Enterprise search

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Enterprise search is the practice of making content from multiple enterprise-type sources, such as databases and intranets, searchable to a defined audience. [1]

Contents

"Enterprise search" is used to describe the software of search information within an enterprise (though the search function and its results may still be public). [2] Enterprise search can be contrasted with web search, which applies search technology to documents on the open web, and desktop search, which applies search technology to the content on a single computer.

Enterprise search systems index data and documents from a variety of sources such as: file systems, intranets, document management systems, e-mail, and databases. Many enterprise search systems integrate structured and unstructured data in their collections. [3] Enterprise search systems also use access controls to enforce a security policy on their users. [4]

Enterprise search can be seen as a type of vertical search of an enterprise.

Components of an enterprise search system

In an enterprise search system, content goes through various phases from source repository to search results:

Content awareness

Content awareness (or "content collection") is usually either a push or pull model. In the push model, a source system is integrated with the search engine in such a way that it connects to it and pushes new content directly to its APIs. This model is used when real-time indexing is important. In the pull model, the software gathers content from sources using a connector such as a web crawler or a database connector. The connector typically polls the source with certain intervals to look for new, updated or deleted content. [5]

Content processing and analysis

Content from different sources may have many different formats or document types, such as XML, HTML, Office document formats or plain text. The content processing phase processes the incoming documents to plain text using document filters. It is also often necessary to normalize content in various ways to improve recall or precision. These may include stemming, lemmatization, synonym expansion, entity extraction, part of speech tagging.

As part of processing and analysis, tokenization is applied to split the content into tokens which is the basic matching unit. It is also common to normalize tokens to lower case to provide case-insensitive search, as well as to normalize accents to provide better recall.

Indexing

The resulting text is stored in an index, which is optimized for quick lookups without storing the full text of the document. The index may contain the dictionary of all unique words in the corpus as well as information about ranking and term frequency.

Query processing

Using a web page, the user issues a query to the system. The query consists of any terms the user enters as well as navigational actions such as faceting and paging information.

Matching

The processed query is then compared to the stored index, and the search system returns results (or "hits") referencing source documents that match. Some systems are able to present the document as it was indexed.

See also

Related Research Articles

Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

A search engine is an information retrieval system designed to help find information stored on a computer system. The search results are usually presented in a list and are commonly called hits. Search engines help minimize the time required to find information and the amount of information which must be consulted, akin to other techniques for managing information overload.

An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction

Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few words.

<span class="mw-page-title-main">Desktop search</span>

Desktop search tools search within a user's own computer files as opposed to searching the Internet. These tools are designed to find information on the user's PC, including web browser history, e-mail archives, text documents, sound files, images, and video. A variety of desktop search programs are now available; see this list for examples. Most desktop search programs are standalone applications. Desktop search products are software alternatives to the search software included in the operating system, helping users sift through desktop files, emails, attachments, and more.

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.

Enterprise content management (ECM) extends the concept of content management by adding a timeline for each content item and, possibly, enforcing processes for its creation, approval, and distribution. Systems using ECM generally provide a secure repository for managed items, analog or digital. They also include one methods for importing content to bring manage new items, and several presentation methods to make items available for use. Although ECM content may be protected by digital rights management (DRM), it is not required. ECM is distinguished from general content management by its cognizance of the processes and procedures of the enterprise for which it is created.

Federated search retrieves information from a variety of sources via a search application built on top of one or more search engines. A user makes a single query request which is distributed to the search engines, databases or other query engines participating in the federation. The federated search then aggregates the results that are received from the search engines for presentation to the user. Federated search can be used to integrate disparate information resources within a single large organization ("enterprise") or for the entire web.

A search engine is an information retrieval software program that discovers, crawls, transforms, and stores information for retrieval and presentation in response to user queries.

Search engine indexing is the collecting, parsing, and storing of data to facilitate fast and accurate information retrieval. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science. An alternate name for the process, in the context of search engines designed to find web pages on the Internet, is web indexing.

Cyn.in is an open-source enterprise collaborative software built on top of Plone a content management system written in the Python programming language which is a layer above Zope. Cyn.in is developed by Cynapse a company founded by Apurva Roy Choudhury and Dhiraj Gupta which is based in India. Cyn.in enables its users to store, retrieve and organize files and rich content in a collaborative, multiuser environment.

<span class="mw-page-title-main">General Architecture for Text Engineering</span>

General Architecture for Text Engineering or GATE is a Java suite of tools originally developed at the University of Sheffield beginning in 1995 and now used worldwide by a wide community of scientists, companies, teachers and students for many natural language processing tasks, including information extraction in many languages.

<span class="mw-page-title-main">Windows Search</span> Desktop search platform by Microsoft

Windows Search is a content index desktop search platform by Microsoft introduced in Windows Vista as a replacement for both the previous Indexing Service of Windows 2000 and the optional MSN Desktop Search for Windows XP and Windows Server 2003, designed to facilitate local and remote queries for files and non-file items in compatible applications including Windows Explorer. It was developed after the postponement of WinFS and introduced to Windows constituents originally touted as benefits of that platform.

A concept search is an automated information retrieval method that is used to search electronically stored unstructured text for information that is conceptually similar to the information provided in a search query. In other words, the ideas expressed in the information retrieved in response to a concept search query are relevant to the ideas contained in the text of the query.

The following outline is provided as an overview of and topical guide to search engines.

intergator is an Information Access Platform and a product suite for Enterprise Search. It is a search engine designed for organizations’ internal systems knowledge management- and analytics platform. The newest iteration of the product focuses on Enterprise Search, Big Content Analytics, Knowledge Capturing and Social Intranet. It is developed by interface projects GmbH, a subsidiary of the interface business group with its head office in Dresden, Sachsen.

The Open Semantic Framework (OSF) is an integrated software stack using semantic technologies for knowledge management. It has a layered architecture that combines existing open source software with additional open source components developed specifically to provide a complete Web application framework. OSF is made available under the Apache 2 license.

Microsoft Azure Cognitive Search, formerly known as Azure Search, is a component of the Microsoft Azure Cloud Platform providing indexing and querying capabilities for data uploaded to Microsoft servers. The Search as a service framework is intended to provide developers with complex search capabilities for mobile and web development while hiding infrastructure requirements and search algorithm complexities. Azure Search is a recent addition to Microsoft's Infrastructure as a Service (IaaS) approach.

References

  1. ↑ Kruschwitz, Udo; Hull, Charlie (2017). "Searching the Enterprise". Foundations and Trends in Information Retrieval. 11: 1–142. doi:10.1561/1500000053.
  2. ↑ "What is Enterprise Search?".
  3. ↑ "The New Face of Enterprise Search: Bridging Structured and Unstructured Information" (PDF). Archived from the original (PDF) on 2015-10-28. Retrieved 2013-05-27.
  4. ↑ "Security Requirements to Enterprise Search: part 1 - New Idea Engineering".
  5. ↑ "Understanding Content Collection and Indexing".
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