Database Schema Research Articles

ПОРІВНЯННЯ SQL І NOSQL БАЗ ДАНИХ НА ПРИКЛАДІ ПРОЕКТУВАННЯ АФФІЛЕЙТ РЕПОРТ СИСТЕМ

Nowadays, due to the rapid development of social networks and the blogger culture, there is a tendency to use affiliate systems to promote their product. The Affiliate Reporting Service is a service offered to customers who want to analyze the affiliate systems' performance data. These systems are used by business executives and business owners to analyze ecommerce data and convert it into profit/expense data to adjust their business path further. This type of service includes data storage for all affiliates, data archive management, conversion of advertising campaigns, trend tracking, and more. These systems are based on large data sets that need to be stored correctly and safely stored and processed using database management systems. There are two major direction: SQL and NoSQL, relational and non-relational databases. The differences between them are how they are designed, what types of data they support, how they store information, how they support information security. A rigid relational database schema helps maintain the security and integrity of data when stored and modified. The lack of a rigid database schema and the need to change the entire structure of the table with a minimal change in the storage concept, make it easier to work with non-relational databases and subsequently support them, but it also has its disadvantages. It is important to understand that the tasks are different and the methods for solving them are also different; Choosing a database and database management system is a complex multi-parameter task and is one of the most important steps in developing such applications. Properly selected database will reduce the monetary and time costs associated with the development of the software, as well as facilitate system support in the future. The purpose of the article is to compare relational and non-relational databases by different metrics used in Affiliate Reporting Systems Design. In particular, a performance analysis was conducted on the performance of various operations, on the basis of which conclusions were drawn about the use of a particular database.

DBTree: Very large phylogenies in portable databases

Abstract Growing numbers of large phylogenetic syntheses are being published. Sometimes as part of a hypothesis testing framework, sometimes to present novel methods of phylogenetic inference, and sometimes as a snapshot of the diversity within a database. Commonly used methods to reuse these trees in scripting environments have their limitations. I present a toolkit that transforms data presented in the most commonly used format for such trees into a database schema that facilitates quick topological queries. Specifically, the need for recursive traversal commonly presented by schemata based on adjacency lists is largely obviated. This is accomplished by computing pre‐ and post‐order indexes and node heights on the topology as it is being ingested. The resulting toolkit provides several command line tools to do the transformation and to extract subtrees from the resulting database files. In addition, reusable library code with object–relational mappings for programmatic access is provided. To demonstrate the utility of the general approach I also provide database files for trees published by Open Tree of Life, Greengenes, D‐PLACE, PhyloTree, the NCBI taxonomy and a recent estimate of plant phylogeny. The database files that the toolkit produces are highly portable (either as SQLite or tabular text) and can readily be queried, for example, in the R environment. Programming languages with mature frameworks for object‐relational mapping and phylogenetic tree analysis, such as Python, can use these facilities to make much larger phylogenies conveniently accessible to researcher programmers.

Type theoretical databases

Abstract We show how the display-map category of finite (symmetric) simplicial complexes can be seen as representing the totality of database schemas and instances in a single mathematical structure. We give a sound interpretation of a certain dependent type theory in this model and show how it allows for the syntactic specification of schemas and instances and the manipulation of the same with the usual type-theoretic operations.

Application of a Novel Subject Classification Scheme for a Bibliographic Database Using a Data-Driven Correspondence

A novel subject classification scheme should often be applied to a preclassified bibliographic database for the research evaluation task. Generally, adopting a new subject classification scheme is labor intensive and time consuming, and an effective and efficient approach is necessary. Hence, we propose an approach to apply a new subject classification scheme for a subject-classified database using a data-driven correspondence between the new and present ones. In this paper, we define a subject classification model of the bibliographic database comprising a topological space. Then, we show our approach based on this model, wherein forming a compact topological space is required for a novel subject classification scheme. To form the space, a correspondence between two subject classification schemes using a research project database is utilized as data. As a case study, we applied our approach to a practical example. It is a tool used as world proprietary benchmarking for research evaluation based on a citation database. We tried to add a novel subject classification of a research project database.

On Quasi Cycles in Hypergraph Databases

The notion of hypergraph cyclicity is important in numerous fields of application of hypergraph theory in computer science and relational database theory. The database scheme and query can be represented as a hypergraph. The database scheme (or query) has a cycle if the corresponding hypergraph has a cycle. An Acyclic database has several desired computational properties such as making query optimization easier and can be recognized in linear time. In this paper, we introduce a new type of cyclicity in hypergraphs via the notions of Quasi $\alpha $ -cycle(s) and the set of $\alpha $ -nodes in hypergraphs, which are based on the existence of an $\alpha $ –cycle(s). Then, it is proved that a hypergraph is acyclic if and only if it does not contain any $\alpha $ -nodes. Moreover, a polynomial-time algorithm is proposed to detect the set of $\alpha $ -nodes based on the existence of Quasi $\alpha $ -cycle(s), or otherwise claims the acyclicity of the hypergraph. Finally, a systematic discussion is given to show how to use the detected set of $\alpha $ -nodes to convert the cyclic hypergraph into acyclic one if the conversion is possible. The acyclic database and acyclic query enjoy time and/or space-efficient access paths for answering a query.

Uncertainty Modeling of Object-Oriented Biomedical Information in HBase

While object-oriented databases (OODBs) are known to be rich in functionality, HBase database, which is a distributed and scalable big data store, as well as uncertain databases have recently gained a lot of attention in the database community. This paper presents a methodology for handling an important step of knowledge integrations and migrations. In particular, a formal approach for reengineering fuzzy object-oriented databases in HBase is firstly developed. The reengineering approach is based on the technique of rule-based schema mapping, which defines a set of transformation rules involved in the process of schema transformations for mapping a fuzzy object-oriented database schema into a fuzzy HBase database schema. In addition, a formal approach to map the fuzzy object-oriented algebra into fuzzy HBase algebra is proposed. On this basis, we complement the work with a comprehensive set of experiments to show the efficiency of our proposed approach in terms of query time and scalability metrics.

Agent data merging

Abstract The present article deals with data collection in a given field using the agent-based technologies from various information sources of the Internet with the aim to ob-tain reliable and up-to-date data. The agent-based approach is illustrated by the data collection on the nuclear power plants operating all over the world. Three open information sources have been selected for data extraction. The information sources concerned have been analyzed and the features of data provision structure identified. In the course of the present work the following tools for the develop-ment of the software agents have been described: the browser control for human behavior simulation, HTML markup analysis using the XPath query language and data extraction from PDF-documents using regular expressions. Above all, the article considers the software architecture and the database scheme. In the re-sult of the software operation, data regarding 789 nuclear power plants has been obtained.

ФУНКЦИОНАЛЬНАЯ СВЯЗЬ, РЕКВИЗИТЫ И ПОКАЗАТЕЛИ КАК СРЕДСТВА РАЗРАБОТКИ СЕМАНТИЧЕСКИ КОРРЕКТНЫХ СХЕМ БАЗ ДАННЫХ

The paper considers an ambiguous representation of the subject area reality in data modelling in terms of the relation scheme. There were some attempts to rectify the relational model drawbacks by determining functional dependences among the relation attributes. Those attempts did not tackle the issue completely due to the specific nature of determining functional dependences during standardization of database scheme. The standardization allows eliminating an anomaly of operation updating caused by incorrect models of the subject area associated with the lack of semantic information in the relation model. To improve the representation process of semantic information, which is applied in designing of subject area, the authors suggest using such terms as essential elements and rating. These terms had been scrutinized ever since the hard-copy document workflow, when they were applied to formalize the document contents. To this purpose, the article describes the main components of the relational model in terms of essential elements and rating. For accomplishing aggregation and generalization operations used in developing the relational model and based on the description, the paper identifies formal criteria enabling to reduce the subjective factor while designing a conceptual database scheme.

Seamless interoperability in logistics by ontology alignment

This paper is dedicated to applying ontology alignment systems to the heterogeneity problem in logistics. The primary motivation for doing so is to enable interoperability among different IT systems in logistics, all with their own database scheme. We first analyze different standards for logistics interoperability, which are implemented by XML schema definition (XSD) or Electronic Data Interchange (EDI) with implicit and inconsistent semantics across these open standards. To analyze the applicability of ontology alignment, we create two ontologies from two well-known standards that are rich in terms of semantic relations between entities. These ontologies are subjected to the state-of-the-art alignment systems, applying matching with and without logistics background knowledge. Our experimental analyses show that the alignment using background knowledge with some annotations finds better mappings between the given logistics standards and is thus applicable to real-world situations under particular conditions.

Processing Archive Information in Digital University

The article discusses methods of processing and storing data archive used in the digital university. Disadvantages of these methods are found. As a result, a fundamentally new method of processing and storing information archive in a constantly changing scheme database is proposed. This method uses mivar technologies. The multidimensional space structure has been developed to store the data archive. This multidimensional space describes the temporal relational model. For processing data, archive is proposed scheme for selecting the subspace and converting it into relations. A method of transformation of relational databases into multidimensional mivar space for efficient execution of operations on temporal data with changing structure is proposed. The transition to a multidimensional space allows us to describe the process of changing temporal data and their structure in a unified way. As a result, the time required to adapt the database schema and the redundancy of information storage are reduced. The results of this work are used in the human resource management database of BMSTU.

ER Model Partitioning: Towards Trustworthy Automated Systems Development

In database development, a conceptual model is created, in the form of an Entity-relationship (ER) model, and transformed to a relational database schema (RDS) to create the database. However, some important information represented on the ER model may not be transformed and represented on the RDS. This situation causes a loss of information during the transformation process. With a view to preserving information, in our previous study, we standardized the transformation process as a one-to-one and onto mapping from the ER model to the RDS. For this purpose, we modified the ER model and the transformation algorithm resolving some deficiencies existed in them. Since the mapping was established using a few real-world cases as a basis and for verification purposes, a formal-proof is necessary to validate the work. Thus, the ongoing research aiming to create a proof will show how a given ER model can be partitioned into a unique set of segments and use it to represent the ER model itself. How the findings can be used to complete the proof in the future will also be explained. Significance of the research on automating database development, teaching conceptual modeling, and using formal methods will also be discussed.

Tripal EUtils: a Tripal module to increase exchange and reuse of genome assembly metadata

Data and metadata interoperability between data storage systems is a critical component of the FAIR data principles. Programmatic and consistent means of reconciling metadata models between databases promote data exchange and thus increases its access to the scientific community. This process requires (i) metadata mapping between the models and (ii) software to perform the mapping. Here, we describe our efforts to map metadata associated with genome assemblies between the National Center for Biotechnology Information (NCBI) data resources and the Chado biological database schema. We present mappings for multiple NCBI data structures and introduce a Tripal software module, Tripal EUtils, to pull metadata from NCBI into a Tripal/Chado database. We discuss potential mapping challenges and solutions and provide suggestions for future development to further increase interoperability between these platforms. Database URL: https://github.com/NAL-i5K/tripal_eutils.

Master Data Management Planning: A Case Study of Flight Information System at PT Angkasa Pura I (Persero)

The flight information system used for flight data management at PT Angkasa Pura I (Persero) uses several applications that have different features. This results in inconsistent values that affect data quality due to absence of single master data source. Therefore, this study aims to provide recommendations to PT Angkasa Pura I (Persero) regarding the planning of master data management for flight information system according to its business processes. Master data management planning is carried out using the key user interview method, analyzing the database schema of each application, analyzing IT policy documents, and using the framework of the Data Management Body of Knowledge (DMBOK). The research succeeded in producing the needs of master data management at PT Angkasa Pura I (Persero) using collaborative methods and transaction hub architecture patterns. The results of this master data management plan are expected to help PT Angkasa Pura I (Persero) in managing and managing flight data so that there are no more inconsistent data between applications and can improve data quality.

LPWAN Based IoT Surveillance System for Outdoor Fire Detection

Many fire situations have represented the loss of lives and material costs due to the lack of early fire detection through smoke or gas sensing, which can become complex and critical. Meanwhile, engineers worldwide develop and test multiple systems for smoke and gas detection, commonly based on sensor networks, digital image processing, or computer vision. Furthermore, the detection system must work thoroughly with alarms and warnings that aware of a risk situation for prompt evacuation of the population in the surroundings based on a reliable data network topology with adequate device deployments that will let us know the moment a fire outbreak. This paper presents a low-cost Internet of Things (IoT) prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN), focused on the accuracy in the temperature and gas measurement at the moment a fire starts. For its achievement, we integrated wireless components, development boards, and electronic devices, following the management of information updates through a database schema for the alarm settings based on the data gathered from the sensors.

ER Model Partitioning: Towards Trustworthy Automated Systems Development

In database development, a conceptual model is created, in the form of an Entity-relationship(ER) model, and transformed to a relational database schema (RDS) to create the database. However, some important information represented on the ER model may not be transformed and represented on the RDS. This situation causes a loss of information during the transformation process. With a view to preserving information, in our previous study, we standardized the transformation process as a one-to-one and onto mapping from the ER model to the RDS. For this purpose, we modified the ER model and the transformation algorithm resolving some deficiencies existed in them. Since the mapping was established using a few real-world cases as a basis and for verification purposes, a formal-proof is necessary to validate the work. Thus, the ongoing research aiming to create a proof will show how a given ER model can be partitioned into a unique set of segments and use it to represent the ER model itself. How the findings can be used to complete the proof in the future will also be explained. Significance of the research on automating database development, teaching conceptual modeling, and using formal methods will also be discussed.

Conceptual Modeling Education as a “Design Problem”

This article frames Conceptual Modeling education as a design problem, in the sense of the Design Science research framework, motivated by student preconceptions and oversimplifications causing a gap between how the discipline is perceived at the bachelor level and the holistic understanding of model value that is required for research work. The treatment to this design problem must comprise teaching approaches and artifacts capable of positioning Conceptual Modeling as a standalone discipline having a value proposition for any application domain, rather than a technique subordinated to other disciplines. The underpinning thesis is that modeling languages should be primarily understood as purposeful knowledge schemas that can be subjected to agile adaptations in support of model-driven systems or knowledge processes, by analogy to how a database schema is evolved in response to changing requirements of a data-driven system or data analytics needs. This thesis is supported by enablers provided by the Open Models Laboratory and the Agile Modeling Method Engineering framework – resources that support the development of treatments to the design problem framed by the article.

Review of Interoperability Practices for Enterprise Information System (EIS) in Public Sector

Interoperable information system is crucial to enable an organization to provide good services to users. It can give a high impact and good return to government agencies, particularly in terms of efficiency in managing information system as well as making it time and cost effective. However, the implementation and work processes involving the interoperability of the public sector information system are still under focused. The major issues identified are concerning on difficulties in the implementation of integration between legacy systems or new systems; Recurring transactions or transactions in various systems, especially those involving legacy systems or new systems; Differences in database platforms; Difficulty in implementing the system redesign process; and no standard use of meta data, data model and complete database schema that can be used as reference. They hinder the implementation of good governance and contribute to ineffective decision-making process in the public sector. Hence, a more detailed and comprehensive study should be carried out to identify the critical success factors that contribute to the interoperability of the enterprise information system in the public sector. The method of the study is qualitative. The instruments for data collection include document analysis. The finding reveals that interoperability factors and elements can be classified according to the levels of interoperability i.e. (i) Organization; (ii) Technical; and (iii) Semantics. They can then be used as the basis for the development of reference for developing comprehensive guidelines based on the success factors and elements outlined. It is hoped that these factors could contribute to the development of enterprise information systems in the public sector which emphasizes the concept of interoperability.

The use of modeling tools at the stages of database design

The article discusses the possibilities of using modeling tools at the stages of database design. Database design is one of the most complex and crucial tasks associated with the creation of an automated information system. As a result of solving this problem, the content of the database should be determined, an effective way for organizing data for all its future users, and data management tools. The main goal of this stage is to develop such a database scheme that it includes the necessary and sufficient data on information objects, their properties and relationships in accordance with the objects of the subject area and the processes of their transformation. The authors present the results of using modeling tools to solve the problems of the database design stage. Creating models is considered from the point of view of three-circuit architecture, the main principle of which is abstraction. To display the levels of data presentation, three related models have been developed: an external data model that displays the representations of each type of user existing in the organization (description of the subject area); a conceptual data model that displays a logical (or generalized) idea of data, independent of the type of database management system selected; an internal data model that displays the conceptual diagram in a specific way that is understandable to the selected target database management system. As an example of a subject area, the activity of a school of foreign languages is considered, namely, the registration of students in a school. The main problem is the large amount of information that is processed manually: paper forms of clients are drawn up, reports on the implementation of work are manually generated and filled out. It is necessary to develop a draft database for the registration of students of a school of foreign languages and to further implement it in the environment of the selected database management system.

OCTANE: Oncology Clinical Trial Annotation Engine.

Many targeted therapies are currently available only via clinical trials. Therefore, routine precision oncology using biomarker-based assignment to drug depends on matching patients to clinical trials. A comprehensive and up-to-date trial database is necessary for optimal patient-trial matching. We describe processes for establishing and maintaining a clinical trial database, focusing on genomically informed trials. Furthermore, we present OCTANE (Oncology Clinical Trial Annotation Engine), an informatics framework supporting these processes in a scalable fashion. To illustrate how the framework can be applied at an institution, we describe how we implemented an instance of OCTANE at a large cancer center. OCTANE consists of three modules. The data aggregation module automates retrieval, aggregation, and update of trial information. The annotation module establishes the database schema, implements data integration necessary for automation, and provides an annotation interface. The update module monitors trial change logs, identifies critical change events, and alerts the annotators when manual intervention may be needed. Using OCTANE, we annotated 5,439 oncology clinical trials (4,438 genomically informed trials) that collectively were associated with 1,453 drugs, 779 genes, and 252 cancer types. To date, we have used the database to screen 4,220 patients for trial eligibility. We compared the update module with expert review, and the module achieved 98.5% accuracy, 0% false-negative rate, and 2.3% false-positive rate. OCTANE is a general informatics framework that can be helpful for establishing and maintaining a comprehensive database necessary for automating patient-trial matching, which facilitates the successful delivery of personalized cancer care on a routine basis. Several OCTANE components are publically available and may be useful to other precision oncology programs.

A Novel Technique for Eliminating Parameter Initialization in Clustering

This study revealed the results of simulating the techniques for eliminate cluster initialization in clustering through series of test of different clustering techniques. Clusters of similar database schema from heterogeneous data sources was successfully clustered at an improved accuracy of 93.33%. The accuracy of clusters using self-organizing map neural network (SOM) was improved by implementing the two-step clustering method prior to SOM with an accuracy of 93.33 percent, an increase of 6.33% from the clustering result of SOM. Agglomerative clustering with hierarchical clustering are best combined to come-up with an automatic clustering task, thus, initialization was eliminated.
 Keywords: clustering, agglomerative clustering, hierarchical clustering, self-organizing map neural networks