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An approach to implementing a relational database system

An approach to implementing a relational database system

AN APPROACH TO IMPLEMENTING A RELATIONAL DATABASE SYSTEM H G Mackenzie (September 1979) I certify that, except where acknowledged in the text, the research contained in this report is entirely my own[.]

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Incremental Information Extraction Using Relational Database System & PTQL

Incremental Information Extraction Using Relational Database System & PTQL

From this labeled data, initial phrases referring to interactions are extracted. These phrases are then refined to compute consensus patterns and the resulting PTQL queries are generated by the query generator. However, training data are not always readily available for certain relationships due to the inherent cost of creating a training corpus. In that regards, our approach provides the pseudo relevance feedback driven approach that takes keyword based queries, and the PTQL query generator then finds common grammatical patterns among the top-k retrieved sentences to generate PTQL queries. 3.2 Parse Tree Database
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Anomaly Response System for Relational Database System Using Joint Administration Model

Anomaly Response System for Relational Database System Using Joint Administration Model

Recent years have witnessed a significant increase in the number of vulnerabilities in database system. Vulnerabilities in database system can be exploited by attackers to obtain unauthorized access to data stored database systems or to illegally execute malicious commands on host computers. Obtaining sensitive data is the real intension of the intruders. And another major challenging in the organization is to prevent the theft of data from the outsider as well as insider of the organization. Therefore, Intrusion Detection (ID) is the most critical technique for observing the database to detect the potential intrusion and take appropriate action on that anomalous request.
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Anomaly Response System for Relational Database System using Joint Administration Model

Anomaly Response System for Relational Database System using Joint Administration Model

Abstract: Data stored in the organization is one of its most important assets. The organizational data has to be protected from various threats that are posed from both insiders and outsiders. Not every user within the organization has the rights to view or modify this data. This restrictions on the access of data is to be implemented by the Database Management System (DBMS). But the current DBM) with their current security mechanisms may be of little help in providing security from internal threats. In this paper we propose a role based approach for anomaly detection system for security of the data from insiders based on the role assigned to them. Apart from detecting the anomalous data request the systems also needs to have a strong response policy. We propose three levels of response policies for responding to the anomalous requests. Additionally we propose a Joint Administration Model for providing security at the administrative level also.
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Performance Comparison of CRUD Operations in IoT based Big Data Computing

Performance Comparison of CRUD Operations in IoT based Big Data Computing

Compare the performance of MySQL, the relational database system, and MongoDB, the NoSQL. Add indexes to make MongoDB's queries more efficient and compare them one more time. Performance comparison compares the computation time after executing the CRUD (Create, Read, Update, Delete) operation. The data were tested with US aviation data for 2008. There are about 70,000 aviation data. Thus, we performed operations with 100, 200, 300, 800, and 1000 data, respectively.

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Survey on relational database watermarking techniques

Survey on relational database watermarking techniques

Digital watermarking has been in multimedia data use over the past years. Recently it has become applicable in relational database system not only to secure copyright ownership but also to ensure data contents integrity. Further, it is used in locating tampered and modified places. However, the watermarking relational database has its own requirements, challenges, attacks and limitations. This paper, surveys recent database watermarking techniques focusing on the importance of watermarking relational database, the difference between watermarking relational database and multimedia objects, the issues in watermarking relational database, type of attacks on watermarked database, classifications, distortion introduced and the embedded information. The comparative study shows that watermarking relational database can be an effective tool for copyright protection, tampered detection, and hacker tracing while maintaining the integrity of data contents. In addition, this study explores the current issues in watermarking relational database as well as the significant differences between watermarking multimedia data and relational database contents. Finally, it provides a classification of database watermarking techniques according to the way of selecting the candidate key attributes and tuples, distortion introduced and decoding methods used.
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Recoverable Watermarking for Distributed Databases

Recoverable Watermarking for Distributed Databases

 Robust and Reversible Watermarking (RRW): Genetic algorithm (optimization algorithm) is employed in the robust and reversible watermarking technique (RRW) to achieve an optimal solution that is feasible for the problem at hand and does not violate the defined constraints. An optimal watermark value is created through the GA and inserted into the selected feature of the relational database in such a way that the data quality remains intact. Mutual Information, a well-known information theory (concept), statistically measures the amount of information that one feature contains about the other features in a database. In RRW, mutual information is used to select a suitable (candidate) feature from the database for watermarking. In RRW, the knowledge of mutual information for every candidate feature is also employed to compute the watermark information. Thus, it is ensured that the data quality will not be affected.The RRW is based on recoverable watermarking numerical data of centralized relational databases[8]. Therefore implemented system provides database security in distributed environment providing recoverable watermarking.
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Hindi language as a graphical user interface to relational  database for transport system

Hindi language as a graphical user interface to relational  database for transport system

storing and retrieving the data. Databases are very hard to handle thus their interfaces is difficult in collaboration with users. Computers are used in accessing of information from database. Most of all the IT applications are accessing, storing, retrieving and analyzing the information from databases using the web as well as offline sources. Retrieving information from database needs knowledge and information of database languages like Structured Query Language (SQL) Everybody is not able to write SQL queries as they are not known to the syntax of SQL structure of the database. Thus, the idea of using natural language interface to provoke the development of new kind of processing method of Natural Language Interface to Database. Users do not need to learn any other formal language, they can execute query in their native language. Hindi language required this method to accept Hindi sentence as a query, process it and after execution make the result available to the user in the same language which is nothing but the Hindi Language Interface to DBMS.
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Modeling and querying alternative paths in Kuantan

Modeling and querying alternative paths in Kuantan

According to the JUPEM (Pahang branch), GIS application on Kuantan digital map for path finding is more directed to give one solution, that based on main road and minimal distance. For example, to find path from Teruntum Complex to Teluk Cempedak, the system compute the ‘best’ path according to the path that has high priority such as main road and has a minimal distance of length whereas, it could be has several paths that can be selected by user. This situation also has derived this study to provide the flexibility of path choices from a set of selected location.

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Information management at state highway departments: Issues and needs

Information management at state highway departments: Issues and needs

The base LRM is intended to be the spatial backbone of the relational database and the GIS. That is, it is the sole means to locate features that are a part of, or are located along, a particular road. At the same time, it supports translation among various other methods, thus allowing different stakeholders to access data using methods and procedures familiar to their personnel while at the same time providing a fixed underlying scheme for representing the topological and geometric aspects of the roadway network. The use of a base LRM also allows DOT units to incorporate additional databases into the organization-wide information framework at some future time without changing their existing spatial referencing systems. It is, therefore, highly expandable.
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Tools and techniques for the technological integration of multi-jazard post-incident assessment

Tools and techniques for the technological integration of multi-jazard post-incident assessment

Recent developments in XML (Extensible Markup Language) based Web Service architecture provide fertile ground for such a service-based architecture. Web services technology is derived from existing, distributed component technologies such as CORBA and DCOM technology (Caudwell et al. 2001) and offers a new framework for computing technology and standards, as well as the means to connect a network of distributed computing nodes (e.g. servers, workstations, desktop clients, and lightweight handheld clients) in a loosely coupled fashion. The loosely coupled architecture supports otherwise incompatible system technologies and creates composite services on demand. The loose coupling is also important for the implementation of complex collaborative applications needed during interagency participation. This framework is an integration of several key protocols and standards: XML, WSDL (Web Services Definition Language), SOAP (Simple Object Access Protocol), and UDDI (Universal Description, Discovery, and Integration). XML messages compliant with the SOAP specifications are exchanged between the requester and provider (SOAP 2000). The provider publishes a WSDL file that contains a description of the message and endpoint information to allow the requester to generate the SOAP message and send it to the correct destination (WSDL 2001). The service registry uses Universal Description, Discovery, and Integration protocol to enable service requestors to find and use Web services (UDDI 2002). The integration of web services (specific services offered by different agencies) into an Enterprise GIS framework (such as a disaster management system) offers tremendous new opportunities and capabilities for emergency management and hazard mitigation (Tsou and Buttenfield 2002).
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Relational to NoSQL Database Migration

Relational to NoSQL Database Migration

Konstantinou et.al[8] performed a study based on elasticity of non-relational database and compared Hbase and Cassandra during execution of read and update operations and concluded that Hbase provides high elasticity and fast reads while Cassandra was capable of delivering fast writes. Van der Veen JS et.al [9]compared Cassandra and MongoDB and proved that Mongodb is capable of providing high throughput, but mainly when it is used as a single server instance. On the other hand, the best choice for supporting a large distributed sensor network was considered Cassandra due to its horizontal scalability. Kashyap et.al[10] compared the performance, scalability and availability of HBase, MongoDB, Cassandra and CouchDB and summarized that Cassandra and Hbase shared similar behaviour, but the former scaled better, and that MongoDB performed better than Hbase by factors in hundreds for their particular workload.
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—EER diagram, Relational database

—EER diagram, Relational database

This system consists of regular entities, weak entities, 1: 1 binary relationship types, 1: N binary relationship types and M: N relationship types. The tool converts EER diagram, written in XML format into relation. The numbers of relation are 26 relations. Each relation has the primary key, attributes, and foreign keys. Table I shows relations of the system. After that, we specify relational database constraints for each relation. Then, we use this tool for generating SQL script. An example of script is shown in Fig. 7. This script includes relational database constraints according to user requirements. Finally, we execute this script with database. We found that this script generates relational database schema correctly.
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RELATIONAL DATABASE SERVICE ON CLOUD

RELATIONAL DATABASE SERVICE ON CLOUD

Abstract: The usefulness of the new transactional service namely “database-as-a-service” (DBaaS) also called as Relational Database Service, can help in future development and maintenance of online databases. A DBaaS promises to reduce much of the operational burden of provisioning, configuration, scaling, performance tuning, backup, security, and easy access control from the database users to the service operator, offering much reduction in overall costs to users. Early DBaaS efforts include Amazon RDS and Microsoft SQL Azure, and also the salesforce which are promising in terms of establishing the market need for such a service. But they do lack in some features which they do not address like these three important challenges: efficient multiple usage, easily scalable, and database privacy. We try to overcome these three challenges before outsourcing database software and many users can easily manage their data and also proves to be economical for many service providers. The key technical features of Relational Database service include: (1) a workload sharing approach to multiple users that identifies the workloads among users that can be easily located on a database server, achieving higher unification and better performance than existing approaches; (2) An adjustable security scheme that enables SQL queries to run over encrypted data, including ordering operations, aggregates, and joins. An underlying theme in the design of the components of Relational Cloud is the notion of workload awareness: by monitoring query patterns and data accesses, the system obtains information useful for various optimization and security functions, reducing the configuration effort for users and operators.
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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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Big Data Management: Towards Managing & Analy...

Big Data Management: Towards Managing & Analy...

Google. Its strengths include providing reliable shared online storage for large amounts of multiple sourced data through the Hadoop Distributed Filesystem (HDFS), analysis through Map Reduce (a batch query processer that abstracts the problem from disk reads and writes), and transforming data into a map-and-reduce computation over sets of keys and values (White, 2012). Map Reduce works well with unstructured or semi-structured data because it is designed to interpret the data at processing time (Verma, Cherkasova, & Campbell, 2012). While the Map Reduce system is able to analyze a whole ―big data‖ set and large samples in batch fashion, the Relational Database Management System (RDBMS) shows more strength in processing point queries where the data is structured into entities with defined format (i.e., structured data) as may occur in key-word or key characteristic sampling (White, 2012). Different from Map Reduce’s linear scalable programming that is not sensitive to the change of data size and cluster, RDBMS is a nonlinear programming which allows complex functions such as quadratic or cubic terms (Sumathi & Esakkirajan, 2007) in the model. RDBMS could be retrieved from http:// mysql-com.en.softonic.com/. Google’s success in text processing and their embrace of statistical machine learning was decoded as an endorsement that facilitated Hadoop’s wide-spread adoption. Hadoop, the open-source software can be downloaded from http://hadoop.apache.org/ releases.html. On the other hand, additional technologies and software are available for use with big data sets and samples. They represent reasonable alternatives to Hadoop, especially when data sets display unique characteristics that can be best addressed with specialized software.
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Review on Comparision of Relational Database Model and other Database Models

Review on Comparision of Relational Database Model and other Database Models

ABSTRACT:.As we know that memory is that palce in computer were we stores the data into the form of blocks same as when we store the data on large amount then we use database systems. A databse system is a software system that stores the data and also manages the data. It is a computer software system that interact with the users and analyse the data.The data model define how data is connected to each other and how they are processed and stored inside the system.In this paper we study about different-2 data models and relationship between these models.
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Telecommunications using Hadoop to Data Modeling Considerations

Telecommunications using Hadoop to Data Modeling Considerations

Hadoop. In addition, HiveQL supports custom map-reduce scripts to be plugged into queries. The language includes a type system with support for tables containing primitive types, collections like arrays and maps, and nested compositions of the same. The underlying IO libraries can be extended to query data in custom formats. Hive also includes a system catalog, Hive-Metastore, containing schemas and statistics, which is useful in data exploration and query optimization. In Facebook, the Hive warehouse contains several thousand tables with over 700 terabytes of data and is being used extensively for both reporting and ad-hoc analyses by more than 100 users.
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Application and Relational Database Co-Refactoring

Application and Relational Database Co-Refactoring

A software implemented by using an object-oriented language, which uses a relational database as a data storage, consists of four main components. There is the application itself, the database schema, stored data and the object-relational mapping. The software can be evolved by adding or removing entities, their properties or associations. These changes affects the database directly. Other kind of evolution is refactoring. Refactoring is a change made to the internal structure of software to make it easier to understand and cheaper to modify without changing its observable behavior [8]. The refactoring may affect not only the application but the database as well. The database has to evolve when the persistence layer of the application changes to fit the object-relational mapping used in the software. The common current solution is based on capabilities of object-relational mapping frameworks which are capable of creating a database schema according to the given source code or model. The process of evolution then proceeds as follows:
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Performance Analysis of Relational and Graph Database

Performance Analysis of Relational and Graph Database

like data and knowledge is very common nowadays. This requires need to store and control complex data. From most recent three decades, the relational databases are being utilized as a part of numerous associations of different natures, for example, Education, Health, Business and in numerous different applications. Relational databases demonstrate huge execution and are intended to deal with organized information with ACID (Atomicity, Consistency, Isolation, Durability) property to oversee information honesty however Relational databases can't process appropriately and oversee extensive measure of information productively. Present requirements are moving towards movement, UI, Internet of things, Browser based IDEs and so on. These innovations require constant reaction and substantial information store. Relational database frameworks recovers and oversees database in a forbidden shape, yet in current situation of dispersed extensive scale database those databases does not perform well. To beat the constraints of relational databases, and to cover the prerequisites of current applications has lead the advancement of new database advances, for example, Nosql databases. Among them The Graph Databases are most well known in the database group in light of the fact that in vogue ventures where a database is required, the extraction of commendable data depends on handling the graph like structure of the information. We are exhibiting an orderly correlation of relational and graph database models, for that we are utilizing MariaDB and Neo4j. Here we will look at the reasonableness of two classes of databases that is Relational database and graph database for putting away and quering datasets. We report consequences of estimations of scalability, query performance, and query expression of, utilizing synthetic datasets.
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