scholarly journals Towards a New Extracting and Querying Approach of Fuzzy Summaries

2018 ◽  
pp. 317-339
Author(s):  
Ines Benali-Sougui ◽  
Minyar Sassi Hidri ◽  
Amel Grissa-Touzi
Keyword(s):  
Relational Databases ◽  
Formal Model ◽  
Database Management System ◽  
Fuzzy Data ◽  
Formal Concepts ◽  
A New Technique ◽  
The Impact ◽  
Relational Database Management ◽  
Flexible Querying

Diversification of DB applications highlighted the limitations of relational database management system (RDBMS) particularly on the modeling plan. In fact, in the real world, we are increasingly faced with the situation where applications need to handle imprecise data and to offer a flexible querying to their users. Several theoretical solutions have been proposed. However, the impact of this work in practice remained negligible with the exception of a few research prototypes based on the formal model GEFRED. In this chapter, the authors propose a new approach for exploitation of fuzzy relational databases (FRDB) described by the model GEFRED. This approach consists of 1) a new technique for extracting summary fuzzy data, Fuzzy SAINTETIQ, based on the classification of fuzzy data and formal concepts analysis; 2) an approach of assessing flexible queries in the context of FDB based on the set of fuzzy summaries generated by our fuzzy SAINTETIQ system; 3) an approach of repairing and substituting unanswered query.

The Impact of the Australian Bibliographic Network on Australian Libraries

1996 ◽  
Vol 8 (1) ◽  
pp. 35-49 ◽  
Author(s):  
Tony Ralli
Keyword(s):  
New Zealand ◽  
Database Management System ◽  
Director General ◽  
National Library ◽  
Industry Standard ◽  
Bibliographic Network ◽  
The Usa ◽  
The Uk ◽  
The Impact ◽  
Relational Database Management

From its small beginnings in 1981 of six pilot users and the National Library of Australia (NLA), the Australian Bibliographic Network (ABN) has grown to be a truly national system, with 1,315 users at May 1995. The National Bibliographic Database has expanded to over 11 million records and 22 million holdings statements. It includes records from the USA, the UK, Canada, New Zealand, Singapore and Vietnam. It has come to be the single union list of holdings of Australian libraries, and the first point of reference for the majority of interlibrary loan transactions. The ABN is seen as both an NLA business and a cooperative undertaking of Australian libraries. Management consists of a Network Committee, which advises the Director General of NLA on all aspects of operation, and a Standards Committee, whose role is to make recommendations to NLA on cataloguing standards for the network. Annual Users' Meetings are held. Since 1987 NLA has been developing a database host for Australian libraries called OZLINE, in parallel with ABN. In 1990 it was decided to go for complete redevelopment using a text retrieval product and an industry standard Relational Database Management System. Following discussions with the National Library of New Zealand, which had indicated broadly similar requirements, it was agreed that the two libraries would jointly seek a system. The Australian service is to be known in future as WORLD 1.

How to Achieve Fuzzy Relational Databases Managing Fuzzy Data and Metadata

2011 ◽  
pp. 351-380 ◽  
Author(s):  
Mohamed Ali Ben Hassine ◽  
Amel Grissa Touzi ◽  
José Galindo ◽  
Habib Ounelli
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Fuzzy Data ◽  
Imprecise Data ◽  
The Third ◽  
Fuzzy Database ◽  
Existing Data ◽  
Flexible Querying ◽  
Mining Tools ◽  
Total Migration

Fuzzy relational databases have been introduced to deal with uncertain or incomplete information demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate flexible querying to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smooth migration towards this technology. This chapter presents a migration approach from relational databases towards fuzzy relational databases. This migration is divided into three strategies. The first one, named “partial migration,” is useful basically to include fuzzy queries in classic databases without changing existing data. It needs some definitions (fuzzy metaknowledge) in order to treat fuzzy queries written in FSQL language (Fuzzy SQL). The second one, named “total migration,” offers in addition to the flexible querying, a real fuzzy database, with the possibility to store imprecise data. This strategy requires a modification of schemas, data, and eventually programs. The third strategy is a mixture of the previous strategies, generally as a temporary step, easier and faster than the total migration.

scholarly journals Cost-Based Query Optimization in Centralized Relational Databases

2019 ◽  
Vol 24 (1) ◽  
pp. 42-46
Author(s):  
Nawaraj Paudel ◽  
Jagdish Bhatta
Keyword(s):  
Query Optimization ◽  
Relational Databases ◽  
Query Language ◽  
Database Management System ◽  
Query Plan ◽  
Efficiency Cost ◽  
The Cost ◽  
Disk Access ◽  
The Given ◽  
Relational Database Management

Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.

scholarly journals Improving the performance of big data databases

2019 ◽  
Vol 4 (2) ◽  
pp. 206-220
Author(s):  
Dashne Raouf Arif ◽  
Nzar Abdulqadir Ali
Keyword(s):  
Relational Database ◽  
Relational Databases ◽  
Query Language ◽  
Database Management System ◽  
Data Sets ◽  
Relational Database Management System ◽  
Nosql Database ◽  
Sql Database ◽  
Performance Results ◽  
Relational Database Management

Real-time monitoring systems utilize two types of database, they are relational databases such as MySQL and non-relational databases such as MongoDB. A relational database management system (RDBMS) stores data in a structured format using rows and columns. It is relational because the values of the tables are connected. A non-relational database is a database that does not adopt the relational structure given by traditional. In recent years, this class of databases has also been referred to as Not only SQL (NoSQL).  This paper discusses many comparisons that have been conducted on the execution time performance of types of databases (SQL and NoSQL). In SQL (Structured Query Language) databases different algorithms are used for inserting and updating data, such as indexing, bulk insert and multiple updating. However, in NoSQL different algorithms are used for inserting and updating operations such as default-indexing, batch insert, multiple updating and pipeline aggregation. As a result, firstly compared with related papers, this paper shows that the performance of both SQL and NoSQL can be improved. Secondly, performance can be dramatically improved for inserting and updating operations in the NoSQL database compared to the SQL database. To demonstrate the performance of the different algorithms for entering and updating data in SQL and NoSQL, this paper focuses on a different number of data sets and different performance results. The SQL part of the paper is conducted on 50,000 records to 3,000,000 records, while the NoSQL part of the paper is conducted on 50,000 to 16,000,000 documents (2GB) for NoSQL. In SQL, three million records are inserted within 606.53 seconds, while in NoSQL this number of documents is inserted within 67.87 seconds. For updating data, in SQL 300,000 records are updated within 271.17 seconds, while for NoSQL this number of documents is updated within just 46.02 seconds.  

Towards a Fuzzy Object-Relational Database Model

2011 ◽  
pp. 435-461 ◽  
Author(s):  
Carlos D. Barranco ◽  
Jesús R. Campaña ◽  
Juan M. Medina
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Relational Databases ◽  
Database Management System ◽  
Data Types ◽  
Relational Database Management System ◽  
Database Model ◽  
Object Relational ◽  
Relational Database Management

This chapter introduces a fuzzy object-relational database model including fuzzy extensions of the basic object-relational databases constructs, the user-defined data types, and the collection types. The fuzzy extensions of these constructs focus on two main flexible aspects, a way to flexibly compare complex data types and an extension of collection types allowing partial membership of its elements. Collection operators are also adapted to consider flexibly comparable domains for its elements. Such a fuzzy object-relational database model, and its implementation in a fuzzy object-relational database management system, provides an easy and effective way to manage a great amount of complex fuzzy data in object-relational databases for emerging fuzzy applications. As a sample of the proposal advantages, an application for dominant color based image retrieval, which is built on an object-relational database management system implementing the proposed fuzzy database model, is introduced.

ON THE CLASSIFICATION OF DIGITAL TECHNOLOGIES IN CIVIL PROCESS

2019 ◽  
pp. 27-35
Author(s):  
Alexandr Neznamov
Keyword(s):  
Information Processing ◽  
Scientific Community ◽  
Digital Technologies ◽  
Significant Feature ◽  
Legal Proceedings ◽  
Civil Process ◽  
Civil Proceedings ◽  
Processing Information ◽  
The Impact

Digital technologies are no longer the future but are the present of civil proceedings. That is why any research in this direction seems to be relevant. At the same time, some of the fundamental problems remain unattended by the scientific community. One of these problems is the problem of classification of digital technologies in civil proceedings. On the basis of instrumental and genetic approaches to the understanding of digital technologies, it is concluded that their most significant feature is the ability to mediate the interaction of participants in legal proceedings with information; their differentiating feature is the function performed by a particular technology in the interaction with information. On this basis, it is proposed to distinguish the following groups of digital technologies in civil proceedings: a) technologies of recording, storing and displaying (reproducing) information, b) technologies of transferring information, c) technologies of processing information. A brief description is given to each of the groups. Presented classification could serve as a basis for a more systematic discussion of the impact of digital technologies on the essence of civil proceedings. Particularly, it is pointed out that issues of recording, storing, reproducing and transferring information are traditionally more «technological» for civil process, while issues of information processing are more conceptual.

XML Modeling of Fuzzy Data with Relational Databases

2011 ◽  
Vol 34 (2) ◽  
pp. 291-303 ◽  
Author(s):  
Li YAN ◽  
Zong-Min MA ◽  
Jian LIU ◽  
Fu ZHANG
Keyword(s):  
Relational Databases ◽  
Fuzzy Data
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