2d barcode vb.net Full-text search: a promising solution in Java

Make Data Matrix ECC200 in Java Full-text search: a promising solution

Full-text search: a promising solution
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(its core) is split into two main operations: indexing the information into an efficient format and searching the relevant information from this precomputed index. From the definition, you can clearly see that the notion of word is at the heart of full-text search; this is the atomic piece of information that the engine will manipulate. Let s dive into those two different operations.
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Indexing is a multiple-step operation whose objective is to build a structure that will make data search more efficient. It solves one of the problems we had with our SQLbased search engine: efficiency. Depending on the fulltext search tools, some of those operations are not considered to be part of the core indexing process and are sometimes not included (see figure 1.5). Let s have a look at each operation:
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The first operation needed is to gather information, for example, by extracting information from a database, crawling the net for new pages, or reacting to an event raised by a system. Once retrieved, each row, each HTML page, or each event will be processed. The second operation converts the original data into a searchable text representation: the document. A document is the container holding the text representation of the data, the searchable representation of the row, the HTML page, the event data, and so on. Not all of the original data will end up in the document; only the pieces useful for search queries will be included. While indexing the title and content of a book make sense, it s probably Figure 1.5 The indexing process. Gather data, and unnecessary to index the URL pointing to the convert it to text. From the cover image. Optionally, the process might also text-only representation of the want to categorize the data; the title of an HTML data, apply word processing page may have more importance than the core of and store the index structure. the page. These items will probably be stored in different fields. Think of a document as a set of fields. The notion of fields is step 1 of our journey to solve one of our SQL-based search engine problems; some columns are more significant than others. The third operation will process the text of each field and extract the atomic piece of information a full-text search engine understands: words. This operation is critical for the performance of full-text search technologies but also for the richness of the feature set. In addition to chunking a sentence into words,
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this operation prepares the data to handle additional problems we ve been facing in the SQL-based search engine: search by object root or stem and search by synonyms. Depending on the full-text search tool used, such additional features are available out of the box or not and can be customized, but the core sentence chunking is always there. The last operation in the indexing process is to store your document (optionally) and create an optimized structure that will make search queries fast. So what s behind this magic optimized structure Nothing much, other than the index in the database we ve seen in section 1.2, but the key used in this index is the individual word rather than the value of the field (see figure 1.6). The index stores additional information per word. This information will help us later on to fix the order-by-relevance problem we faced in our SQL-based search engine; word frequency, word position, and offset are worth noticing. They allow the search engine to know how popular a word is in a given document and its position compared to another word.
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While indexing is quite essential for the performance of a search engine, searching is really the visible part of it (and in a sense the only visible feature your user will ever care about). While every engineer knows that the mechanics are really what makes a good car, no user will fall in love with the car unless it has nice curvy lines and is easy
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Figure 1.6 Optimizing full-text queries using a specialized index structure. Each word in the title is used as a key in the index structure. For a given word (key), the list of matching ids is stored as well as the word frequency and position.
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