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Cascading consists of a data processing API, integration API, process planner and process scheduler. Cascading leverages the scalability of Hadoop but abstracts standard data processing operations away from underlying map and reduce tasks. [7] [better source needed] Developers use Cascading to create a .jar file that describes the required ...
In database systems, consistency (or correctness) refers to the requirement that any given database transaction must change affected data only in allowed ways. Any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This does not guarantee correctness ...
Information cascades occur when external information obtained from previous participants in an event overrides one's own private signal, irrespective of the correctness of the former over the latter. The experiment conducted by Anderson [10] is a useful example of this process. The experiment consisted of two urns labeled A and B. Urn A ...
In-database processing, sometimes referred to as in-database analytics, refers to the integration of data analytics into data warehousing functionality. Today, many large databases, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods.
SQL refers to Structured Query Language, a kind of language used to access, update and manipulate database. In SQL, ROLLBACK is a command that causes all data changes since the last START TRANSACTION or BEGIN to be discarded by the relational database management systems (RDBMS), so that the state of the data is "rolled back" to the way it was before those changes were made.
One subtlety is that the value of a method call ("message") in a cascade is still the ordinary value of the message, not the receiver. This is a problem when you do want the value of the receiver, for example when building up a complex value. This can be worked around by using the special yourself method that simply returns the receiver: [2]
Construction of an information flow diagram requires the knowledge of different information sources and the connections between them. The sources and targets of information flow are one of the following: actor, use case, node, artefact, class, component, port, property, interface, package, activity node, activity partition, or instance specification.
Having produced a conceptual data model that users are happy with, the next stage is to translate this into a schema that implements the relevant data structures within the database. This process is often called logical database design, and the output is a logical data model expressed in the form of a schema. Whereas the conceptual data model ...