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  2. Query optimization - Wikipedia

    en.wikipedia.org/wiki/Query_optimization

    The set of query plans examined is formed by examining the possible access paths (e.g., primary index access, secondary index access, full file scan) and various relational table join techniques (e.g., merge join, hash join, product join). The search space can become quite large depending on the complexity of the SQL query. There are two types ...

  3. Database index - Wikipedia

    en.wikipedia.org/wiki/Database_index

    To reduce such index size, some systems allow including non-key fields in the index. Non-key fields are not themselves part of the index ordering but only included at the leaf level, allowing for a covering index with less overall index size. This can be done in SQL with CREATE INDEX my_index ON my_table (id) INCLUDE (name);. [8] [9]

  4. Full table scan - Wikipedia

    en.wikipedia.org/wiki/Full_table_scan

    The cost is predictable, as every time database system needs to scan full table row by row. When table is less than 2 percent of database block buffer, the full scan table is quicker. Cons: Full table scan occurs when there is no index or index is not being used by SQL. And the result of full scan table is usually slower that index table scan.

  5. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]

  6. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    Ideally, the value of ε is given by the problem to solve (e.g. a physical distance), and minPts is then the desired minimum cluster size. [ a ] MinPts : As a rule of thumb, a minimum minPts can be derived from the number of dimensions D in the data set, as minPts ≥ D + 1.

  7. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri

  8. Materialized view - Wikipedia

    en.wikipedia.org/wiki/Materialized_view

    This enables much more efficient access, at the cost of extra storage and of some data being potentially out-of-date. Materialized views find use especially in data warehousing scenarios, where frequent queries of the actual base tables can be expensive. [citation needed] In a materialized view, indexes can be built on any column. In contrast ...

  9. File size - Wikipedia

    en.wikipedia.org/wiki/File_size

    File size is a measure of how much data a computer file contains or how much storage space it is allocated. Typically, file size is expressed in units based on byte . A large value is often expressed with a metric prefix (as in megabyte and gigabyte ) or a binary prefix (as in mebibyte and gibibyte ).