between benzodiazepine exposure before conception and ectopic pregnancy; between specific antidepressants during pregnancy and adverse maternal outcomes such as postpartum hemorrhage, preeclampsia, and spontaneous abortion, and child outcomes such as respiratory issues, low Apgar scores, persistent pulmonary hypertension of the newborn, depression in children, and autism spectrum disorder; between quetiapine or olanzapine and gestational diabetes; and between benzodiazepine and neonatal intensive care admissions. Causality cannot be inferred from these studies. We found insufficient evidence on benefits and harms from comparative effectiveness studies, with one exception: one study suggested a higher risk of overall congenital anomalies (adjusted RR [ARR], 1.85; 95% CI, 1.23 to 2.78; N=2,608) and cardiac anomalies (ARR, 2.25; 95% CI, 1.17 to 4.34; N=2,608) for lithium compared with lamotrigine during first- trimester exposure. Conclusions. Few studies have been conducted in pregnant and postpartum women on the benefits of pharmacotherapy; many studies report on harms but are of low quality. The limited evidence available is consistent with some benefit, and some studies suggested increased adverse events. However, because these studies could not rule out underlying disease severity as the cause of the association, the causal link between the exposure and adverse events is unclear. Patients and clinicians need to make an informed, collaborative decision on treatment choices.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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