Fuzzy Database Modeling with XML: 29 (Advances in Database Systems)

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A survey on fuzzy ontologies for the Semantic Web. Zongmin Ma , Miriam A. Capretz , Li Yan: Luyi Bai , Li Yan , Z. Applied Artificial Intelligence 29 3: Ma , Changming Xu: Incorporating fuzziness in spatiotemporal XML and transforming fuzzy spatiotemporal data from XML to relational databases. Li Yan , Zongmin Ma: A probabilistic object-oriented database model with fuzzy probability measures and its algebraic operations.

Complete Chapter List

Journal of Intelligent and Fuzzy Systems 28 5: Ma , Xu Chen: Formalizing fuzzy object-oriented database models using fuzzy ontologies. Journal of Intelligent and Fuzzy Systems 29 4: Jian Liu , Z. Ma , Xue Feng: Answering ordered tree pattern queries over fuzzy XML data. Ma , Weijun Li: Storing OWL ontologies in object-oriented databases.

Fuzzy Knowledge Management for the Semantic Web. Li Yan , Z. Querying fuzzy spatiotemporal data using XQuery. Integrated Computer-Aided Engineering 21 2: Ma , Qiulong Qv: Dynamically querying possibilistic XML data. Ma , Fu Zhang: Algebraic operations in fuzzy object-oriented databases. Information Systems Frontiers 16 4: Ma , Li Yan: Journal of Intelligent and Fuzzy Systems 26 2: Li Yan , Hailong Wang , Z. Journal of Intelligent and Fuzzy Systems 26 6: Modeling fuzzy information in fuzzy extended entity-relationship model and fuzzy relational databases.

Journal of Intelligent and Fuzzy Systems 27 4: Xing Wang , Z. Ma , Ji Chen , Xiangfu Meng: Weijun Li , Xu Chen , Z. Reengineering fuzzy nested relational databases into fuzzy XML model. Formal approach for reengineering fuzzy XML in fuzzy object-oriented databases.

Determining topological relationship of fuzzy spatiotemporal data integrated with XML twig pattern. Storing and querying fuzzy XML data in relational databases. Formal transformation from fuzzy object-oriented databases to fuzzy XML. Ma , Li Yan , Jingwei Cheng: Ma , Ruizhe Ma: Efficient processing of twig query with compound predicates in fuzzy XML. Conceptual design of object-oriented databases for fuzzy engineering information modeling.

Integrated Computer-Aided Engineering 20 2: Extending engineering data model for web-based fuzzy information modeling. Integrated Computer-Aided Engineering 20 4: Efficient labeling scheme for dynamic XML trees. Lingyu Zhang , Yi Yan , Z. Construction of fuzzy ontologies from fuzzy XML models. An overview of fuzzy Description Logics for the Semantic Web. Querying and ranking incomplete twigs in probabilistic XML. In the long term, these efforts were generally unsuccessful because specialized database machines could not keep pace with the rapid development and progress of general-purpose computers.

Thus most database systems nowadays are software systems running on general-purpose hardware, using general-purpose computer data storage. However this idea is still pursued for certain applications by some companies like Netezza and Oracle Exadata. IBM started working on a prototype system loosely based on Codd's concepts as System R in the early s.

Subsequent multi-user versions were tested by customers in and , by which time a standardized query language — SQL [ citation needed ] — had been added. PostgreSQL is often used for global mission critical applications the. In , this project was consolidated into an independent enterprise.

Another data model, the entity—relationship model , emerged in and gained popularity for database design as it emphasized a more familiar description than the earlier relational model. Later on, entity—relationship constructs were retrofitted as a data modeling construct for the relational model, and the difference between the two have become irrelevant. The s ushered in the age of desktop computing. The new computers empowered their users with spreadsheets like Lotus and database software like dBASE.

The dBASE product was lightweight and easy for any computer user to understand out of the box. The data manipulation is done by dBASE instead of by the user, so the user can concentrate on what he is doing, rather than having to mess with the dirty details of opening, reading, and closing files, and managing space allocation.

The s, along with a rise in object-oriented programming , saw a growth in how data in various databases were handled. Programmers and designers began to treat the data in their databases as objects. That is to say that if a person's data were in a database, that person's attributes, such as their address, phone number, and age, were now considered to belong to that person instead of being extraneous data. This allows for relations between data to be relations to objects and their attributes and not to individual fields.

Object databases and object-relational databases attempt to solve this problem by providing an object-oriented language sometimes as extensions to SQL that programmers can use as alternative to purely relational SQL. On the programming side, libraries known as object-relational mappings ORMs attempt to solve the same problem.

XML databases are a type of structured document-oriented database that allows querying based on XML document attributes. XML databases are mostly used in applications where the data is conveniently viewed as a collection of documents, with a structure that can vary from the very flexible to the highly rigid: NoSQL databases are often very fast, do not require fixed table schemas, avoid join operations by storing denormalized data, and are designed to scale horizontally.

In recent years, there has been a strong demand for massively distributed databases with high partition tolerance, but according to the CAP theorem it is impossible for a distributed system to simultaneously provide consistency , availability, and partition tolerance guarantees. A distributed system can satisfy any two of these guarantees at the same time, but not all three. For that reason, many NoSQL databases are using what is called eventual consistency to provide both availability and partition tolerance guarantees with a reduced level of data consistency.

NewSQL is a class of modern relational databases that aims to provide the same scalable performance of NoSQL systems for online transaction processing read-write workloads while still using SQL and maintaining the ACID guarantees of a traditional database system. Databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers see Enterprise software.

Databases are used to hold administrative information and more specialized data, such as engineering data or economic models. Examples include computerized library systems, flight reservation systems , computerized parts inventory systems , and many content management systems that store websites as collections of webpages in a database. One way to classify databases involves the type of their contents, for example: Another way is by their application area, for example: A third way is by some technical aspect, such as the database structure or interface type. This section lists a few of the adjectives used to characterize different kinds of databases.

2010 – today

Connolly and Begg define Database Management System DBMS as a "software system that enables users to define, create, maintain and control access to the database". Other extensions can indicate some other characteristic, such as DDBMS for a distributed database management systems. The functionality provided by a DBMS can vary enormously.

The core functionality is the storage, retrieval and update of data. Codd proposed the following functions and services a fully-fledged general purpose DBMS should provide: It is also generally to be expected the DBMS will provide a set of utilities for such purposes as may be necessary to administer the database effectively, including import, export, monitoring, defragmentation and analysis utilities. The core part of the DBMS interacting between the database and the application interface sometimes referred to as the database engine. Often DBMSs will have configuration parameters that can be statically and dynamically tuned, for example the maximum amount of main memory on a server the database can use.

The trend is to minimise the amount of manual configuration, and for cases such as embedded databases the need to target zero-administration is paramount. The large major enterprise DBMSs have tended to increase in size and functionality and can have involved thousands of human years of development effort through their lifetime.

Early multi-user DBMS typically only allowed for the application to reside on the same computer with access via terminals or terminal emulation software. The client—server architecture was a development where the application resided on a client desktop and the database on a server allowing the processing to be distributed. This evolved into a multitier architecture incorporating application servers and web servers with the end user interface via a web browser with the database only directly connected to the adjacent tier.

A general-purpose DBMS will provide public application programming interfaces API and optionally a processor for database languages such as SQL to allow applications to be written to interact with the database. For example an email system performing many of the functions of a general-purpose DBMS such as message insertion, message deletion, attachment handling, blocklist lookup, associating messages an email address and so forth however these functions are limited to what is required to handle email.

Because of the critical importance of database technology to the smooth running of an enterprise, database systems include complex mechanisms to deliver the required performance, security, and availability, and allow database administrators to control the use of these features. Database storage is the container of the physical materialization of a database.

It comprises the internal physical level in the database architecture. It also contains all the information needed e. Putting data into permanent storage is generally the responsibility of the database engine a.

A Literature Overview of Fuzzy Database Modeling

Though typically accessed by a DBMS through the underlying operating system and often using the operating systems' file systems as intermediates for storage layout , storage properties and configuration setting are extremely important for the efficient operation of the DBMS, and thus are closely maintained by database administrators.

A DBMS, while in operation, always has its database residing in several types of storage e. The database data and the additional needed information, possibly in very large amounts, are coded into bits. Data typically reside in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that attempt to optimize the best possible these levels' reconstruction when needed by users and programs, as well as for computing additional types of needed information from the data e.

Some DBMSs support specifying which character encoding was used to store data, so multiple encodings can be used in the same database.

Advances in Database Systems

Various low-level database storage structures are used by the storage engine to serialize the data model so it can be written to the medium of choice. Techniques such as indexing may be used to improve performance. Conventional storage is row-oriented, but there are also column-oriented and correlation databases. Often storage redundancy is employed to increase performance.

A common example is storing materialized views , which consist of frequently needed external views or query results. Storing such views saves the expensive computing of them each time they are needed. The downsides of materialized views are the overhead incurred when updating them to keep them synchronized with their original updated database data, and the cost of storage redundancy.

Occasionally a database employs storage redundancy by database objects replication with one or more copies to increase data availability both to improve performance of simultaneous multiple end-user accesses to a same database object, and to provide resiliency in a case of partial failure of a distributed database. Updates of a replicated object need to be synchronized across the object copies. In many cases, the entire database is replicated.

Database security deals with all various aspects of protecting the database content, its owners, and its users. It ranges from protection from intentional unauthorized database uses to unintentional database accesses by unauthorized entities e. Database access control deals with controlling who a person or a certain computer program is allowed to access what information in the database.

The information may comprise specific database objects e. Database access controls are set by special authorized by the database owner personnel that uses dedicated protected security DBMS interfaces. This may be managed directly on an individual basis, or by the assignment of individuals and privileges to groups, or in the most elaborate models through the assignment of individuals and groups to roles which are then granted entitlements. Data security prevents unauthorized users from viewing or updating the database.

Refine list

Using passwords, users are allowed access to the entire database or subsets of it called "subschemas". For example, an employee database can contain all the data about an individual employee, but one group of users may be authorized to view only payroll data, while others are allowed access to only work history and medical data. If the DBMS provides a way to interactively enter and update the database, as well as interrogate it, this capability allows for managing personal databases. Data security in general deals with protecting specific chunks of data, both physically i.

Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic database audit later by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be set up to attempt to detect security breaches.

Database transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database e. The acronym ACID describes some ideal properties of a database transaction: However, in some situations, it is desirable to move, migrate a database from one DBMS to another.

The migration involves the database's transformation from one DBMS type to another.

The transformation should maintain if possible the database related application i. Thus, the database's conceptual and external architectural levels should be maintained in the transformation. It may be desired that also some aspects of the architecture internal level are maintained. A complex or large database migration may be a complicated and costly one-time project by itself, which should be factored into the decision to migrate.

This in spite of the fact that tools may exist to help migration between specific DBMSs. After designing a database for an application, the next stage is building the database.

Typically, an appropriate general-purpose DBMS can be selected to be used for this purpose. Incorporating fuzziness in spatiotemporal XML and transforming fuzzy spatiotemporal data from XML to relational databases. Li Yan , Zongmin Ma: A probabilistic object-oriented database model with fuzzy probability measures and its algebraic operations.

Journal of Intelligent and Fuzzy Systems 28 5: Ma , Xu Chen: Formalizing fuzzy object-oriented database models using fuzzy ontologies. Journal of Intelligent and Fuzzy Systems 29 4: Jian Liu , Z. Ma , Xue Feng: Answering ordered tree pattern queries over fuzzy XML data. Ma , Weijun Li: Storing OWL ontologies in object-oriented databases.

Fuzzy Knowledge Management for the Semantic Web. Li Yan , Z. Querying fuzzy spatiotemporal data using XQuery. Integrated Computer-Aided Engineering 21 2: Ma , Qiulong Qv: Dynamically querying possibilistic XML data. Ma , Fu Zhang: Algebraic operations in fuzzy object-oriented databases. Information Systems Frontiers 16 4: Ma , Li Yan: Journal of Intelligent and Fuzzy Systems 26 2: Li Yan , Hailong Wang , Z.

Journal of Intelligent and Fuzzy Systems 26 6: Modeling fuzzy information in fuzzy extended entity-relationship model and fuzzy relational databases. Journal of Intelligent and Fuzzy Systems 27 4: Xing Wang , Z. Ma , Ji Chen , Xiangfu Meng: Weijun Li , Xu Chen , Z. Reengineering fuzzy nested relational databases into fuzzy XML model. Formal approach for reengineering fuzzy XML in fuzzy object-oriented databases. Determining topological relationship of fuzzy spatiotemporal data integrated with XML twig pattern.

Storing and querying fuzzy XML data in relational databases. Formal transformation from fuzzy object-oriented databases to fuzzy XML. Ma , Li Yan , Jingwei Cheng: Ma , Ruizhe Ma: Efficient processing of twig query with compound predicates in fuzzy XML. Conceptual design of object-oriented databases for fuzzy engineering information modeling. Integrated Computer-Aided Engineering 20 2: Extending engineering data model for web-based fuzzy information modeling.

Integrated Computer-Aided Engineering 20 4: Efficient labeling scheme for dynamic XML trees. Lingyu Zhang , Yi Yan , Z. Construction of fuzzy ontologies from fuzzy XML models. An overview of fuzzy Description Logics for the Semantic Web. Querying and ranking incomplete twigs in probabilistic XML.

World Wide Web 16 3: Ma , Li Yan , Yu Wang: A description logic approach for representing and reasoning on fuzzy object-oriented database models. Fuzzy Sets and Systems 1: Ma , Li Yan , Fu Zhang: Modeling fuzzy information in UML class diagrams and object-oriented database models.

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