Discovering quasi-equivalence relationships from database systems

Data mining examines large pre-existing databases in order to generate new information. There are various tasks included under Data mining and association rule mining is considered as one of the crucial tasks among its. They are in form of if-then kind of statements which help to find relationships among huge data which do not hold relationship with each other within a relational database or any other information repository. As there are many applications like market basket analysis, detection of fraud in web, medical diagnosis, census data, Customer Relationship Management of credit card business which uses association rules so it is possible to improve the process of Decision making. Security is required for individual transaction and for frequent itemsets when the database is partitioned horizontally among multiple sites. In this case, every site is interested in globally supported association rules without revealing its own local information. To fulfill this goal, We use a secure multi-party algorithm based on secure sum technique to simplify the operation of mining association rule when the database is horizontally partitioned among multiple sites. We are using a Frequent-Pattern (FP) growth algorithm to find frequent itemsets and try to reduce total computation time.

B2B 전자상거래와 같이 XML.을 이용한 정보교환이 확산되고 있으며 이에 따라 상호 교환되는 정보에 대하여 체계적이며 안정적인 저장관리가 요구되고 있다. 이를 위해 XML 응용과 데이터베이스 연계를 위한 다양한 연구가 관계형 데이터베이스를 중심으로 수행되었다. 그러나 계층구조를 갖는 XML 데이터를 2차원 테이블의 집합인 관계형 정보로 표현하는 관계형 데이터베이스로의 저장에는 본질적인 한계가 있어, 계층구조를 지원하는 객체지향 데이터베이스로의 저장이 요망된다. 이에 따라 계층구조를 갖는 XML 데이터를 객체지향 데이터 베이스로 지장하기 위한 모델링 방안이 요구된다. 본 논문에서는 객체모델을 토대로 DTD를 객체 지향 데이터베이스 스키마로 변환하기 위한 방법을 제안한다. 이를 위하여 먼저 DTD를 객체모델로 변환시키기 위한 객체변환 방안을 제시하고, 변환된 객체모델을 객체지향 데이터베이스 스키마로 변경시키기 위한 스키마 변환 방법을 제안했다. Information exchange on XML such as B2B electronic commerce is spreading. Therefore the systematic and stable management mechanism for storing the exchanged information is needed. For this goal there are many research activities for connection between XML application and relational database. But because XML data have hierarchical structures and relational database can store only flat-structured data, we need to store XML data in object-oriented database that support hierarchical structure. Accordingly the modeling methodology for storing XML data in object-oriented database is needed. In this parer, the transforming methods based on object model from XML DTD to object-oriented database schema is proposed. For this, we first introduce mapping methods that map XML DTD to object model and then we propose soem methods that transform from the object model to object-oriented database schema.

This paper compares two approaches to meeting the data handling requirements of Intermedia, a hypermedia system developed at the Institute for Research in Information and Scholarship at Brown University. Intermedia, though written using an object-oriented programming language, relies on a traditional relational database management system for data storage and retrieval. We examine the ramifications of replacing the relational database with an object-oriented database. We begin by describing the important characteristics each database system. We then describe Intermedia and give an overview of its architecture and its data handling requirements. We explain why and how we used a relational database management system and the problems that we encountered with this system. We then present the design of an object-oriented database schema for Intermedia and compare the relational and object-oriented database management system approaches.

Taking a byte out of big data

Privacy is very important in Data Mining. Association rule mining, is a important technique, has already been applied in a wide range of areas. Large databases cannot be secured by encryption or other techniques because of complexities. These security issues can be divided into two categories: data hiding and knowledge hiding. This paper deals with the problem of association rule mining which preserves the confidentiality of each database i.e. Knowledge hiding. Knowledge hiding is concerned with the sanitization of confidential knowledge from the data. There are many methods to solve this problem. The one, presented in the paper is called support-based and confidence-based blocking schemes. Apriori algorithm is used for mining rule from database. Knowledge hiding is achieved using ISL and DSR. The improvement over sensitive rule hiding is proposed to offer more accuracy and security with smaller negative impact. In ever changing business environment like enterprises e-businesses, the old fashioned disclosure and database inference protection techniques are not capable to ensure complete data privacy. Privacy is important for the on line disclosure of private information Organizations should be able to evaluate the risk of disclosing information and should adopt new more efficient approaches for information disclosure control, in order to maintain their competitive edge in the market. Not only the data but also the hidden knowledge in the data should be made secure. The sensitive information can be extracted in the form of association rules with association rule mining tools. But this can jeopardize the privacy of the customers. Some other rule could be very critical for the company itself such as buying patterns of very rich customers. For example, an Internet based company may give up selling hardware and may concentrate on selling books and videos. Therefore a rule relating the customer buying patterns of hardware may no longer be sensitive for that company. It is desirable to apply a trivial algorithm that hides data by deleting it randomly. Data Mining is the process of discovering new patterns from large data sets involving methods from statistics and artificial intelligence but also database management. Privacy has become an important issue in Data Mining. Many methods have been brought out to solve this problem. This paper focuses on the problem of association rule mining, which hides the knowledge from each database. This paper reviews the major method of knowledge hiding. Association rule mining, as a very important technique, has already been applied in a wide range of areas. Knowledge hiding is concerned with the sanitization of confidential knowledge from the data. As a result of association rule mining, many useful association rules will be discovered, but at the same time, many privacy rules will also be exposed which do not want others to know. To solve this, limit the mining process, in order to keep these sensitive rules being hidden. There are so many methods to solve this problem. The one that is covered in the paper is just one kind of them, called support-based and confidence-based blocking schemes. Algorithm used For Knowledge Hiding is ISL algorithm or DSR algorithm (2). The main disadvantage of a block in algorithm is the fact that the dataset, apart from the blocked values, is not distorted. Thus, an adversary can disclose the hidden rules by identifying those generating item sets that contain question marks and lead to rules with a maximum confidence that lies above the minimum confidence threshold. If the number of these rules is small then the probability of identifying the sensitive ones among them becomes high. To avoid this problem, a method for selectively removing individual values from a database is introduced to prevent the discovery of a set of rules, while preserving the data for other applications. The problem of building privacy preserving algorithms is considered for one category of data mining techniques, the association rule mining. New metrics is introduced in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining (1).

Scientific data management has recently become a critical research issue due to computerization and automation of scientific research procedures and the resulting explosion of electronic data. For proper management of scientific data, adequate data types must be provided for representing the semantics of scientific data directly. Issues such as large data volume and data evolution are common among scientific applications, so traditional database support, such as storage management, are also beneficial to them. Current approaches such as standardized data formats and adoption of traditional databases do not accommodate the data support requirements of existing scientific applications well. Object-oriented databases, on the other hand, seem to hold promise, by combining flexible data models with traditional database support. In this dissertation, we constructed an experimental platform for exploring the design space for a scientific data management system. With this experimental approach, we could dynamically examine possible architectures for data support against actual instances of scientific data and typical operations executed on them. As many of the dynamic features of existing scientific applications, such as data access patterns, are yet to be discovered, the platform also provides an opportunity to explore such features. Based on the observed potential of object-oriented databases for scientific data management, the GemStone object-oriented database management system was chosen for the baseline data management architecture of the platform. Among scientific applications where better data support is desired, a scientific persistent language and data analysis environment called NewS and applications using it were selected as a target for the study. Connecting NewS with GemStone provided a cost-effective experimental platform where we could investigate a variety of scientific applications with single implementation. We incorporated GemStone into the NewS environment in such a manner that we could use existing NewS applications without modifications for experiments. We describe the design and implementation of our platform, GemStone-based NewS, and experiments performed on the platform. At the end, we describe primary contributions of the work, and assess whether or not our approach was a productive initial step toward improved scientific data management.

Object databases have been introduced since 1987. The first version of ODMG standard appeared in 1991, and the new release 2.0 is from 1997. During ten year period, object database technology has received considerable attention and improvements. Today products are mature and proved their efficiency in many real applications (telecommunication, energy distribution, spatial data management, Web, etc.) where gigabytes of data are manipulated. The goal of this paper is to present two key subjects: (i) why the ODMG standard is crucial, and (ii) how to implement an open architecture to connect object and relational databases.

Data mining has made a significant impact on business and knowledge management in recent years. Data mining also known as KDD is a powerful new technology having great potential to analyze useful information stored in large data bases. Association rule mining is an active research area, used to find association and/or correlation among frequent item sets. Association rule mining discover frequent item sets with out considering total cost, quantity and number of items in a transaction. Utility based frequent item set mining is a new research area, which provide importance to sale quantity and price among items in a transaction. In this paper, we have proposed a novel approach for utility frequent item set mining. Our method mines novel frequent item sets by giving importance to items quantity, significance weightage, utility and user defined support. Our approach can be used to provide valuable recommendation to the enterprise to improve business utility.

New technologies have been continuously introduced in the e-Business environment since the emergence of e-commerce. For example, ebXML (Electronic Business eXtensible Markup Language) has been adopted instead of EDI (Electronic Data Interchange) for exchanging electronic documents, or RDB (Relational Database) is being replaced by OODB (Object Oriented DataBase) for storing data. In view of the fact that ebXML is based on the object-oriented concept, OODB is more efficient than RDB at XML-based e-business system. In this paper, we design document format and Database schema with ebXML concept. We designs the XML structure (instance) of OODB instead of table or field of RDBMS(Relational Database Management System). The important thing in Database designing is the classification rule to choose element or attribute among meaningful values. It is also important to define Database configuration rules or archiving rules. That is a key point to determine system performance and efficiency. By numerical analysis, we demonstrate that the proposed DB modelling actually achieves operational efficiency and improve resource utilization.

The Association rule mining is one of the recent data mining research. Mining frequent itemsets in relational databases using relational queries give great attention to researchers nowadays. This paper implements modified set oriented algorithm for mining frequent itemsets in relational databases. In this paper, the sort and merge scan algorithm SETM (Houtsma and Swami, IEEE 25–33 (1995)) [1] is implemented for super market data set which is further improved by integrating transaction reduction technique. Our proposed algorithm Improved SETM (ISETM) generate the frequent itemsets from the database and find its execution time. Finally the performance of the algorithm is compared with the traditional Apriori and SETM algorithm.

Content-based retrieval on a large multimedia database attracts the interest of many researchers. However, the database architecture needed for content-based retrieval is still a problem. Traditional relation database systems do not support high-dimension feature form content description and indexing, and is thus limited in its content-based retrieval function. Some systems do support high-dimension feature form content description and indexing, but lack descriptions and query expressions on media object content and relations. In this paper, we present our study results on query mechanism and proposed CbExpr - a powerful, flexible query expression mechanism on a media object. Based on CbExpr, we proposed GMA (general mediabase architecture) - a general architecture for management and content-based retrieval on large media databases. We also present VideoBase, a content-based video retrieval system, present as example of GMA. Basic thoughts, considerations, and definitions are presented in this paper, along with some details for implementation.

Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

Mining association rules in large databases is one of the most interesting data mining techniques in database communities. The number of association rules in a large database highly depends on rule’s support and confidence, and there might exist hundreds of rules in a very large database. The presentation of large number of rules in a very nice and noticeable way becomes highly challenging. In recent years researchers have developed several tools to visualize association rules. However, a large number of the tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up yet. We studied existing descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules. The proposed technique can be used to extract the discovered rules of a very large database very conveniently and efficiently.

Translating object-oriented database transactions into relational transactions

Assets are critical working tools in any organisation; their technical nature makes them significant for the effective outcome of strategic designs in today’s fast-growing technological age; hence, proper management of AIS is important for an increase in organisational performance. Such conventional approaches as relational databases or data warehousing are not entirely suitable for dealing with the problem intricacy, size and volatility of today’s connected data. This paper focuses on the assessment of the traditional and novel perspectives of data relationship management in AIS. It also compares traditional approaches like Relational and Object-oriented databases with new/Emerging technologies like Graph databases which optimise data complexity and semantic web technologies for better integration of the data. Also, the integration of advanced computational technologies such as machine learning and artificial intelligence improves predictive analysis and data mining to promote decision-making and organisational performance. Advanced technologies, such as blockchain and cloud applications, advance asset management even more because of their enhanced security, clarity, and expansiveness in approaching relevant databases. Blockchain enhances the features of decentralised record-keeping and immutability, while cloud solutions are scalable storage and computational resources. This paper considers problems like integration, data quality, data scalability and security and also provides some recommendations for further studies to solve these problems. Through examining these developments, the wish for the paper is to contribute to the improvement of asset management as well as decisions in this sphere in the context of growing concern and popularity of technologies.