scholarly journals NORMALIZATION IN DATABASE DESIGN

2018 ◽  
Vol 4 (1) ◽  
pp. 34
Author(s):  
Zainul Efendy
Keyword(s):  
Data Structures ◽  
Query Language ◽  
Database Design ◽  
Simple Solution ◽  
Transaction Model ◽  
Model Form

This research is done to find a simple solution how to find a normalization techniques are appropriate in database design, normalization techniques has several steps of which are forms of abnormal, normalization first, normalization 2st and normalization 3st, only 3 stages rare to be discussed in this study, as in lectures often find their students do not understand to implement this normalization techniques. The results of this study include determining the database data structures, forming sql (structural query language) by using MySQL DBMS and prototype transaction model form.

Database Design Based on B

2009 ◽  
pp. 440-456 ◽  
Author(s):  
Elvira Locuratolo
Keyword(s):  
Data Structures ◽  
Formal Method ◽  
Real Life ◽  
Database Design ◽  
Model Transformations ◽  
Software Systems ◽  
The Novel ◽  
Life Changes ◽  
Semantic Data ◽  
Object Models

This chapter is devoted to the integration of the ASSO features in B. ASSO is a database design methodology defined for achieving conceptual schema consistency, logical schema correctness, flexibility in reflecting the real-life changes on the schema and efficiency in accessing and storing information. B is an industrial formal method for specifying, designing, and coding software systems. Starting from a B specification of the data structures and of the transactions allowed on a database, two model transformations are designed: The resulting model, called Structured Database Schema, integrates static and dynamics exploiting the novel concepts of Class-Machine and Specialized Class-Machine. Formal details which must be specified if the conceptual model of ASSO is directly constructed in B are avoided; the costs of the consistency obligations are minimized. Class-Machines supported by semantic data models can be correctly linked with Class-Machines supported by object Models.

scholarly journals Context Definition and Query Language: Conceptual Specification, Implementation, and Evaluation

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1478 ◽  
Author(s):  
Alireza Hassani ◽  
Alexey Medvedev ◽  
Pari Delir Haghighi ◽  
Sea Ling ◽  
Arkady Zaslavsky ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Data Structures ◽  
Query Language ◽  
Contextual Information ◽  
Critical Factor ◽  
Software Components ◽  
Context Management ◽  
Iot Applications ◽  
Research Organizations ◽  
High Level

As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between ‘things’, where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context; and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.

Gambit: An Interactive Database Design Tool for Data Structures, Integrity Constraints, and Transactions

1985 ◽  
Vol SE-11 (7) ◽  
pp. 574-583 ◽  
Author(s):  
R.P. Braegger ◽  
A.M. Dudler ◽  
J. Rebsamen ◽  
C.A. Zehnder
Keyword(s):  
Data Structures ◽  
Database Design ◽  
Design Tool ◽  
Integrity Constraints

scholarly journals Application of graph databases for transport purposes

2016 ◽  
Vol 64 (3) ◽  
pp. 457-466 ◽  
Author(s):  
A. Czerepicki
Keyword(s):  
Information System ◽  
Data Structures ◽  
Public Transport ◽  
Data Model ◽  
Query Language ◽  
Relational Model ◽  
Graph Database ◽  
Graph Databases ◽  
Graph Data ◽  
Innovative Concept

Abstract The article presents an innovative concept of applying graph databases in transport information systems. The model of a graph database has been presented together with implementation of data structures and search operations in a graph. The transformation concept of relational model to a graph data model has been developed. The schema of graph database has been proposed for public transport information system purposes. The realization methods have been illustrated by the use of search function based on the Cypher query language.

Modeling Web-Based Data in a Data Warehouse

2011 ◽  
pp. 826-831
Author(s):  
Hadrian Peter ◽  
Charles Greenidge
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Data Warehouse ◽  
Data Structures ◽  
Support System ◽  
Database Design ◽  
Storage Facility ◽  
Web Based ◽  
Good Data

Good database design generates effective operational databases through which we can track customers, sales, inventories, and other variables of interest. The main reason for generating, storing, and managing good data is to enhance the decision-making process. The tool used during this process is the decision support system (DSS). The information requirements of the DSS have become so complex, that it is difficult for it to extract all the necessary information from the data structures typically found in operational databases. For this reason, a new storage facility called a data warehouse has been developed. Data in the data warehouse have characteristics that are quite distinct from those in the operational database (Rob & Coronel, 2002).

Integrative, Interdisciplinary Database Design for the Spatial Humanities: the Case of the Holocaust Ghettos Project

2020 ◽  
Vol 14 (1-2) ◽  
pp. 64-80 ◽  
Author(s):  
Anne Kelly Knowles ◽  
Justus Hillebrand ◽  
Paul B. Jaskot ◽  
Anika Walke
Keyword(s):  
Data Structures ◽  
Collaborative Design ◽  
Data Entry ◽  
Database Design ◽  
Ethical Standards ◽  
Historical Gis ◽  
Scholarly Literature ◽  
The Core ◽  
Database Construction ◽  
The Holocaust

Databases are central to the digital, spatial, and geohumanities. There is surprisingly little scholarly literature, however, on the process of database construction in humanities projects. This article describes a process of interdisciplinary database design that emerged in the course of building the core sections of an historical GIS of Holocaust ghettos. The process foregrounds collaborative design, testing that purposely flushes out paradigmatic differences and ontological problems, and revision to incorporate group decisions and agreed-upon meanings into data structures, field definitions, and instructions for data entry. The result is a deeply integrative form of mixed methodology that incorporates ethical standards along with data entry instructions and team training.

Optimizing Procurement Decisions in Networked Virtual Enterprises

2012 ◽  
Vol 4 (3) ◽  
pp. 43-67
Author(s):  
Amihai Motro ◽  
Alexander Brodsky ◽  
Nathan Egge ◽  
Alessandro D’Atri
Keyword(s):  
Ad Hoc ◽  
Query Language ◽  
Optimization Problems ◽  
Virtual Enterprise ◽  
Efficient Solutions ◽  
Virtual Enterprises ◽  
Business Objectives ◽  
Transaction Model ◽  
Formal Concepts ◽  
Business Entities

A virtual enterprise is an ad hoc coalition of independent business entities who collaborate on the manufacturing of complex products in a networked environment. This collaboration is enabled by the concept of a transaction, a mechanism with which members acquire necessary components from other members. An external procurement request submitted to the enterprise launches a tree-structured series of transactions among its members (similar to supply chains). Each such transaction is associated with a purchase price, but also with a risk of failure. That members have the option to procure components from different co-members, each charging its individual price and posing its specific risk, raises challenging optimization problems related to the fulfillment of business objectives. This paper defines a transaction model for virtual enterprises, with formal concepts such as price, risk, and business objectives. The Decision Guidance Query Language (DGQL) is presented, a language for modeling and solving optimization problems in a database setting, and shows how DGQL can provide intuitive and efficient solutions to the optimization problems raised in the model. The model, the optimization programs, and the experimentation promote strong collaboration and common objectives among its members, and one in which collaboration is limited, with members retaining much of their autonomy and individual objectives.

Database Design Based on B

2011 ◽  
pp. 35-61
Author(s):  
Elvira Locuratolo
Keyword(s):  
Data Structures ◽  
Formal Method ◽  
Real Life ◽  
Database Design ◽  
Model Transformations ◽  
Software Systems ◽  
The Novel ◽  
Life Changes ◽  
Semantic Data ◽  
Object Models

This chapter is devoted to the integration of the ASSO features in B. ASSO is a database design methodology defined for achieving conceptual schema consistency, logical schema correctness, flexibility in reflecting the real-life changes on the schema and efficiency in accessing and storing information. B is an industrial formal method for specifying, designing, and coding software systems. Starting from a B specification of the data structures and of the transactions allowed on a database, two model transformations are designed: The resulting model, called Structured Database Schema, integrates static and dynamics exploiting the novel concepts of Class-Machine and Specialized Class-Machine. Formal details which must be specified if the conceptual model of ASSO is directly constructed in B are avoided; the costs of the consistency obligations are minimized. Class-Machines supported by semantic data models can be correctly linked with Class-Machines supported by object Models.

Schema-Based JSON Data Stores in Relational Databases

2019 ◽  
Vol 30 (3) ◽  
pp. 38-70 ◽  
Author(s):  
Lubna Irshad ◽  
Li Yan ◽  
Zongmin Ma
Keyword(s):  
Data Exchange ◽  
Relational Databases ◽  
Query Language ◽  
Relational Model ◽  
Relational Schema ◽  
Transaction Model ◽  
Binary Format ◽  
Fast Development ◽  
Relational Database Management ◽  
Transactional Models

JSON is a simple, compact and light weighted data exchange format to communicate between web services and client applications. NoSQL document stores evolve with the popularity of JSON, which can support JSON schema-less storage, reduce cost, and facilitate quick development. However, NoSQL still lacks standard query language and supports eventually consistent BASE transaction model rather than the ACID transaction model. This is very challenging and a burden on the developer. The relational database management systems (RDBMS) support JSON in binary format with SQL functions (also known as SQL/JSON). However, these functions are not standardized yet and vary across vendors along with different limitations and complexities. More importantly, complex searches, partial updates, composite queries, and analyses are cumbersome and time consuming in SQL/JSON compared to standard SQL operations. It is essential to integrate JSON into databases that use standard SQL features, support ACID transactional models, and has the capability of managing and organizing data efficiently. In this article, we empower JSON to use relational databases for analysis and complex queries. The authors reveal that the descriptive nature of the JSON schema can be utilized to create a relational schema for the storage of the JSON document. Then, the powerful SQL features can be used to gain consistency and ACID compatibility for querying JSON instances from the relational schema. This approach will open a gateway to combine the best features of both worlds: the fast development of JSON, consistency of relational model, and efficiency of SQL.

scholarly journals XQuery, XSLT and JSON

2012 ◽  
Author(s):  
Jonathan Robie
Keyword(s):  
Data Structures ◽  
Working Group ◽  
Query Language ◽  
The Internet ◽  
Nosql Databases ◽  
Common Solution ◽  
Transformation Language ◽  
Working Groups ◽  
Efficient Processing ◽  
Intermediate Results

XML and JSON have become the dominant formats for exchanging data on the Internet, and applications frequently need to send and receive data in many different JSON-based or XML-based formats, consuming or producing data in JSON, XML, or HTML. JSON has not yet developed an application stack as mature as the XML application stack; for instance, there is still no standard query language, transformation language, or schema language. And the XML application stack has not yet evolved to easily process JSON. There are several areas where the XML stack should evolve to better support developers who work with JSON together with XML, and the features needed to support JSON in XQuery and XSLT also provide data structures that simplify writing queries and transformations, and allow more efficient processing of intermediate results when processing XML. As JSON becomes increasingly common in databases, and is exchanged among servers, these same kinds of tools may even become important in environments that use only JSON. This paper focuses on queries and transformations, looking at JSON support in several NoSQL databases, the JSONiq proposal (which adds JSON objects and arrays to XQuery), and the XSLT maps proposal (which adds maps that can represent JSON objects and arrays). At the time of writing, the W3C XML Query Working Group and the W3C XSL Working Group are considering several proposals for supporting JSON. The Working Groups expect to agree on a common solution that can be used in both XSLT and XQuery.

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