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Lettris is a curious tetris-clone game where all the bricks have the same square shape but different content. Each square carries a letter. To make squares disappear and save space for other squares you have to assemble English words (left, right, up, down) from the falling squares.
Boggle gives you 3 minutes to find as many words (3 letters or more) as you can in a grid of 16 letters. You can also try the grid of 16 letters. Letters must be adjacent and longer words score better. See if you can get into the grid Hall of Fame !
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Tips: browse the semantic fields (see From ideas to words) in two languages to learn more.
A federated database system is a type of meta-database management system (DBMS), which transparently maps multiple autonomous database systems into a single federated database. The constituent databases are interconnected via a computer network and may be geographically decentralized. Since the constituent database systems remain autonomous, a federated database system is a contrastable alternative to the (sometimes daunting) task of merging several disparate databases. A federated database, or virtual database, is a composite of all constituent databases in a federated database system. There is no actual data integration in the constituent disparate databases as a result of data federation.
McLeod and Heimbigner were among the first to define a federated database system, as one which "define[s] the architecture and interconnect[s] databases that minimize central authority yet support partial sharing and coordination among database systems".
Through data abstraction, federated database systems can provide a uniform user interface, enabling users and clients to store and retrieve data in multiple noncontiguous databases with a single query -- even if the constituent databases are heterogeneous. To this end, a federated database system must be able to decompose the query into subqueries for submission to the relevant constituent DBMS's, after which the system must composite the result sets of the subqueries. Because various database management systems employ different query languages, federated database systems can apply wrappers to the subqueries to translate them into the appropriate query languages.
Among other surveys, defines a Federated Database as a collection of cooperating component systems which are autonomous and are possibly heterogeneous. The three important components of an FDBS as pointed out in  are autonomy, heterogeneity and distribution. Another dimension which has also been considered is the Networking Environment Computer Network, e.g., many DBSs over a LAN or many DBSs over a WAN update related functions of participating DBSs (e.g., no updates, nonatomic transitions, atomic updates).
A DBMS can be classified as either centralized or distributed. A centralized system manages a single database while distributed manages multiple databases. A component 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 DBMS that are not autonomous. A federated database system consists of component 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.
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 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 DB 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.
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 models provide different primitives such as object oriented (OO) models that support specialization and inheritance and relational models that do not. Differences due to constraints occur when two models support two different constraints. For example the set type in CODASYL 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 a DBMSs can also contribute to heterogeneity between other component DBMSs. For example differences in query languages with same data models or different versions of query languages could contribute 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
In creating a federated schema, one has to resolve such heterogeneities before integrating the component DB schemas.
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 semantically equivalent, but differently named parts from different schemas (=data models) (tables, attributes). A pairwise mapping between n attributes would result in 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 views. Two principal solutions can be realized, depending on the direction of the mapping:
Both are explained in more detail in the article Data integration. Alternate approaches to the schema matching problem and a classification of the same are explained in more detail in the article Schema Matching
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
Heterogeneities in an FDBS are primarily due to design autonomy.
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.
The Heterogeneity and Autonomy requirements pose special challenges concerning concurrency control in an FDBS, which is crucial for the correct execution of its concurrent transactions (see also Global concurrency control). Achieving global serializability, the major correctness criterion, under these requirements has been characterized as very difficult and unsolved. Commitment ordering, introduced in 1991, has provided a general solution for this issue (See Global serializability; See Commitment ordering also for the architectural aspects of the solution).
The five level schema architecture includes the following:
While accurately representing the state of the art in data integration, the Five Level Schema Architecture above does suffer from a major drawback, namely IT imposed look and feel. Modern data users demand control over how data is presented; their needs are somewhat in conflict with such bottom-up approaches to data integration.