Top PDF Implementation of Efficient Keyword Search in Relational Databases

Implementation of Efficient Keyword Search in Relational Databases

Implementation of Efficient Keyword Search in Relational Databases

Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follow a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bilevel index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Search process; H.3.1 [Content Analysis and Indexing]: Indexing methods.
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An Implementation of Formal Semantics in the Formalism of Relational Databases

An Implementation of Formal Semantics in the Formalism of Relational Databases

AN IMPLEMENTATION OF FORMAL SEMANTICS IN THE FORMALISM OF RELATIONAL DATABASES A N I M P L E M E N T A T I O N O F F O R M A L S E M A N T I C S I N T H E F O R M A L I S M O F R E L A T I O N A L D A[.]

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EFFICIENT KEYWORD SEARCH IN RELATIONAL DATABASES

EFFICIENT KEYWORD SEARCH IN RELATIONAL DATABASES

Index is one way to access the data quickly. Indexes can be created on any relation of attributes. Queries that filter using those attributes can obtain related tuples randomly using the index, without having to check every tuple in turn. Index is analogous to using the index of a book to go directly to the every page on which the data we are looking for is found i.e. we do not have to read the complete book to find what we are looking for. Relational databases systems typically supply various indexing techniques, each one of which is optimal for some combination of relation size, typical access pattern, and data distribution. Indexes are usually not part of the database, as they are considered a detailed implementation and indexes are usually organized by the similar group that maintains the another parts of the database. It should be noted that efficient indexes use on primary and foreign keys can dramatically improve the query performance. Because number of tuples in a table and hash indexes result are constant time queries. In which we different techniques of used for analyze the performance.
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Volume 2, Issue 3, March 2013 Page 578

Volume 2, Issue 3, March 2013 Page 578

Today,most of theClient-Server applications rely on database as a data store for servicing requests from multiple clients.Data organization and management have become so complex and challenging in this electronic age of information.The database technologies have constantly evolved to meet these changing requirements by adopting object oriented programming concepts. Most of the applications use a Relational Database Management System (RDBMS) as their data store. The main theme of this paper is incorporating object-oriented programming concepts into existing relational databases. Object oriented programming concepts such as encapsulation, polymorphism and inheritance are enforced as well as database management concepts such as the ACID properties (Atomicity, Consistency, Isolation and Durability) which lead to the efficient integration. Mainly concepts like inheritance and polymorphism are employed.Nowadays, the necessity to support complex data in databases is intensified. Our main objective is to reduce the implementation overhead,complexity and the memory space required for storage when compared to the traditional databases.The object-oriented development paradigm has the advantage of being a more natural way to model the "real world" of the application domain.
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A Metadata Search Approach to Keyword Query in Relational Databases

A Metadata Search Approach to Keyword Query in Relational Databases

DataSpot [16] is a database search system using free-form queries similar to our approach. It represents database content in form of schema-less semi-structured graph called hyperbase. Nodes in hyperbase represent data objects (e.g., relations, tuples, and attributes) and edges represent associations between data objects. Query results are connected subgraphs of hyperbase containing all query keywords. Goldman et al. [17] proposed a simple query language with two sets of keywords in form of find x near y. Two sets of objects in a database are found and the result set is ranked based on distance between these two sets. A similar system is proposed by Yin et al. [18]. Their concept is to find the target objects related to source objects with AND and OR semantics. The system converts a database schema to a graph. At the query time, it extends shortest join paths to measure the strengths of their relationships. Mragyati [19] is the system to keyword searching and browsing on relational databases. The system maps query keywords to a database schema using metadata as four-level trees and translates answer trees to SQL. The ranking function can be based on user-specified criteria but the default ranking is based on the number of foreign-key constraints. It is similar to our work in supporting synonyms and metadata. However, the implementation does not handle queries with more than 2 solution paths. Dissimilar to the other approaches, Wheeldon et al. [6] proposed a system to keyword search over relational databases which indexed a relational database as virtual documents to querying and navigation. Their approach indexes textual content of each tuple as a web page and their foreign-key constraints are
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Efficient Prediction of Difficult Keyword Queries over Databases

Efficient Prediction of Difficult Keyword Queries over Databases

individual substance set contains properties like name, moniker, and memoir. The SemSearch informational collection is a subset of the informational collection utilized as a part of Semantic Search 2010 test [15]. The first informational collection contains 116 records with around one billion RDF triplets. Since the measure of this information set is to a great degree extensive, it sets aside a long opportunity to record what's more, run inquiries over this informational index. Subsequently, we have utilized a subset of the first informational index in our examinations. We to begin with evacuated copy RDF triplets. At that point, for each document in SemSearch informational index, we computed the aggregate number of particular question terms in SemSearch inquiry workload in the document. We chose the 20, out of the 116, records that contain the biggest number of inquiry watchwords for our tests. We changed over each particular RDF subject in this informational index to a substance whose identifier is the subject identifier. The RDF properties are mapped to characteristics in our model. The estimations of RDF properties that end with substring ―#type" demonstrates the kind of a subject. Thus, we set the element set of every substance to the link of the estimations of RDF properties of its RDF subject that end with substring ―#type". In the event that the subject of an element does not have any property that finishes with substring ―#type", we set its element set to ―UndefinedType". We have included the estimations of other RDF properties for the subject as characteristics of its element. We put away the data about every substance in a different XML record. We have evacuated the importance judgment data for the subjects that don't dwell in these 20 documents. The sizes of the two informational collections are very close; be that as it may, SemSearch is
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Schema and Design Free Keyword Search Interfaces for XML Databases

Schema and Design Free Keyword Search Interfaces for XML Databases

The extreme success of web search engines makes keyword search the most popular search model for ordinary users. As XML is becoming a standard in data representation, it is desirable to support keyword search[2],[4],[7] in XML database. It is a user friendly way to query XML databases since it allows users to pose queries without the knowledge of complex query languages and the database schema. Schema and Design Free Keyword search Interfaces (SDFKI) have been proposed as solutions to the above search problems for XML DBs. With SDFKIs, users do not need to knowschema details or a query language. For example, suppose auser wants to find the papers that Bob published aboutXML in the DB fragment in Fig. 1. The user submitsquery Bob XML. The SDFKI returns the answer, whichis the paper at node 6.Database administrators always use various normalization techniques to eliminate the redundancy in databases. Our Keyword search approach can be performed on the normalized database structures in order to overcome the redundancy problem and to retrieve the most relevant elements as results. Because of this SDFKI is schema independent approach we can adopt the same keyword search interface for any XML database to retrieve the results regardless of their schema implementations and updating. In this paper this feature is called as Schema and Design Independent approach. So our proposed interfaces not only schema but also design independent approaches. Consequently, an important requirement for keyword search is to rank the query results so that the most relevant results appear first. We propose a novel relevance oriented ranking scheme called XRank similarity which can capture the hierarchical structure of XML and resolve ambiguity in a heuristic way. Besides, the popularity of query results is designed to distinguish the results with comparable relevance scores. At last the final ranked list of results will be displayed to the user.
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Efficient Keyword-Based Searching Strategies for Linked Databases

Efficient Keyword-Based Searching Strategies for Linked Databases

Example 1: Person X is a new computer science student at Y College. She(X) is interested to learn more about this vast research field and decided to find information about research work of researchers at Z University. This example mainly specifies that users face the burden of accessing web of data is hard, because knowing the schema is must by using any query language means. And it is not correct way to think that by querying and accessing the web of data is typical task, because the web contains not only a collection of textual documents but also an interlinked data i.e., remembering the schema of linked data is not easy task so in this scenario keyword search is solution. By using keyword searching no need of remembering the query languages and database schema. Concerning these problems the question we deal with this is user requirement is expressed in terms of keywords, that query is either single or multiple keyword sets. Before a period of time keyword search is supported by some of semantic web search engines such as SWSE and SIGMA, they are limited to processing a simple list of keywords, in that user can refer. Whereas this approach deals with finding relationships among the keywords and display the results.
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Effective Method for Retrieval of Information and Relational Database by Keyword Search Mechanism

Effective Method for Retrieval of Information and Relational Database by Keyword Search Mechanism

Pattern based methodologies bolster keyword search over social databases utilizing execution of SQL summons [1]. These systems are blend of vertices and edges including tuples and keys (essential and outside key). There are a few systems are existed for mapping based methodologies. I. Find: DISCOVER is the strategies that numerous Information Retrieval approaches take after. Find enables its client to issue keyword questions with no information of the databases pattern or SQL[2].DISCOVER returns qualified joining system of tuples, which is set of tuples that are related in light of the fact that they join on their essential and remote keys, aggregately contain every one of the keywords of the inquiry. Find utilizes static advancement. In future, it applies on powerful improvement. DIS-COVER restores a monotonic score total capacity for positioning an outcome. S ii. Start: With the expanding of the content information put away in social databases, there is increment an interest for RDBMS to help keyword question search on content information. For the same existing keyword search strategy cannot satisfy the prerequisite of content information
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A Hybrid Keyword Search across Peer to Peer Federated databases

A Hybrid Keyword Search across Peer to Peer Federated databases

Our development of hybrid search [18] was primarily motivated by the need to search large legacy body of educational documents with additional metadata [25]. A chunk of unstructured data typically is a file containing those data. The format of the file can be pure text, Microsoft Word, Adobe Portable Document Format (PDF), PostScript, or any other format of binary or text document for which a program is able to read text. For information retrieval, indexing on the text data is essential to search a keyword within an appropriate time. In a search service based on file systems, text filtering may be necessary to support the various data formats. Through the filter, we can obtain the unified format documents—the pure texts—and the document indexing program is uniformly applied to those text data. The market leading commercial database systems— IBM DB2, Microsoft SQL Server, and Oracle—have integrated text management in their systems and their query languages include text search syntax [20, 11, 8]. The metadata may be structured or semistructured to describe the infor- mation content of the data. We use XML, a semistructured data format, as a description language for the metadata. This is because XML provides a unified format among heterogeneous databases. Similar to the case of unstructured data repositories, metadata storage can be based on low-level file systems. To extract the information from XML instances in files, parsing and matching programs are needed. The extracted metadata information is combined with the query result of content search against the unstructured data. The relational database management systems are able to keep XML instances in their relational tables by the mapping paradigm. XML-enabled relational databases [6, 3, 26] are use- ful for the metadata storage, because the mapping and querying features are embedded in the database management systems. Native XML databases have excellent features to manipulate XML instances, but they are document-centric storages.
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PERFORMANCE EVALUATION OF RELATIONAL KEYWORD SEARCH USING ENHANCED COHERENCY TECHNIQUE

PERFORMANCE EVALUATION OF RELATIONAL KEYWORD SEARCH USING ENHANCED COHERENCY TECHNIQUE

The implicit assumption of keyword search is, the search terms are related complicates the search process because typically there are many possible relationships between two search terms. This realization leads to little tension between the compactness and coverage of search results. This paper proves that DISCOVER finds without redundancy all relevant candidate networks by exploiting the structure of the schema.DISOVER operates on relational databases and facilitates information discovery on them. A high level representation of the architecture DISCOVER used to find the joining networks shown in figure 1.
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AUTHENTICATION AND ACCESS CONTROL FOR MULTI KEYWORD RANKED SEARCH OVER ENCRYPTED DATA-AMRSED

AUTHENTICATION AND ACCESS CONTROL FOR MULTI KEYWORD RANKED SEARCH OVER ENCRYPTED DATA-AMRSED

Keyword based search on encrypted data is a promising technique that offers the search facility over the encrypted cloud data. It can mainly be classified into two types: keyword based search on Public-key Encryption and keyword based search on Symmetric Encryption. One can adopt the inverted index TSet, which maps the keyword to the documents containing it, to accomplish efficient multi-keyword search for freebase datasets or large scale databases .Blind storage system achieve searchable encryption and mask the access pattern of the search user. However, only single-keyword search is supported. Here this propose a security analysis with trapdoor confidentiality system and provide an security in download side.
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Efficient, Effective, and Scalable Personalized Keyword Query Interpretation for RDF Databases.

Efficient, Effective, and Scalable Personalized Keyword Query Interpretation for RDF Databases.

On the other hand, for the problem of keyword query segmentation that has been inves- tigated for relational database, [74] proposed a segmentation on keyword query based on a technique called conditional random field. It is said to outperform the segmentation approach using hidden Markov model proposed in [48]. Because the primary advantage of CRF over HMM is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRF can avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRF outperforms both MEMMs and HMMs on a number of real-world sequence labeling tasks. The segment they defined is also limited to database terms, and both statistical model based approaches need a training data, which is the query logs that containing the correct labels of the query. CRF is used only as a scoring function. The next step is looking for an optimal segmentation among all possible segmentations. They first calculated all “maxterms ” using a O(n) algorithm, where n is the number of terms in the query), then the optimal segmentation for each such invalid segment is computed through a tree search procedure. [51], [48], and [74] are all extended versions of [50]. However, query segmentation over semi-structured data, like RDF, adds more challenges. An RDF data model is a highly connected graph model, which emphasize connec- tivity and relationship between entities. A desired segment is no longer restricted to database terms but more complex structures, which leads to the “Deep” segmentation problem.
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Fuzzy Multi-Join and Top-K Query Model for Search-As-You-Type in Multiple Tables

Fuzzy Multi-Join and Top-K Query Model for Search-As-You-Type in Multiple Tables

Abstract— A search-as-you-type system determines answers on-the-fly as a user types in a keyword query, character by character. There arises a higher need to know the support search-as-you-type on data residing in a relational DBMS. The existing work on keyword query focuses on to support type of search using the native database SQL. The leverage existing database functionalities is to meet high performance requirement to achieve an interactive speed. It uses auxiliary indexes that are stored as tables to increase search performance. But the main drawbacks in existing work were that it handle search as you type for databases for single table at the same time multiple tables were not taken into consideration. The Proposed work presents a Fuzzy Multi-Join technique to support multiple tables for search as-you-type in relational databases. Further the proposal presents a Top-K Query Search model to support ranking queries for search as-you-type in relational databases Top-k join queries are generated in relational query processors.
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A Searching Strategy to Adopt Multi-Join Queries Based on Top-K Query Model

A Searching Strategy to Adopt Multi-Join Queries Based on Top-K Query Model

Abstract— A search-as-you-type system determines answerson-the-fly as a user types in a keyword query, character by character. There arises a higher need to know the support search-as-you-type on data residing in a relational DBMS. The existing work on keyword query focuses on to support type of search using the native database SQL. The leverage existing database functionalities is to meet high performance requirement to achieve an interactive speed. It uses auxiliary indexes that are stored as tables to increase search performance. But the main drawbacks in existing work were that it handle search as you type for databases for single table at the same time multiple tables were not taken into consideration. The Proposed work presents a Fuzzy Multi-Join technique to support multiple tables for search as-you-type in relational databases. Further the proposal presents a Top-K Query Search model to support ranking queries for search as-you-type in relational databases Top- k join queries are generated in relational query processors.
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An efficient and improved performance for keyword searching in larger databases

An efficient and improved performance for keyword searching in larger databases

First, No-index Method one section is Using Like predicates: In SQL there is an in-built predicate LIKE which allows matching the keyword and retrieving that record. Second section is Using User-Defined Function (UDF): We can add the functions into the databases to examine whether a record contains the query keyword or not. Second, Index Based Method a database index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure. Indexes are used to quickly locate data without having to search every row in a database table every time a database table is accessed. Indexes can be created using one or more columns of a database table, providing the basis for both rapid random lookups and efficient access of ordered records.For making an inverted index table, firstly an single id is assign to keywords in alphabetic order which are in Table T. Afterwards inverted index table IT is created with records in form<key id,rec id> where key id is the id of keyword and rec id is id of record For a table T, Prefix table PT is created with <prefix, skey id, lkey id> where p represent the prefix of keywords, lkey id the largest string id of prefix p and skey id smallest string id of p. An example; TABLE II of inverted
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Chinese Keyword Search by Indexing in Relational Databases

Chinese Keyword Search by Indexing in Relational Databases

In this paper, we proposed a new method to realize IR-style free-form Chinese keyword search over rela- tional databases. The basic idea of this method is to create an index by extracting information from relations in a database. For a given query, we use the index to ob- tain the candidate tuples and calculate the similarity of between the query and each candidate tuple through im- proved ranking strategy. The Top-N results are retrieved by SQL selection statements for the natural join of rela- tions in the database. Extensive experiments were carried out to measure the performance of our method based on a real dataset. Experimental results show that the average elapsed time including Index-time and Result-time is less than 500 milliseconds for queries with 1 to 10 query words. When N ≥80, the average recalls and pr ecisions are higher than 50% and 60% respectively.
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A Survey on Fuzzy Based Fast Query for Data Retrieval

A Survey on Fuzzy Based Fast Query for Data Retrieval

ABSTRACT: Structured query language (SQL) is a classical way to access relational databases. Although SQL is powerful to query relational databases, it is rather hard for inexperienced users. A search-as-you-type system computes answers on-the-fly as a user types in a keyword query character by character. To support prefix matching, we use auxiliary tables as index structures and SQL queries to support search-as-you-type. We present solutions for both single-keyword queries and multi keyword queries. We extended the techniques in the case of fuzzy queries and proposed various techniques to improve query performance. However, to support fuzzy search, there may be multiple prefixes similar to the keyword. We call the nodes of these similar prefixes the active nodes for a keyword. Now we are going to use the fuzzy search to retrieve images. Data Indexer builds Keyword to Attribute Mapping for mapping keywords to attributes. Given a keyword query, Template Matcher suggests relevant templates based on the index structures.
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KEYWORD SEARCH IN RELATIONAL DATABASES

KEYWORD SEARCH IN RELATIONAL DATABASES

Keyword searching is an effective method for finding information in any computerized database. It can be classified into two types, one is schema based keyword search and other is graph based key word search. Keyword search has been applied to retrieve useful data in documents, texts, graphs, and even relational databases. In Relational keyword search(R- KWS), the basic unit of information is a tuple/record. In contrast to Keyword search on documents, results in Relational keyword search cannot simply be found by inspecting units of information (records) individually. Instead, results have to be constructed by joining tuples. R-KWS has benefits over SQL queries. First, it frees the user from having to study a database schema. Second, R-KWS allows querying for terms in unknown locations (tables/attributes). Finally, a single R-KWS query replaces numerous complex SQL statements. Keyword search can be classified into two types. One is schema based approach, other is graph based approach.
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MapReduce based Method for User Friendly XML Keyword Search

MapReduce based Method for User Friendly XML Keyword Search

In this section we showed the details of the proposed methods. Major problem associated with standard XML query processing tool is with their syntax which requires XML query languages like XPath and XQuery to find XML data. These approaches need a good knowledge of XML query languages which does not go well with a non-database expert user.To address these problems a user friendly approaches are introduced here.They are LCA and ELCA based search method [7] [20]. Since data is being growing day by day the LCA/ELCA approach alone cannot withstand users’ needs. So a distributed and parallel MapReduce framework is combined with this technique for fast searching. Top-k results are retrieved based on the ranking score after searching. The techniques used in this paper are described in detail in the following section.
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