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The (standard) Boolean model of information retrieval (BIR) [1] is a classical information retrieval (IR) model and, at the same time, the first and most-adopted one. [2] The BIR is based on Boolean logic and classical set theory in that both the documents to be searched and the user's query are conceived as sets of terms (a bag-of-words model).
The output of the problem is the set of answers to the query on the database. If the queries are Boolean queries, i.e., queries have a yes or no answer (for example, Boolean conjunctive queries) then the query evaluation problem is a decision problem. The query evaluation problem is usually posed for a specific class of queries and databases.
This is sometimes called an inverse mask or a wildcard mask. When the value of the mask is broken down into binary (0s and 1s), the results determine which address bits are to be considered in processing the traffic. A 0-bit indicates that the address bit must be considered (exact match); a 1-bit in the mask is a "don't care". This table ...
A wildcard mask can be thought of as an inverted subnet mask. For example, a subnet mask of 255.255.255.0 (11111111.11111111.11111111.00000000 2) inverts to a wildcard mask of 0.0.0.255 (00000000.00000000.00000000.11111111 2). A wild card mask is a matching rule. [2] The rule for a wildcard mask is: 0 means that the equivalent bit must match
The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science [ 1 ] of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes ...
Data masking or data obfuscation is the process of modifying sensitive data in such a way that it is of no or little value to unauthorized intruders while still being usable by software or authorized personnel. Data masking can also be referred as anonymization, or tokenization, depending on different context.
The SQL:1999 standard calls for a Boolean type, [1]. IBM Db2 supports boolean values since around 11.1 [2]. Microsoft SQL Server supports storage for booleans using "BIT" data type [citation needed]. MySQL interprets "BOOL" and "BOOLEAN" as a mapping for its native TINYINT(1) type. [3] PostgreSQL provides a standard conforming Boolean type. [4]
Candidate documents from the corpus can be retrieved and ranked using a variety of methods. Relevance rankings of documents in a keyword search can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as a vector with same dimension as the vectors that ...