• Windows Full Text Search
• Windows 7 Full Text Search
• Ms Sql Full Text Search

• With Windows 10, many options and settings have been moved around or changed entirely. If you’d like Windows’ search to examine the contents of files, as well as the names, here’s how to do.
• Oct 28, 2011  Occasionally I need to search all the files on my computer for a specific word or phrase. The 'occasionally' part of that sentence is actually the most important, because it means I don't want to use a search feature that creates and maintains a full index of my hard disk. It takes up space, and slows down my computer. I'd much rather take the speed hit, and have something.

Here are all the steps I think you need to follow to actually search: 1. Make sure that Windows 7 is actually going to search files with your extension, and it appears that using this list properly is important.whether or not you are looking in an indexed location.!! Hit the start button and type 'search' in the search box.

In text retrieval, full-text search refers to techniques for searching a single computer-stored document or a collection in a full-text database. Full-text search is distinguished from searches based on metadata or on parts of the original texts represented in databases (such as titles, abstracts, selected sections, or bibliographical references).

In a full-text search, a search engine examines all of the words in every stored document as it tries to match search criteria (for example, text specified by a user). Full-text-searching techniques became common in online bibliographic databases in the 1990s.^{[verification needed]} Many websites and application programs (such as word processing software) provide full-text-search capabilities. Some web search engines, such as AltaVista, employ full-text-search techniques, while others index only a portion of the web pages examined by their indexing systems.^[1]

• 4Performance improvements
• 5Software

Indexing[edit]

When dealing with a small number of documents, it is possible for the full-text-search engine to directly scan the contents of the documents with each query, a strategy called 'serial scanning'. This is what some tools, such as grep, do when searching.

However, when the number of documents to search is potentially large, or the quantity of search queries to perform is substantial, the problem of full-text search is often divided into two tasks: indexing and searching. The indexing stage will scan the text of all the documents and build a list of search terms (often called an index, but more correctly named a concordance). In the search stage, when performing a specific query, only the index is referenced, rather than the text of the original documents.^[2]

The indexer will make an entry in the index for each term or word found in a document, and possibly note its relative position within the document. Usually the indexer will ignore stop words (such as 'the' and 'and') that are both common and insufficiently meaningful to be useful in searching. Some indexers also employ language-specific stemming on the words being indexed. For example, the words 'drives', 'drove', and 'driven' will be recorded in the index under the single concept word 'drive'.

The precision vs. recall tradeoff[edit]

Recall measures the quantity of relevant results returned by a search, while precision is the measure of the quality of the results returned. Recall is the ratio of relevant results returned to all relevant results. Precision is the number of relevant results returned to the total number of results returned.

The diagram at right represents a low-precision, low-recall search. In the diagram the red and green dots represent the total population of potential search results for a given search. Red dots represent irrelevant results, and green dots represent relevant results. Relevancy is indicated by the proximity of search results to the center of the inner circle. Of all possible results shown, those that were actually returned by the search are shown on a light-blue background. In the example only 1 relevant result of 3 possible relevant results was returned, so the recall is a very low ratio of 1/3, or 33%. The precision for the example is a very low 1/4, or 25%, since only 1 of the 4 results returned was relevant.^[3]

Due to the ambiguities of natural language, full-text-search systems typically includes options like stop words to increase precision and stemming to increase recall. Controlled-vocabulary searching also helps alleviate low-precision issues by tagging documents in such a way that ambiguities are eliminated. The trade-off between precision and recall is simple: an increase in precision can lower overall recall, while an increase in recall lowers precision.^[4]

False-positive problem[edit]

Full-text searching is likely to retrieve many documents that are not relevant to the intended search question. Such documents are called false positives (see Type I error). The retrieval of irrelevant documents is often caused by the inherent ambiguity of natural language. In the sample diagram at right, false positives are represented by the irrelevant results (red dots) that were returned by the search (on a light-blue background).

Clustering techniques based on Bayesian algorithms can help reduce false positives. For a search term of 'bank', clustering can be used to categorize the document/data universe into 'financial institution', 'place to sit', 'place to store' etc. Depending on the occurrences of words relevant to the categories, search terms or a search result can be placed in one or more of the categories. This technique is being extensively deployed in the e-discovery domain.^{[clarification needed]}

Performance improvements[edit]

The deficiencies of free text searching have been addressed in two ways: By providing users with tools that enable them to express their search questions more precisely, and by developing new search algorithms that improve retrieval precision.

Improved querying tools[edit]

• Keywords. Document creators (or trained indexers) are asked to supply a list of words that describe the subject of the text, including synonyms of words that describe this subject. Keywords improve recall, particularly if the keyword list includes a search word that is not in the document text.
• Field-restricted search. Some search engines enable users to limit free text searches to a particular field within a stored data record, such as 'Title' or 'Author.'
• Boolean queries. Searches that use Boolean operators (for example, 'encyclopedia' AND 'online' NOT 'Encarta') can dramatically increase the precision of a free text search. The AND operator says, in effect, 'Do not retrieve any document unless it contains both of these terms.' The NOT operator says, in effect, 'Do not retrieve any document that contains this word.' If the retrieval list retrieves too few documents, the OR operator can be used to increase recall; consider, for example, 'encyclopedia' AND 'online' OR 'Internet' NOT 'Encarta'. This search will retrieve documents about online encyclopedias that use the term 'Internet' instead of 'online.' This increase in precision is very commonly counter-productive since it usually comes with a dramatic loss of recall.^[5]
• Phrase search. A phrase search matches only those documents that contain a specified phrase, such as 'Wikipedia, the free encyclopedia.'
• Concept search. A search that is based on multi-word concepts, for example Compound term processing. This type of search is becoming popular in many e-discovery solutions.
• Concordance search. A concordance search produces an alphabetical list of all principal words that occur in a text with their immediate context.
• Proximity search. A phrase search matches only those documents that contain two or more words that are separated by a specified number of words; a search for 'Wikipedia' WITHIN2 'free' would retrieve only those documents in which the words 'Wikipedia' and 'free' occur within two words of each other.
• Regular expression. A regular expression employs a complex but powerful querying syntax that can be used to specify retrieval conditions with precision.
• Fuzzy search will search for document that match the given terms and some variation around them (using for instance edit distance to threshold the multiple variation)
• Wildcard search. A search that substitutes one or more characters in a search query for a wildcard character such as an asterisk. For example, using the asterisk in a search query 's*n' will find 'sin', 'son', 'sun', etc. in a text.

Improved search algorithms[edit]

The PageRank algorithm developed by Google gives more prominence to documents to which other Web pages have linked.^[6] See Search engine for additional examples.

Software[edit]

The following is a partial list of available software products whose predominant purpose is to perform full-text indexing and searching. Some of these are accompanied with detailed descriptions of their theory of operation or internal algorithms, which can provide additional insight into how full-text search may be accomplished.

Proprietary software[edit]

• Locayta(rebranded to ATTRAQT in 2014)
• SAP HANA^[7]

References[edit]

1. ^In practice, it may be difficult to determine how a given search engine works. The search algorithms actually employed by web-search services are seldom fully disclosed out of fear that web entrepreneurs will use search engine optimization techniques to improve their prominence in retrieval lists.
2. ^Capabilities of Full Text Search SystemArchived December 23, 2010, at the Wayback Machine
3. ^Coles, Michael (2008). Pro Full-Text Search in SQL Server 2008 (Version 1 ed.). Apress Publishing Company. ISBN1-4302-1594-1.
4. ^B., Yuwono; Lee, D. L. (1996). Search and ranking algorithms for locating resources on the World Wide Web. 12th International Conference on Data Engineering (ICDE'96). p. 164.
5. ^Studies have repeatedly shown that most users do not understand the negative impacts of boolean queries.[1]
6. ^US 6285999, Page, Lawrence, 'Method for node ranking in a linked database', published 1998-01-09, issued 2001-09-04. 'A method assigns importance ranks to nodes in a linked database, such as any database of documents containing citations, the world wide web or any other hypermedia database. The rank assigned to a document is calculated from the ranks of documents citing it. In addition, the rank of a document is...'
7. ^http://www.martechadvisor.com/news/databases-big-data/sap-adds-hanabased-software-packages-to-iot-portfolio/

See also[edit]

• Pattern matching and string matching
• WebCrawler, first FTS engine
• Search engine indexing - how search engines generate indices to support full-text searching

I don’t know about you, but I deal with a lot of applications that require SQL Server as their back end database. I like SQL Server for many reasons. For starters, it is broadly supported by applications. But probably most importantly, is that I feel I can support it to a certain extent.

That is until I need to do a big change to the installation. Recently, I had to add a component to an existing SQL Server 2008 R2 database. The component was the SQL Full-Text Search and is required for System Center 2012, which I was performing as a new install. I definitely didn’t want to add another database, so the next option was to add the component to the existing central DB server. But, SQL Full-Text Search isn’t a default component that is installed. The fix was easy, but it’s not exactly what you may think.

My default reaction was to run the setup again from the DVD and add the component. I’ll save you the hassle — that doesn’t work. What we need to do is go into the Programs and Features applet and add the component to the existing installation. This step is shown in Figure A:

Figure A

This is where we can change the SQL Server components (click to enlarge)

Once we add features, we will then be prompted for the DVD for the setup to run. But, it’s important to note that it has to be launched from the Programs and Features applet. The feature can then be selected explicitly from the setup wizard to add the SQL Full-Text Search component as shown in Figure B

Windows Full Text Search

.

Figure B

The components that need to be added are selected here

The final step is to specify the instance where SQL Full-Text Search is to be installed. In my practice, I live in a world of default instantiation of SQL Server instances, and generally don’t put multiple instances on one Windows Server. Nonetheless, it is a supported configuration to add SQL Full-Text Search to one instance yet not another on a server that has multiple SQL Servers. This step is shown in Figure C below:

Figure C

The SQL Server instance is selected here.

Windows 7 Full Text Search

The process of adding a SQL Server Component, in this example, SQL Full-Text Search, is quite simple. But, it can be disconcerting to go and make big changes to a SQL Server that is in use, especially if you don’t have a DBA on staff to go to for specific expertise.

Ms Sql Full Text Search

Do you make frequent component changes to SQL Server? If so, what types? Also, do you change the default options to prevent changes like this down the road? Share your SQL Server tips below!