Relational Model Research Articles

Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data

BackgroundIntegrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings.ResultsLAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources.ConclusionsThe prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.

Association-Oriented Database Model — n-ary Associations

This paper provides a comparison between methods used to represent unary, binary and n-ary relations in the relational database (RDB) and object-oriented database (OODB) models and the association-oriented database (AODB) model proposed in terms of their unambiguity. We have elaborated upon the RDB, OODB and AODB models perceived from the perspective of the categories applied to define them. What has been discussed is a number of solutions assuming the form of schemes for the AODB model. We substantiate that those solutions have unequivocally met the conditions of relations of any chosen arity and multiplicity, at the same time keeping the notation concise, unambiguous and considerably expressive.

Software Professionals use Object Oriented data modeling instead of traditional relational data modeling

The purpose of this paper is to explain why object oriented data modeling is more popular than relational data modeling. A data model is a logic organization of the real world objects (entities), constraints on them, and the relationships among objects. Relational model is very simple since data in represented in the form of relations that are depicted by use of two-dimensional tables. Rows in the table represent records and Columns represent attributes of the entity. The basic concept in the relational model is that of a relation. In object-oriented model main construct is an object. As in relational model, there are relations similarly we have objects in OO data modeling. So first thing in OO model is to identify the objects for the systems. Examining the problem statement can do it. Other important task is to identify the various operations for these objects. It is easy to relate the objects to the real world entity. The object-oriented approach has proved to be especially fruitful in application areas, such as the design of geographical information systems which have a richly structured knowledge domain and are associated with multimedia databases. Relational data modeling is different from Object Oriented data modeling because it focuses solely on data while object oriented data models focuses on both the behavior and data aspects of your domain. OODBMS are faster than relational DBMS because data isn’t stored in relational rows and columns but as objects. Objects have a many to many relationship and are accessed by the use of pointers.

Бинарная реляционная модель данных

The paper is related to the problem of data integration in the Semantic Web and is a continuation of the previously published works, which was dedicated to the creation of mappings from the description logic (DL) into binary relational data model. In this paper, we define the binary relational data model, namely, its structure and algebra. This algebra contains classical relational algebra operations that do not increase arity of relations, modified classical relational algebra operations that increase arity, and a number of additional operations that allow interpreting certain concepts and roles of DL. The publication also discusses how to convert the n-ary relational data structure into a binary one. In this paper, the previously obtained research results are used, namely RM 2 data structure and mapping of basic concepts, roles, axioms of DL ALC and its extensions into the relational data model. Problems in programming 2017; 2: 96-105

SmartOntoSensor: Ontology for Semantic Interpretation of Smartphone Sensors Data for Context-Aware Applications

The integration of cheap and powerful sensors in smartphones has enabled the emergence of several context-aware applications and frameworks. However, the available smartphone context-aware frameworks are static because of using relational data models having predefined usage of sensory data. Importantly, the frameworks lack the soft integration of new data types and relationships that appear with the emergence of new smartphone sensors. Furthermore, sensors generate huge data that intensifies the problem of too much data and not enough knowledge. Smarting of smartphone sensory data is essential for advanced analytical processing, integration, inferencing, and interpretation by context-aware applications. In order to achieve this goal, novel smartphone sensors ontology is required for semantic modeling of smartphones and sensory data, which is the main contribution of this paper. This paper presents SmartOntoSensor, a lightweight mid-level ontology that has been developed using NeOn methodology and Content Ontology Design pattern. The ontology describes smartphone and sensors from different aspects including platforms, deployments, measurement capabilities and properties, observations, data fusion, and context modeling. SmartOntoSensor has been developed using Protégé and evaluated using OntoQA, SPARQL, and experimental study. The ontology is also tested by integrating into ModeChanger application that leverages SmartOntoSensor for automatic changing of smartphone modes according to the varying contexts. We have obtained promising results that advocate for the improved ontological design and applications of SmartOntoSensor.

Интеграция аксиоматики дескриптивных логик с реляционной моделью данных

The paper is a logical continuation of the previously published work, which was dedicated to the creation of mappings from the description logic into binary relational data model. On base of the previously created binary relational data structure we perform mappings of the ALC axiomatic into relational data model (RDM). The results of previous research namely data structure RM2, mappings of the basic ALC concepts were used in this paper.

A Mathematical Model for Comparing Memory Storage of Three Interval-Based Parametric Temporal Database Models

Interval-Based Parametric Temporal Database Model (IBPTDM) captures the historical changes of database object in single tuple. Such data model violates 1NF and it is difficult to be implemented on top of conventional Database Management Systems (DBMS). The reason behind that, IBPTDM cannot directly use relational storage structure or query evaluation technique that depends on atomic attribute values as well as it is unfixed attribute size. 1NF model with its features can be used to solve such challenge. Modeling time-varying data in 1NF model raise a question about memory storage efficiency and ease of use. A novel approach for representing temporal data in 1NF model and compare it with other main approaches in literature is the main goal of this research. To this end, a mathematical model for comparing a three different storage models is demonstrated to illustrate that the proposed model is more efficient than other approaches under certain conditions. The simulation results showed that the proposed model overcomes the needless redundancy of data, achieves saving in memory storage, and it is easy to be implemented in relational data model or to be adapted with a production systems that need to track temporal aspects of functioning database Systems.

Analysis of Typed Inclusion Dependencies with Null Values

Null values have become an urgent problem since the creation of the relational data model. The impact of the uncertainty affects all types of dependencies used in the design and operation of the database. This fully applies to the inclusion dependencies, which are the theoretical basis for referential integrity on the data. Attempts to solve this problem contain inaccuracy in the statement of the problem and its solution. The errors in formulation of the problem can be associated with the use in the definition of untyped inclusion dependencies, which leads to permutations of the attributes, although, the attributes in database technology are identified by name and not by their place. In addition, linking with the use of the inclusion dependencies of heterogeneous attributes, even of the same type, is a sign of lost functional dependencies and leads to interaction of inclusion dependencies and non-trivial functional dependencies. Inaccuracies in the solution of the problem are contained in the statements of axioms and the proof of their properties, including completeness. In this paper we propose an original solution of this problem only for typed inclusion dependencies in the presence of Null values: a new axiom system is proposed, its completeness and soundness are proved. On the basis of inference rules we developed an algorithm for the construction of a not surplus set of typed inclusion dependencies. The correctness of the algorithm is proved.

A New Method for Time-Series Big Data Effective Storage

Today, one of the main challenges of big data research is the processing of big time-series data. Moreover, time data analysis is of considerable importance, because previous trends are useful for predicting the future. Due to the considerable delay when the volume of the data increases, the presence of redundancy, and the innate lack of time-series structures, the traditional relational data model does not seem to be adequately able to analyze time data. Moreover, many traditional data structures do not support time operators, which results in an inefficient access to time data. Therefore, relational database management systems have difficulty in dealing with big data—it may require massively parallel software that runs on many servers. This has led us to implement Chronos Software, an in-memory background-based time database for key-value pairs; this software was implemented using C++ language. An independent design has been suggested through appropriately using temporal algorithms, parallelism algorithms, and methods of data storage in RAM. Our results indicate that the employment of RAM for storing the data and of the Timeline Index algorithm for getting access to the time background of the keys in Chronos translate into an increase of about 40%–90% in the efficiency as compared with other databases, such as MySQL and MongoDB.

Evaluation of Relational and NoSQL Approaches for Cohort Identification from Heterogeneous Data Sources in the National Sleep Research Resource

Patient cohort identification across heterogeneous data sources is a challenging task, which may involve a complicated process of data loading, harmonization and querying. Most existing cohort identification tools use a relational database model implemented in SQL for storing patient data. However, SQL databases have restrictions on the maximum number of columns in a table, which necessitates the breaking down of high dimensional data into multiple tables and as a consequence affects query performance. In this paper, we developed two NoSQL-based patient cohort query systems based on an existing SQL-based system for the cross-cohort query in the National Sleep Resource Research (NSRR). We used eight NSRR datasets in our experiment to evaluate the performance of the NoSQLbased and SQL-based systems in data loading, harmonization and query. Our experiment showed that NoSQL-based approaches outperformed the SQL-based and are rather promising for developing patient cohort query systems across heterogeneous data sources.

Intrusion detection in a trust-based recommendation system

In multi-agent systems, agents cooperate by asking other agent's opinions as part of their own decision making process. The goal of a recommendation system is to provide advice either on request or pro-actively. In this paper, we assume that the value of an advice from another agent is determined by the trust in that other agent. We present a trust-based model of collaboration and decision making in a multi-agent system. We assume however that agents may be dishonest (such an agent is called an intruder), resulting in advices that may be false. The goal of this paper is the detection of intruders, thereby minimising the damage that they can cause. We also present the underlying relational database model and use this to build a prototype. In our tests, we show that the more influential an agent is in the multi-agent system, measured as its CreditRank, the faster that agent will be unmasked would it be an intruder.

Design and Implementation of Movie Recommendation System Based on Knn Collaborative Filtering Algorithm

In the spread of information, how to quickly find one’s favorite movie in a large number of movies become a very important issue. Personalized recommendation system can play an important role especially when the user has no clear target movie. In this paper, we design and implement a movie recommendation system prototype combined with the actual needs of movie recommendation through researching of KNN algorithm and collaborative filtering algorithm. Then we give a detailed principle and architecture of JAVAEE system relational database model. Finally, the test results showed that the system has a good recommendation effect.

A FUZZY PROBABILISTIC RELATIONAL DATABASE MODEL AND ALGEBRA

This paper introduces a complete extended relational data base model based on probability theory and fuzzy theory, called FPRDB, for representing and handling vague and uncertain information of objects in the real world applications. FPRDB is built by first extending probabilistic triples with fuzzy sets, associating the probability of such fuzzy sets for expressing and computing uncertain degree of imprecise information. Then, schemas, fuzzy probabilistic relations, fuzzy probabilistic functional dependencies and algebraic operations are defined coherently and consistently for FPRDB. A set of the properties of the probabilistic relational algebraic operations in FPRDB is also formulated and proven.

Temporal Database Models Validation and Verification using Mapping Methodology

The majority of any temporal data model is claimed to be superset of the conventional relational data model as well as it has the ability to be represented in the main temporal data models in literature. In this paper, a complete and formal proof is provided in order to show that the A Tuple Timestamping Historical Relational (TTHR) temporal data model is a consistent extension of conventional relational model, and has the expressive power to be represented in other reference temporal data model and the modeling approaches in relational temporal data model.

Интеграция дескриптивных логик с операциями над ролями с реляционной моделью данных

The paper is a logical continuation of the previously published work, which was dedicated to the creation of mappings from the description logic into binary relational data model. Based on the previously created binary relational data structure we perform mappings of the ALC extension into relational data model (RDM). The results of previous research namely data structure RM2, mappings of the basic ALC concepts and its classical extensions into RDM were used in this paper.

Comdb2 bloomberg's highly available relational database system

Comdb2 is a distributed database system designed for geographical replication and high availability. In contrast with the latest trends in this field, Comdb2 offers full transactional support, a standard relational model, and the expressivity of SQL. Moreover, the system allows for rich stored procedures using a dialect of Lua. Comdb2 implements a serializable system in which reads from any node always return current values. Comdb2 provides transparent High Availability through built-in service discovery and sophisticated retry logic embedded in the standard API. In addition to the relational data model, Comdb2 implements queues for publisher-to-subscriber message delivery. Queues can be combined with table triggers for time-consistent log distribution, providing functionality commonly needed in modern OLTP. In this paper we give an overview of our last twelve years of work. We focus on the design choices that have made Comdb2 the primary database solution within our company, Bloomberg LP (BLP).

Preserving Data Privacy with Record Retrieval using Visual Cryptography and Encryption Techniques

Objectives: Secure data sharing using visual cryptography with selective retrieval upon key match is introduced in this paper. As an illustration, medical and student database is considered. Methods/Analysis: The original database is stored as relational database model. In shared data applications encryption is used to achieve privacy of sensitive (personal) data from unauthorized users. The work includes encryption, selective retrieval, retrieving records with matching markers, etc. The proposed system search records from queries in the encrypted domain itself. Findings: All application related operations like checking for some threshold value, searching for similar records across different users, etc. is included. Because of proposed system any leakage of information over retrieved set of documents is avoided. Existing searching algorithm requires O (n) comparisons (searching operations) at the server to evaluate if the database contains a chosen keyword. The communication overhead minimized between the user and server. The computation is minimized between server and user. It allows the multi-user to search over the encrypted data. Searching time is minimized by relational database management. From encrypted query server cannot, distinguish between documents, determine document contents, check for search keyword and extend beyond decrypted result. Novelty/Improvement: The algorithm is novel in the sense that uncorrelatedness among the different user's data is preserved, along with complete sharing.

An Improved Indexing Method for Xpath Queries

Today, the XML is used as data storage for complex data models like bioinformatics information. A bioinformatics system deals with large data sets and complex queries. Thus, it is necessary to have accessing methods for XML data. XPath is a method to quickly locate any information that we need in an XML (tree) data starting from the context node in a root node to subtrees. In this paper, we propose a system model to store XML data more efficiently and also an improved indexing method to support Xpath queries. In the system model, we integrated big data model with relational data model in order to get benefit from both of them. The new indexing method is an improvement of R-tree that helps Xpath queries run more efficiently in some axes. Our experiments showed that the proposed method gains better results for node queries compared to the R-tree in transformed XML data. Our method is intended to apply to phylogenetic queries of Treefam databases.

Modeling fuzzy relational database in HBase

With the increase of massive data, a large number of business applications began to seek effective and scalable frameworks for data storages and processing. Under this background, emerging technologies for big data, such as Hadoop- based systems that use scalable distributed storage system HBase, become available. Since most of business data nowadays are stored in relational databases, and information imprecision and uncertainty widely exist in real-world applications, there is an increasing willingness to manage large-scale fuzzy relational data in the Hadoop-based platform. This paper concentrates on fuzzy information modeling in HBase. In particular, we investigate the formal transformation from the fuzzy relational data model to the HBase model and develop a set of mapping rules to assist in the transformation process. In addition, we present a generic approach to transform the fuzzy relational algebra into the fuzzy HBase manipulation language.

Интеграция семейства расширенных дескриптивных логик с реляционной моделью данных

The paper is a logical continuation of the previously published work, which was dedicated to the creation of the data manipulation methods. Based on the previously created binary relational data structure we perform mappings of the ALC extension into relational data model (RDM). The results of previous research namely data structure RM2 and mappings of the basic ALC concepts into RDM was used in this paper.