Relational databases

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Implementing Semantic Query Optimization in Relational Databases

Implementing Semantic Query Optimization in Relational Databases

The semantic query optimization can further be extended to the distributed databases. In distributed databases not only optimization is difficult but also the query processing is complicated as compared to the relational databases. The semantic query optimization will be more difficult to be implemented in distributed databases as the databases are stored at different locations. So the generation of alternate query will not be an easy process. A very different and strong

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Efficient Practices of Privacy Preservation in Relational Databases

Efficient Practices of Privacy Preservation in Relational Databases

A. Access control mechanism for relational databases To define tuple-level permissions fine-grained access control like Oracle VPD [8] and SQL [9] are introduced in relational databases. Truman model [10] is introduced for evaluating user queries. A user query is modified by the access control mechanism and only authorized tuples are returned in this model. Column level access control allows queries to execute on the authorized column of the In relational data[8], [11] column level access control mechanisms allow queries to execute on authorized column, by replacing the unauthorized cell values by NULL values [12] cell level access control for relational data is achieved. For defining permissions on objects based on roles in an organization a Role-based Access Control (RBAC) was introduced. An RBAC policy configuration includes a set of Users(U), a set of Roles (R), and a set of Permissions (P). We assume that the selection predicates on the QI attributes define a permission for the relational RBAC model, [11] . UA is a user-to-role (U _ R) assignment relation and PA is a role to- permission (R _ P) assignment relation.
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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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Watermarking of relational databases: Survey

Watermarking of relational databases: Survey

Javier Franco-Contreras, Gouenou Coatrieux, et.al[33] proposed the robust reversible watermarking modilized by originally proposed under Vleeschouwer et al for the images protection of given relational databases. The propose scheme states relative angular position of the circular histogramic centeric mass of one numerical attribute for message displaying and embedding. It can be used for verifying databases authenticate and for traceability when identifying database origin after it has been modified. Evaluation of this scheme is done in terms of capacity, distortion, and robustness with two common database modifications against it. addition and removal of tuples. To that end that even model the impact of the embedding process and database modified for the probability even distributive to the center mass position. M. Kamran Sabah Suhail, and Muddassar Farooq[34] proposed a robust and efficient water marking scheme for relation to the database that enables to meet four
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Logical Querying of Relational Databases

Logical Querying of Relational Databases

There are advantages. Evaluating expressions and functional programming has already given us the support for a declarative way of parsing collections of objects. Since relational databases cease way to noSQL ones, we have to discover a good substitute for SQL language. Beginning with Java 8 lambda expressions, streams and method references, we have to search no more...

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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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Watermarking of Relational Databases Using Optimization Technique

Watermarking of Relational Databases Using Optimization Technique

ABSTRACT: Watermarking of the databases is a technique where we add additional non-detectable information is added into the original data. The main aim of implementing watermarking is for ownership proof and to prevent our precious data from being tampered with by an anonymous entity. The accuracy of the item is slightly degraded but the watermark acts as a seal that henceforth identifies the owner of the software. In our paper we present an effective watermarking technique geared for watermarking of relational databases. In our watermarking technique, use of a secret key is done, which play’s a very important role in protecting the owner’s data from being tampered with. Only if one has access to this secret key can the watermark be detected with high probability. The watermark can be easily and efficiently be maintained using insertion, deletion and the update of the database .
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O-ODM Framework for Object-Relational Databases

O-ODM Framework for Object-Relational Databases

Abstract —Object-Relational Databases introduce new features which allow manipulating objects in databases. At present, many DBMS offer resources to manipulate objects in database, but most application developers just map class to relations tables, failing to exploit the O-R model strength. The lack of tools that aid the database project contributes to this situation. This work presents O-ODM (Object-Object Database Mapping), a persistent framework that maps objects from OO applications to database objects. Persistent Frameworks have been used to aid developers, managing all access to DBMS. This kind of tool allows developers to persist objects without solid knowledge about DBMSs and specific languages, improving the developers’ productivity, mainly when a different DBMS is used. The results of some experiments using O-ODM are shown.
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Open Dialogue Management for Relational Databases

Open Dialogue Management for Relational Databases

This paper addresses a particular type of infor- mation-seeking dialogue in which the user’s goal is to select a tuple from a table. Tuples are identified by constraints, attribute-value pairs elicited from a user during the dialogue. A typical user, however, cannot supply all values with equal readiness. For example, attributes such as primary or foreign keys are irrelevant or unintelligible to users. This results in a vocabulary problem, a mismatch between sys- tem and user vocabulary (Furnas et al., 1987). Fur- thermore, tables differ in their relevance to users. Tables that contain little semantic information have less potential to address user goals. Dialogue sys- tems for relational databases often rely on manual pre-processing to select the attributes a typical user can most readily supply and identify the tables with the most relevance to basic user goals. An open dialogue system obviates this manual step by exploiting the database semantics.
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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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ABSTRACT: Object relational mapping from relational databases has become topic to be dealt with in the recent

ABSTRACT: Object relational mapping from relational databases has become topic to be dealt with in the recent

Relational databases [10] and object-oriented programming languages are based upon distinct paradigms, with technical conceptual and cultural incompatibilities. The set of those incompatibilities is commonly referred to as the object-relational impedance mismatch problem [8]. Object-relational mapping (ORM) frameworks address the impedance problem of software implementation level [6], providing the developer with ways to declare how each technical incompatibility should be treated [6]. ORM tools brings to the same level relational resources, such as data querying and object-oriented resources, such as inheritance, polymorphism, enabling to explore synergy between those constructs [4].
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Water Marking Relational Databases Using Optimization   Based Techniques

Water Marking Relational Databases Using Optimization Based Techniques

enforced. This is the case, for example, of weather data, stock market data, power consumption, consumer behavior data, medical and scientific data. Watermark embedding for relational data is made possible by the fact that real data can very often tolerate a small amount of error without any significant degradation with respect to their usability. For example when dealing with weather data, changing some daily temperatures of 1 or 2 degrees is a modification that leaves the data still usable. Watermarking of relational databases is very important point for the researches; because the free databases available on the internet websites are published without copyrights protection and the future will exploding problems. If the DB contains very important numerical data; To add watermark in the numerical and relational database without affecting the usefulness and the quality of the data.
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A Metadata Search Approach to Keyword Query in Relational Databases

A Metadata Search Approach to Keyword Query in Relational Databases

In this paper, a metadata search approach to ranked keyword search in relational databases is proposed. A semantic graph as a data model and strategies is used to find the optimal solutions. Additionally, the IR-style ranking function is applied to rank result tuples from answer graphs. The experiments confirmed that metadata in a semantic representation are useful for giving precise answers in both attribute-level and relation-level. Moreover, user terms can solve ambiguity problem of querying. Answer graphs are optimal solutions and suitable for mapping to corresponding SQL statements.
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Securing Relational Databases with an Artificial Immunity Features

Securing Relational Databases with an Artificial Immunity Features

Database security is considered one of the major computer science research trends because of its importance in maintaining the privacy, integrity, and confidentiality of data. Human immune system is a set of defense mechanisms that can be used to defend the body against diseases caused by pathogens. Artificial immune system is the artificial simulation of human immunity that can be applied to computer security applications. The main goal of this paper is to develop a database security system based on danger theory. Danger theory is one of the most recent algorithms of artificial immunity that can provide interactive features for securing relational databases. By merging the developed features of artificial immunity to the security system, the secrecy of the database can be maintained.
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Chinese Keyword Search by Indexing in Relational Databases

Chinese Keyword Search by Indexing in Relational Databases

For a database system, keyword search with the gener- al-purpose query engine uses user-supplied data to query the contents of string properties that store keywords, and then requires users to have the knowledge of database schema and a query language (say, SQL). Inspired by the success of free-form keyword search on information re- trieval (IR) and Web search engines, i.e., it is popular to users who need not know query languages and the struc- ture of underlying data. Researches of English keyword search with IR-style free-form in relational databases have been extensively studied since 2002[1-7]. [1] and [2] join tuples from multiple relations in the database to identify tuple trees with all the query keywords, for each enumerated join tree, both of them simply rank join se- quences according to the number of joins. ObjectRank system [3] applies authority-based ranking to keyword search in database modeled as labeled graph. [4] pro- posed a method (G-KS) for selecting the top-N candi- dates based on their potential to contain results for a given query. [5] succeeded in putting the model of com- puting similarities in IR into computing similarities be- tween a candidate answers and tuples in relational data- bases, the methods pay more attention on effectiveness of keyword search. [6] proposed a middleware free ap-
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An Efficient Fuzzy Data Clustering Algorithm for Relational Databases

An Efficient Fuzzy Data Clustering Algorithm for Relational Databases

This algorithm produces good results for categorical attributes of relational databases. The results have clearly proven that the proposed algorithm produces better results than Fuzzy k Modes in terms of parameters Accuracy, Precision, Recall and F-measure. The results clearly proved that the clusters generated were well separated. The algorithm can be tested on more datasets to test its efficiency.

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QUEST: A Natural Language Interface to Relational Databases

QUEST: A Natural Language Interface to Relational Databases

With recent progress in the structured databases, seman- tic parsing against these databases has attracted more and more research interests. Almost all of the existing seman- tic parsers need to be trained either with the direct super- vision such as logical forms (Kwiatkowski et al., 2011) or with the weak supervision such as question-answer pairs (Berant et al., 2013). However, in real application scenar- ios, collecting sufficient question-answer pairs to train the model remains a challenge. To address this, in this paper we proposed a rule-based semantic parser which consists of the following three components.
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GQL : a graphical query interface for relational databases

GQL : a graphical query interface for relational databases

The two employee tuple variables in figure 3 have been exploded, revealing that the name and salary attributes of both are in the target list and that simple selection conditions have be[r]

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Implementation of Efficient Keyword Search in Relational Databases

Implementation of Efficient Keyword Search in Relational Databases

It is widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1; p2; _ _ _ ; pl, against a relational database which can be modeled as a weighted graph, G(V;E). Here V is a set of nodes (tuples) and E is a set of edges representing foreign key references between tuples. Let Vi _ V be a set of nodes that contain the keyword pi. We study finding top-k minimum cost connected trees that contain at least one node in every subset Vi, and denote our problem as GST-k. When k = 1, it is known as a minimum cost group Steiner tree problem which is NPComplete. We observe that the number of keywords, l, is small, and propose a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3ln + 2l((l + log n)n +m)), where n and m are the numbers of nodes and edges in graph G. Our solution can handle graphs with a large number of nodes. OurGST-1 solution can be easily extended to support GST-k, which outperforms the existing GST-k solutions over both weighted undirected/directed graphs. We conducted extensive experimental studies, and report our finding.
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Similarity Measures for Relational Databases

Similarity Measures for Relational Databases

Persuaded that many applications will never reach the limitations of the widespread relational data model this article focuses on traditional relational algebra equipped with extra features that allow query relaxation and simi- larity searches. Although a large body of work has ad- dressed how to extend the relational data model to incor- porate cooperativity, neighbouring information, and/or or- derings (2; 3; 10; 11; 13), neither of them have succeeded to fit into the representational and operational uniformity of traditional relational algebra or even to reach a certain degree of generality.
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