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Major DBMSs, including SQLite, [5] MySQL, [6] Oracle, [7] IBM Db2, [8] Microsoft SQL Server [9] and PostgreSQL [10] support prepared statements. Prepared statements are normally executed through a non-SQL binary protocol for efficiency and protection from SQL injection, but with some DBMSs such as MySQL prepared statements are also available using a SQL syntax for debugging purposes.
A classification of SQL injection attacking vector as of 2010. In computing, SQL injection is a code injection technique used to attack data-driven applications, in which malicious SQL statements are inserted into an entry field for execution (e.g. to dump the database contents to the attacker).
Conjunctive queries without distinguished variables are called boolean conjunctive queries.Conjunctive queries where all variables are distinguished (and no variables are bound) are called equi-join queries, [1] because they are the equivalent, in the relational calculus, of the equi-join queries in the relational algebra (when selecting all columns of the result).
An example of how you can see code injection first-hand is to use your browser's developer tools. Code injection vulnerabilities are recorded by the National Institute of Standards and Technology (NIST) in the National Vulnerability Database as CWE-94. Code injection peaked in 2008 at 5.66% as a percentage of all recorded vulnerabilities. [4]
The natural numbers 0 and 1 are trivial sum-product numbers for all , and all other sum-product numbers are nontrivial sum-product numbers. For example, the number 144 in base 10 is a sum-product number, because 1 + 4 + 4 = 9 {\displaystyle 1+4+4=9} , 1 × 4 × 4 = 16 {\displaystyle 1\times 4\times 4=16} , and 9 × 16 = 144 {\displaystyle 9 ...
A function is bijective if it is both injective and surjective. A bijective function is also called a bijection or a one-to-one correspondence (not to be confused with one-to-one function, which refers to injection). A function is bijective if and only if every possible image is mapped to by exactly one argument. [1]
Belief propagation, also known as sum–product message passing, is a message-passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields. It calculates the marginal distribution for each unobserved node (or variable), conditional on any observed nodes (or variables).
This example is an instance of the Few Sums, Many Products [6] version of the sum-product problem of György Elekes and Imre Z. Ruzsa. A consequence of their result is that any set with small additive doubling (such as an arithmetic progression) has the lower bound on the product set | AA | = Ω(| A | 2 log −1 (| A |)).