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Formerly the "ELK stack", short for "Elasticsearch, Logstash, Kibana".) Elasticsearch uses Lucene and tries to make all its features available through the JSON and Java API. It supports facetting and percolating (a form of prospective search), [32] [33] which can be useful for notifying if new documents match for registered queries.
Kibana also provides a presentation tool, referred to as Canvas, that allows users to create slide decks that pull live data directly from Elasticsearch. [ 5 ] The combination of Elasticsearch, Logstash , and Kibana, referred to as the " Elastic Stack " (formerly the "ELK stack"), is available as a product or service. [ 6 ]
The company develops the Elastic Stack—Elasticsearch, Kibana, Beats, and Logstash—previously known as the ELK Stack, [4] free and paid proprietary features (formerly called X-Pack), Elastic Cloud (a family of SaaS offerings including the Elasticsearch Service), and Elastic Cloud Enterprise (ECE). [5] [6]
In computer science, an inverted index (also referred to as a postings list, postings file, or inverted file) is a database index storing a mapping from content, such as words or numbers, to its locations in a table, or in a document or a set of documents (named in contrast to a forward index, which maps from documents to content). [1]
Indexes can be created using one or more columns of a database table, providing the basis for both rapid random lookups and efficient access of ordered records. An index is a copy of selected columns of data, from a table, that is designed to enable very efficient search.
NodeXL integrates into Microsoft Excel 2007, 2010, 2013, 2016, 2019 and 365 and opens as a workbook with a variety of worksheets containing the elements of a graph structure such as edges and nodes. NodeXL can also import a variety of graph formats such as edgelists, adjacency matrices, GraphML , UCINet .dl, and Pajek .net.
A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-Complete.