Editing Federated database system (section)

== FDBS architecture ==

A [[Database management system|DBMS]] can be classified as either centralized or distributed. A centralized system manages a single database while distributed manages multiple databases. A component [[Database|DBS]] in a DBMS may be centralized or distributed. A multiple DBS (MDBS) can be classified into two types depending on the autonomy of the component DBS as federated and non federated. A nonfederated database system is an integration of component [[Database management system|DBMS]] that are not autonomous.
A federated database system consists of component [[Database|DBS]] that are autonomous yet participate in a federation to allow partial and controlled sharing of their data.

Federated architectures differ based on levels of integration with the component database systems and the extent of services offered by the federation. A FDBS can be categorized as loosely or tightly coupled systems.

* Loosely Coupled require component databases to construct their own federated [[Database schema|schema]]. A user will typically access other component database systems by using a multidatabase language but this removes any levels of location transparency, forcing the user to have direct knowledge of the federated schema. A user imports the data they require from other component databases and integrates it with their own to form a federated schema.
* Tightly coupled system consists of component systems that use independent processes to construct and publicize an integrated federated schema.

Multiple DBS of which FDBS are a specific type can be characterized along three dimensions: Distribution, Heterogeneity and Autonomy. Another characterization could be based on the dimension of networking, for example single databases or multiple databases in a LAN or WAN.

=== Distribution ===

Distribution of data in an FDBS is due to the existence of a multiple DBS before an FDBS is built. Data can be distributed among multiple databases which could be stored in a single computer or multiple computers. These computers could be geographically located in different places but interconnected by a network. The benefits of data distribution help in increased availability and reliability as well as improved access times.

==== Heterogeneity ====
{{main|Heterogeneous database system}}
Heterogeneities in databases arise due to factors such as differences in structures, semantics of data, the constraints supported or [[query language]]. Differences in structure occur when two [[data model]]s provide different primitives such as [[Object-Oriented Modeling|object oriented (OO) models]] that support specialization and inheritance and [[relational model]]s that do not. Differences due to constraints occur when two models support two different constraints. For example, the set type in [[CODASYL]] [[Database schema|schema]] may be partially modeled as a referential integrity constraint in a relationship schema. [[CODASYL]] supports insertion and retention that are not captured by referential integrity alone. The query language supported by one [[Database management system|DBMS]] can also contribute to [[Heterogeneous Database System|heterogeneity]] between other component [[Database management system|DBMSs]]. For example, differences in query languages with the same [[data model]]s or different versions of query languages could contribute to [[Heterogeneous Database System|heterogeneity]].

Semantic heterogeneities arise when there is a disagreement about meaning, interpretation or intended use of [[data]]. At the schema and data level, classification of possible heterogeneities include:
* Naming conflicts e.g. [[database]]s using different names to represent the same concept.
* Domain conflicts or [[data]] representation conflicts e.g. [[database]]s using different values to represent same concept.
* Precision conflicts e.g. [[database]]s using same data values from domains of different [[Cardinality|cardinalities]] for same [[data]].
* [[Metadata]] conflicts e.g. same concepts are represented at [[Database schema|schema]] level and instance level.
* [[Data]] conflicts e.g. missing [[Attribute (computing)|attributes]]
* [[Database schema|Schema]] conflicts e.g. table versus table conflict which includes naming conflicts, data conflicts etc.

In creating a federated schema, one has to resolve such heterogeneities before integrating the component DB schemas.

==== Schema matching, schema mapping ====
Dealing with incompatible data types or query syntax is not the only obstacle to a concrete implementation of an FDBS. In systems that are not planned top-down, a generic problem lies in matching [[semantic equivalence|semantically equivalent]], but differently named parts from different [[logical schema|schemas]] (=data models) (tables, attributes). A pairwise mapping between ''n'' attributes would result in <math>n (n-1) \over 2</math> mapping rules (given equivalence mappings) - a number that quickly gets too large for practical purposes. A common way out is to provide a global schema that comprises the relevant parts of all member schemas and provide mappings in the form of [[database view]]s. Two principal approaches depend on the direction of the mapping:
# Global as View (GaV): the global schema is defined in terms of the underlying schemas
# Local as View (LaV): the local schemas are defined in terms of the global schema
Both are examples of [[data integration]], called the [[schema matching]] problem.

=== Autonomy ===
Fundamental to the difference between an MDBS and an FDBS is the concept of autonomy.  It is important to understand the aspects of autonomy for component databases and how they can be addressed when a component DBS participates in an FDBS.
There are four kinds of autonomies addressed:
* Design Autonomy which refers to ability to choose its design irrespective of data, query language or conceptualization, functionality of the system implementation.
[[Heterogeneous Database System|Heterogeneities]] in an FDBS are primarily due to design autonomy.
* Communication autonomy refers to the general operation of the DBMS to communicate with other [[Database management system|DBMS]] or not.
* Execution autonomy allows a component DBMS to control the operations requested by local and external operations.
* Association autonomy gives a power to component DBS to disassociate itself from a federation which means FDBS can operate independently of any single [[Database|DBS]].

The ANSI/X3/SPARC Study Group outlined a three level data description architecture, the components of which are the conceptual schema, internal schema and external schema of databases.  The three level architecture is however inadequate to describing the architectures of an FDBS.  It was therefore extended to support the three dimensions of the FDBS namely Distribution, Autonomy and Heterogeneity.  The five level schema architecture is explained below.

=== Concurrency control ===
The ''Heterogeneity'' and ''Autonomy'' requirements pose special challenges concerning [[concurrency control]] in an FDBS, which is crucial for the correct execution of its concurrent [[Database transaction|transactions]] (see also [[Global concurrency control]]). Achieving [[global serializability]], the major correctness criterion, under these requirements has been characterized as very difficult and unsolved.<ref name="refone" />