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The CAP theorem is based on three trade-offs, one of which is "atomic consistency" (shortened to "consistency" for the acronym), about which the authors note, "Discussing atomic consistency is somewhat different than talking about an ACID database, as database consistency refers to transactions, while atomic consistency refers only to a property of a single request/response operation sequence.
Note that consistency as defined in the CAP theorem is quite different from the consistency guaranteed in ACID database transactions. [4] Availability Every request received by a non-failing node in the system must result in a response. This is the definition of availability in CAP theorem as defined by Gilbert and Lynch. [1]
C-theorem ; CAP theorem (theoretical computer science) CPCTC (triangle geometry) Cameron–Erdős theorem (discrete mathematics) Cameron–Martin theorem (measure theory) Cantor–Bernstein–Schroeder theorem (set theory, cardinal numbers) Cantor's intersection theorem (real analysis) Cantor's isomorphism theorem (order theory)
The CMA includes assessments for ELA, mathematics, and science. [3] Eligible students may take either the CST or the CMA in a subject area; for example, a student in grade five may take the CST for ELA and take the CMA for mathematics and science. [3] The CMA was first administered in the spring of 2008 to students in grades three through five. [3]
In a distributed database system, a transaction could execute its operations at multiple sites. Since atomicity requires every distributed transaction to be atomic, the transaction must have the same fate (commit or abort) at every site.
The PACELC theorem was first described by Daniel Abadi from Yale University in 2010 in a blog post, [2] which he later clarified in a paper in 2012. [3] The purpose of PACELC is to address his thesis that "Ignoring the consistency/latency trade-off of replicated systems is a major oversight [in CAP], as it is present at all times during system operation, whereas CAP is only relevant in the ...
Eric Allen Brewer is professor emeritus of computer science at the University of California, Berkeley [1] and vice-president of infrastructure at Google. [2] His research interests include operating systems and distributed computing. He is known for formulating the CAP theorem about distributed network applications in the late 1990s. [3]
Blockchain analysis is the process of inspecting, identifying, clustering, modeling and visually representing data on a cryptographic distributed-ledger known as a blockchain. [ 1 ] [ 2 ] The goal of blockchain analysis is to discover useful information about different actors transacting in cryptocurrency.