Traditional Relational Model Research Articles

Leveraging Big Data through NoSQL Databases-Architecture Methodologies, and Strategic Applications

The rise of Big Data has necessitated the evolution of databases beyond traditional relational models, paving the way for NoSQL databases to manage unstructured and semi-structured data at scale. This paper explores the architecture, methodologies, and strategic applications of NoSQL databases in the Big Data ecosystem. We will examine key challenges, tools, and technologies supporting NoSQL adoption and discuss real-world use cases across industries. Additionally, we will outline general activation steps, dependencies, and the risks associated with deploying NoSQL databases in enterprise settings.

Evaluation analysis and promotion paths of regional green innovation vitality in China

PurposeThe purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.Design/methodology/approachThe traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.FindingsThe novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.Practical implicationsThe grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.Originality/valueA comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.

Multi-channel hypergraph topic neural network for clinical treatment pattern mining

Recently, increasing attention has been paid to mining clinical treatment patterns from electronic medical records (EMRs), which provide physicians with explicit knowledge to guide the patient’s treatment. However, the current work mainly focuses on the topic probability models. These shallow models fail to consider the complex high-order correlations. Hypergraphs provide a natural way to establish complex high-order correlations, but their potential to discover the semantics of latent topic has not been fully explored. In this paper, we integrate the topic model in hypergraph learning and propose a multi-channel hypergraph topic neural network (C3-HGTNN) to discover latent topic treatment patterns with learning high-order correlations. Specifically, the hypergraph network is constructed based on the interactions in the treatment traces, which describe the latent high-order correlations. Then, based on the heterogeneity of the nodes in the hypergraph, we generate multiple channels and perform a convolution operation on each channel to fully encode the hypergraph. Finally, we develop an extensive representation of the treatment trace and activity by combining the embeddings from various channels and generating relevant topic distributions to identify latent treatment patterns. Also, we demonstrate that our model is a form of deep inference for traditional relational topic models, filling the gap in learning high-order correlation in traditional topic models. Extensive experiments on our dataset and public dataset show the effectiveness and superiority of our model compared to the competitive baselines.

Effects of Aerobic Training on Cardiopulmonary Function Based on Multiple Linear Regression Analysis.

In order to study the effect of aerobic training exercise on cardiopulmonary function of the human body, in this study, multiple linear regression based on the particle swarm optimization cardiopulmonary function test method of constructing the sports cardiopulmonary function test model is used. The traditional multiple linear regression after 41 iteration achieves convergence, and after the particle swarm optimization, about 25 times, convergence is achieved. Moreover, the convergence error of pSO is less than that of traditional multiple linear regression algorithm, which verifies the effectiveness of PSO. This method can effectively detect cardiopulmonary function of athletes before and after aerobic training, and the modeling accuracy is high, and the detection performance of cardiopulmonary function of aerobic training is better than the traditional relational model algorithm, which provides a new way for cardiopulmonary model detection of the human body.

Graph Modeling for Efficient Retrieval of Power Network Model Change History

Power grids are constantly evolving, and data changes are increasing. Operational technology (OT) is controlled by IT technologies in smart grids, where changes in the physical world impose changes in the software data model, as well as the continuous generation of data points, resulting in time series datasets. The increased need for processing large amounts of data combined with requirements to maintain and increase overall performances has created a significant challenge for traditional database solutions and relational database models. The main idea of this paper was to find and propose a graph model that will allow the retrieval of historical connectivity in a reduced time complexity. Furthermore, the research question was addressed by evaluating three different approaches where the results provide a foundation for the proposed design guidelines related to optimizing graph-based databases for a modern smart grid system. The results of the experiments demonstrated reduced time complexities from 3 to 5 times depending on the typical industry usage patterns and the selected graph model. This suggests that the design decision may severely affect the outcome for given smart grid use cases when using historical features in OT technologies. Therefore, the main contribution of the research is the proposed guidelines on how to design an optimal graph model that satisfies the described smart grid requirements.

Major Roles of Technology and Analytics together towards Advancement in Clinical Trials

For hassle-free management and reduce this errors tour booking 75% as compared to manual methods visual analytics is most sought-after technique in clinical trials for data presentation it evaluates large number of data and makes it easier to review dimensional database model and traditional relational database model explode in self-service mode and can deliver results with Precision and efficiency the graphs charts are few of the examples the promising future looking at vigorously changing market and technologies health care system adopting the good digital Moto for reducing cost and accelerate authorization process. The terabytes of the machineries get involved and management is looking after the easier on confidence systems to work on with Data Analytics the safety and delivery system of Healthcare is improving gradually with clinical professionals leveraging the information technology. The EHR is employed to nullify management in patient care for hassle-free management and reduce this errors to whooping 75% as compared to manual methods.

Design of a New Distributed NoSQL Database with Distributed Hash Tables

Abstract Databases play a fundamental role in today’s world, being used by most companies, especially those that offer services through the Internet. Today there is a wide variety of database models, each adapted for use according to the specific requirements of each application. Traditionally, the relational models with centralized architectures have been used mostly due to their simplicity and general-purpose query language, which made relational systems suitable for almost any application. However, with the growth of the Internet in recent decades, both in the number of users and in the amount of information, those centralized models began to suffer availability and scalability issues. To address those issues, the use of decentralized architectures and alternative database models began to arise, eventually replacing relational databases and centralized architectures when the requirements on availability and scalability are high. Those database models alternative to the traditional relational model are grouped under the name of NoSQL (Not only Structured Query Language). In this article, we present a NoSQL database developed as an end of degree work, with a flexible data model based on documents and a fully decentralized architecture based on the Gossip protocol for node discovery and a distributed hash table, in particular the rendezvous hashing algorithm, used to distribute and replicate the data across all the nodes. The main goals of the system are to achieve high availability (the data should be almost always accessible) and high scalability (the system should be able to scale by increasing the number of nodes to increase its capacity both on data and number of users). High availability is achieved thanks to the replication of the data, while high scalability is achieved by its decentralized architecture, which allows multiple entry points from the requests, and the data distribution, effectively increasing the database capacity by increasing the number of nodes.

RETRACTED ARTICLE: Multimedia Imaging Model of Information System Based on Self-Organizing Capsule Neural Network and Game Theory

With the advent of multimedia technology, the application fields of computers are further broadened, such as computers widely used in video conferencing, video telephony, shopping mall shopping guides, tour guides, computer-aided integrated manufacturing systems, geographic information systems, computer-aided teaching, etc., with new The emergence of application fields and the increasing demands of people for complex data management are increasing, and the requirements for database management systems are increasing. In general, numbers and characters are formatted data, and existing database technologies, especially relational database technology, can be used for good access management. However, text, sound, and images are unformatted data, so traditional relational models cannot be described and processed. To this end, people began to research multimedia database technology in order to manage multimedia data. In this paper, we proposed a multimedia model of management system based on self-organizing capsule neural network and game theory. Experimental results show that the proposed method has higher efficiency and robustness.

Dime lo que escuchas y te diré quién eres. Representaciones de la mujer en las 100 canciones de reggaetón más populares en 2018

The purpose of this work is to analyze and characterize the role of women in the representations in the videos, lyrics, and the place of the women performers within the reggaeton rhythm. One hundred videos and lyrics of most popular songs in 2018 are analyzed; the detailed observation with registration is used for the analysis of the videos and for the lyrics the thematic analysis is used, which consists of 6 phases: familiarization with the data, generation of categories or initial codes, search of topics, review of topics, definition and denomination of topics and final report. The results show that women are sexualized, objectified, described through traditional and negative relational models, which makes reggaeton a space in which the model of the identity of enjoyment is transmitted.

Performance Modeling of Database Systems: a Survey

This paper presents a systematic survey of the existing database system performance evaluation models based on the queueing theory. The continuous evolution of the methodologies developed is classified according to the mathematical modeling language used. This survey covers formal models – from queueing systems and queueing networks to queueing Petri nets. Some fundamentals of the queueing system theory are presented and queueing system models are classified according to service time distribution. The paper introduces queueing networks and considers several classification criteria applicable to such models. This survey distinguishes methodologies, which evaluate database performance at the integrated system level. Finally, queueing Petri nets are introduced, which combine modeling power of queueing networks and Petri nets. Two performance models within this formalism are investigated. We find that an insufficient amount of research effort is directed into the area of NoSQL data stores. Vast majority of models developed focus on traditional relational models. These models should be adapted to evaluate performance of non-relational data stores.

A new grey relational model based on discrete Fourier transform and its application on Chinese marine economic

Purpose The traditional grey relational models directly describe the behavioural characteristics of the systems based on the sample point connections. Few grey relational models can measure the dynamic periodic fluctuation rules of the objects, and most of these models do not have affinities, which results in instabilities of the relational results because of sequence translation. The paper aims to discuss these issues. Design/methodology/approach Fourier transform functions are used to fit the system behaviour curves, redefine the area difference between the curves and construct a grey relational model based on discrete Fourier transform (DFTGRA). Findings To verify its validity, feasibility and superiority, DFTGRA is applied to research on the correlation between macroeconomic growth and marine economic growth in China coastal areas. It is proved that DFTGRA has the superior properties of affinity, symmetry, uniqueness, etc., and wide applicability. Originality/value DFTGRA can not only be applied to equidistant and equal time sequences but also be adopted for non-equidistant and unequal time sequences. DFTGRA can measure both the global relational degree and the dynamic correlation of the variable cyclical fluctuation between sequences.

Bayesian Nonparametric Relational Topic Model through Dependent Gamma Processes

Traditional relational topic models provide a successful way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, and link prediction, could benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known a priori, which is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model using stochastic processes instead of fixed-dimensional probability distributions in this paper. Specifically, each document is assigned a Gamma process, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different Gamma process from the global Gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.

Big graph search: challenges and techniques

On one hand, compared with traditional relational and XML models, graphs have more expressive power and are widely used today. On the other hand, various applications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. To show this, we first introduce the application of graph search in various scenarios. We then formalize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.

Relational Operations and Uncertainty Measure in Rough Relational Database

The traditional relational database model RDM is not effective for dealing with imprecise and uncertain data as it deals with precise and unambiguous data. Hence, Beaubouef et al. proposed the rough relational database model RRDM for the management of uncertainty in relational databases. Beaubouef et al. defined the corresponding rough relational operators in rough relational databases as in ordinary relational databases. And to give an effective measure of uncertainty in rough relational databases, they defined the rough relation entropy. In this paper, we further discuss the issues of relational operations and uncertainty measure in rough relational databases. We give some new definitions for rough relational operators and rough relation entropy in rough relational databases. Furthermore, we discuss the basic properties of rough relational operators and rough relation entropy, as well as the connections between rough relational operators and rough relation entropy.

A novel grey relational model based on grey number sequences

PurposeThis paper aims to construct a novel grey relational model based on grey number sequences and to solve the problems which exist in traditional grey relational models, in which the uncertain information cannot be described.Design/methodology/approachBased on the definition of traditional grey relational models, considering the limited information and knowledge, the algorithm of grey numbers was combined with the grey relational model. A general formula of grey operations and grey distance is defined. A novel grey relational model based on grey number sequences, named grey geometrical relational model, is proposed according to the definition of grey distance. Finally, several properties including parallel, multiple and order‐keeping about the proposed model are discussed.FindingsThe results show that the novel grey relational model satisfies the properties properly. It is useful to calculate the relational degree of two different grey number sequences. And the process of calculating is easier than traditional grey relational models.Practical implicationsThe method exposed in the paper can be used to calculate every two sequences. The method can also be used to rank sequences of more than two.Originality/valueThe paper succeeds in constructing a novel grey relational model. The properties of novel model are studied and it is a new development in grey systems theory undoubtedly.

Query evaluation and optimization in the semantic web

AbstractWe address the problem of answering Web ontology queries efficiently. An ontology is formalized as adeductive ontology base(DOB), a deductive database that comprises the ontology's inference axioms and facts. A cost-based query optimization technique for DOB is presented. A hybrid cost model is proposed to estimate the cost and cardinality of basic and inferred facts. Cardinality and cost of inferred facts are estimated using an adaptive sampling technique, while techniques of traditional relational cost models are used for estimating the cost of basic facts and conjunctive ontology queries. Finally, we implement a dynamic-programming optimization algorithm to identify query evaluation plans that minimize the number of intermediate inferred facts. We modeled a subset of the Web ontology language Lite as a DOB and performed an experimental study to analyze the predictive capacity of our cost model and the benefits of the query optimization technique. Our study has been conducted over synthetic and real-world Web ontology language ontologies and shows that the techniques are accurate and improve query performance.

A dynamic information schema for supporting product lifecycle management

Abstract The globalization of competition and changing customer requirements are the emerging phenomenon of today's manufacturing environment. Nowadays, manufacturers need useful information prior to making important decisions based on their experience and knowledge. However, the lack of seamless data transformation between database and knowledge based system hinders design practitioners from responding rapidly to the changes in the complex and dynamic product development environment. This paper proposes a dynamic product information schema which is characterized by its ability to provide designers with a product data exchange standard, thus, enhancing the responsiveness of product development. The approach is based on the modification of the XML standard which is assimilated with object technology to form a cross-platform protocol that enables enterprises to update their data constantly. In particular, data from the traditional relational database model can be transformed to a format which is acceptable in a knowledge base system. To validate the feasibility of the proposed schema, a trial run has been conducted in a local company based on the suggested approach.

Lesbian FLEX-ibility

Lesbian ex-lover relationships have frequently been noted as unique phenomena, yet they have received little empirical or theoretical attention. In this article, the available literature on lesbian ex-lovers is examined, as are possible factors and/or motivations among lesbians for maintaining ties with ex-lovers, and especially for constructing close friend and/or family connections among lesbian ex-lovers-what the author calls FLEX relationships. Three main types of factors/motivations are identified: (a) those that reflect a problematic developmental perspective, (b) contextual influences (such as lesbians’ oppression as lesbians and women, socialization as girls and women, and the tendency for same-sex friendships), and (c) constructive challenges to traditional relational forms, family patterns and/or adult development models that offer alternative models for adult lives and relationships. Finally, the author considers the importance of attending to the ex-lover relational form and future directions this attention might take, closing with consideration of the implications for all of us-whatever our sexualities and gender identities-that may be drawn from telling constructive stories of friend/family relationships among lesbian ex-lovers.

Optimizing top-k selection queries over multimedia repositories

Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A query on these attributes will typically, request not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, which indicates how well the object matches the selection condition (ranking). Furthermore, unlike in the relational model, users may just want the k top-ranked objects for their selection queries for a relatively small k. In addition to the differences in the query model, another peculiarity of multimedia repositories is that they may allow access to the attributes of each object only through indexes. We investigate how to optimize the processing of top-k selection queries over multimedia repositories. The access characteristics of the repositories and the above query model lead to novel issues in query optimization. In particular, the choice of the indexes used to search the repository strongly influences the cost of processing the filtering condition. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we present an efficient algorithm that solves the problem optimally with respect to our cost model and execution space when the predicates in the query are independent. We also show that the problem of optimizing top-k selection queries can be viewed, in many cases, as that of evaluating more traditional selection conditions. Thus, both problems can be viewed together as an extended filtering problem to which techniques of query processing and optimization may be adapted.

Dependencies and generalized relations

A combinatorial, data-oriented approach is suggested to study generalized relational databases (GRDs) and generalized functional dependencies (GFDs). GRDs, as defined here, extend the traditional relational model by Codd. Also, certain aspects of nonfirst-normal-form databases and databases with null values can be captured in the setting of GRDs. We show that, after a suitable transformation, GFDs can be described in the framework of traditional functional dependencies (FDs). This leads to a convenient description of GFDs in terms of propositional logic.