Search engine indexing is the systematic storage of data that enables optimal speed and performance when finding content on the web. Without it, search engines would need to scan every document in every database, requiring considerable time and computing power. A central focus of computer science in general, is how quickly search engines find a word in the inverted index, the speed of finding an entry in a data structure, and how quickly it updates or removes content. After the indexer checks whether it is updating old content or adding new content, the issue of merging arises, whether multiple indexers can work asynchronously. Search engine index merging is similar in concept to the SQL Merge command and other merge algorithms that compare content for matching conditions. Storing content also requires data filtration or compression depending in part on how much computer storage is required to support the index. The additional computer storage required to store the index, as well as the considerable decrease in the time required for an update to take place, are trade offs for the time saved during information retrieval.

Several factors are important to consider in a search engine’s architecture. First, data enters the index using document parsing. Document parsing is the process of breaking the components of a document or media form into pieces, or tokens, for insertion into forward and inverted indices. Therefore, in the context of search engine indexing and natural language processing, parsing is more commonly referred to as tokenization. It is also sometimes called, word boundary disambiguation, tagging, text segmentation, content analysis, text analysis, text mining, concordance generation, speech segmentation, lexing, or lexical analysis. Often, the terms ‘indexing’, ‘parsing’, and ‘tokenization’ are interchangeable in corporate slang.

Over time, maintenance is required to sustain reliability in the face of issues like corruption of indices, isolation of bad data, dealing with faulty hardware, as well as the complications pertaining to the use of partitioning and replication.

Search engine architecture varies in index performance and methodology of index storage, to meet the various design factors. Types of indices include:

• Suffix trees that support linear time lookup, and extendable hashing. One example of suffix tree use is the identification of DNA sequences. Suffix trees require more storage, but alteration into suffix arrays can support data compression.
• Inverted indexes store a list of atomic search criterion in the form of hash tables or binary trees that enable decision making when performing operations with the use of algebraic structures.
• A citation index stores citations or hyperlinks between documents to support citation analysis, and is the subject of Bibliometrics. Most often used in the field of library and information sciences to examine referring documents that identify relationships, or differences and similarities.
• An Ngram index stores sequences of length of data to support other types of retrieval or text mining.
• Finally, document-term matrices employ latent semantic analysis, storing the occurrences of words in documents in a two-dimensional sparse matrix, used in combinatorics and network theory because of the low density of significant data.

Understanding the framework and processes of search engines can be a real benefit when customizing website characteristics. This is a classic example of how knowledge can empower the individual, the business, or the organization, and by empowering each, we all benefit.

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