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Range minimum query reduced to the lowest common ancestor problem. Given an array A[1 … n] of n objects taken from a totally ordered set, such as integers, the range minimum query RMQ A (l,r) =arg min A[k] (with 1 ≤ l ≤ k ≤ r ≤ n) returns the position of the minimal element in the specified sub-array A[l … r].
Given a function that accepts an array, a range query (,) on an array = [,..,] takes two indices and and returns the result of when applied to the subarray [, …,].For example, for a function that returns the sum of all values in an array, the range query (,) returns the sum of all values in the range [,].
A range query is a common database operation that retrieves all records where some value is between an upper and lower boundary. [1] For example, list all employees with 3 to 5 years' experience. Range queries are unusual because it is not generally known in advance how many entries a range query will return, or if it will return any at all.
In computer science, the range searching problem consists of processing a set S of objects, in order to determine which objects from S intersect with a query object, called the range. For example, if S is a set of points corresponding to the coordinates of several cities, find the subset of cities within a given range of latitudes and longitudes .
This contrasts with other range query problems, such as the range minimum query which have solutions offering constant time query time and linear space. This is due to the hardness of the mode problem, since even if we know the mode of A [ i : j ] {\displaystyle A[i:j]} and the mode of A [ j + 1 : k ] {\displaystyle A[j+1:k]} , there is no ...
Queries, which retrieve the data based on specific criteria. This is an important element of SQL. Statements, which may have a persistent effect on schemata and data, or may control transactions, program flow, connections, sessions, or diagnostics. SQL statements also include the semicolon (";") statement terminator. Though not required on ...
For example, AVERAGE=SUM/COUNT and RANGE=MAX−MIN. In the MapReduce framework, these steps are known as InitialReduce (value on individual record/singleton set), Combine (binary merge on two aggregations), and FinalReduce (final function on auxiliary values), [ 5 ] and moving decomposable aggregation before the Shuffle phase is known as an ...
In situations where the number of unique values of a column is far less than the number of rows in the table, column-oriented storage allow significant savings in space through data compression. Columnar storage also allows fast execution of range queries (e.g., show all records where a particular column is between X and Y, or less than X.)