Concepts inDynamic diversification of continuous data
Continuous function
In mathematics, a continuous function is a function for which, intuitively, "small" changes in the input result in "small" changes in the output. Otherwise, a function is said to be "discontinuous". A continuous function with a continuous inverse function is called "bicontinuous". Continuity of functions is one of the core concepts of topology, which is treated in full generality below.
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Cover tree
The cover tree is a type of data structure in computer science that is specifically designed to facilitate the speed-up of a nearest neighbor search. It is a refinement of the Navigating Net data structure, and related to a variety of other data structures developed for indexing intrinsically low-dimensional data. The tree can be thought of as a hierarchy of levels with the top level containing the root point and the bottom level containing every point in the metric space.
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Database
A database is an organized collection of data, today typically in digital form. The data are typically organized to model relevant aspects of reality (for example, the availability of rooms in hotels), in a way that supports processes requiring this information (for example, finding a hotel with vacancies). The term database is correctly applied to the data and their supporting data structures, and not to the database management system (DBMS).
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Result set
An SQL result set is a set of rows from a database, as well as meta-information about the query such as the column names, and the types and sizes of each column. Depending on the database system, the number of rows in the result set may or may not be known. Usually, this number is not known up front because the result set is built on-the-fly. Precomputations often impose undesired performance impacts. A result set is effectively a table.
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Recommender system
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item or social element they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environment (collaborative filtering approaches).
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NP-complete
In computational complexity theory, the complexity class NP-complete (abbreviated NP-C or NPC) is a class of decision problems. A decision problem L is NP-complete if it is in the set of NP problems so that any given solution to the decision problem can be verified in polynomial time, and also in the set of NP-hard problems so that any NP problem can be converted into L by a transformation of the inputs in polynomial time.
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