About: Logical equality is a research topic. Over the lifetime, 2 publications have been published within this topic receiving 5 citations. The topic is also known as: logical equality.
TL;DR: This paper investigates the logical foundations of data replication by relating it to the theory of logical equality, and defines equality classes, which also determine the level of quality or consistency of a replicated data item.
Abstract: Copy management practiced by transactions bears some principal disadvantages. The most critical one is their bad performance. Another fundamental inconvenience depends on their restricted intensional expressiveness. We demonstrate that sophisticated copy management reduces this defect. Today many new strategies for distributed data management are developed (e.g. the snapshot concept, the escrow method), which enhance the intensional meaning of transactions. In this paper we investigate the logical foundations of data replication by relating it to the theory of logical equality. Therefore we define equality classes, which also determine the level of quality or consistency of a replicated data item. Also data processing mechanisms, by which indirectly data are assigned to certain equality classes, are discussed and assessed with regard to their extensional and intensional meaning.
TL;DR: This paper proposes association rule algorithms for logical equality relationships, modified from the original Apriori and FP-Growth algorithms.
Abstract: The association rule has become one of the most important techniques in data mining. New algorithms must be developed in order to apply it to more areas. This paper proposes association rule algorithms for logical equality relationships, modified from the original Apriori and FP-Growth algorithms. Logical equality is defined as truerarrtrue (1rarr1) or falserarrfalse (0rarr0) associations. This special relationship commonly occurs in the real world, such as the linkage in the stock markets and customer loyalty for a certain product.