A Study on the Management of Stock Data with an Object-Oriented Database Management System

Financial analysis of stock data usually involves extensive computation of large amount of time series data sets. To handle the large size of the data sets and complexity of the analyses, database management systems have been increasingly adopted for efficient management of stock data. Specifically, relational database management system is employed more widely due to its simplistic data management approach. However, the normalized two-dimensional tables and the structured query language of the relational system turn out to be less effective than expected in accommodating time series stock data as well as the various computational operations. This paper explores a new data management approach to stock data management on the basis of an object-oriented database management system (ODBMS), and proposes a data model supporting time series data storage and incorporating a set of financial analysis functions. In terms of financial stock data analysis, it specially focuses on a primitive set of operations such as variance of stock data. In accomplishing this, we first point out the problems of a relational approach to the management of stock data and show the strength of the ODBMS. We secondly propose an object model delineating the structural relationships among objects used in the stock data management and behavioral operations involved in the financial analysis. A prototype system is developed using a commercial ODBMS

데이터 추상화와 퍼지 관계를 이용한 근사적 질의응답에 관한 연구

본 논문은 데이터베이스에 존재하는 데이터 값들 사이의 유사성에 관한 지식을 이용하여 사용자가 요구한 정확한 답뿐 아니라 그와 유사한 답까지 제공해 줄 수 있는 근사적 질의처리 기법을 제시한다. 이를 위하여, 계량적인 방법에 해당하는 퍼지 관계와 비계량적인 방법에 해당하는 데이터 추상화를 하나로 통합한 유사성 표현 프레임웍을 제시하고 그를 이용한 지식 베이스를 설계한다

Cooperative Query Answering Based on Abstraction Database

Since query language is used as a handy tool to obtain information from a database, a more intelligent query answering system is needed to provide user-friendly and fault-tolerant human-machine interface. Frequently, database users prefer less rigid querying structure, one which allows for vagueness in composing queries, and want the system to understand the intent behind a query. When there is no matching data available, users would rather receive approximate answers than a null information response. This paper presents a knowledge abstraction database that facilitates the development of such a fault-tolerant and intelligent database system. The proposed knowledge abstraction database adopts a multilevel knowledge representation scheme called the knowledge abstraction hierarchy(KAH), extracts semantic data relationships from the underlying database, and provides query transformation mechanisms uslng query generalization and speclalization steps. In cooperation with the underlying database, the knowledge abstraction database accepts vague queries and allows users to pose approximate queries as well as conceptually abstract queries. Specifically, four types of vague queries are discussed, including approximate ｓｅｌｅｃｔion, approximate join, conceptual ｓｅｌｅｃｔion, and conceptual join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database anplication systems

Providing Approximate Answers Using a Knowledge Abstraction Hierarchy

Cooperative query answering is a research effort to develop a fault-tolerant and intelligent database system using the semantic knowledge base constructed from the underlying database. Such knowledge base has two aspects of usage. One is supporting the cooperative query answering process for providing both an exact answer and neighborhood information relevant to a query. The other is supporting ongoing maintenance of the knowledge base for accommodating the changes in the knowledge content and database usage purpose. Existing studies have mostly focused on the cooperative query answering process but paid little attention to the dynamic knowledge base maintenance. This paper proposes a multi-level knowledge representation framework called Knowledge Abstraction Hierarchy(KAH) that can not only support cooperative query answering but also permit dynamic knowledge maintenance, On the basis of the KAH, a knowledge abstraction database is constructed on the relational data model and accommodates diverse knowledge maintenance needs and flexibly facilitates cooperative query answering. In terms of the knowledge maintenance, database operations are discussed for the cases where either the internal contents for a given KAH change or the structures of the KAH itself change. In terms of cooperative query answering, four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems

실행공동체를 위한 지식관리시스템에서의 퍼지기반 신뢰도 측정

The importance of communities of practice (CoP) as an organizational informal unit for fostering knowledge trasfer and sharing gains a lot of attention from KM researchers and practitioners. Since most of CoPs are formulated online these days, the credibility or trustworthiness of knowledge contents circulated within a certain CoP should be considered thoroughly for them to be fully utilized safely. Here comes the need for an appropriate trust measuring methodology to determine the true value of knowledge given by unknown people through an online channel. In this paper, an improved trust measuring method is proposed using new trust variables such as level of degrees derived from the relationship among community users. In addition, activeness, relevance, and usefulness of the knowledge contents themselves, which are calculated automatically using a text categorization technique, are also used for trust measurement. The proposed framework incorporates fuzzy set and calculation concepts to help build trust matrices and models, which are used to measure the level of trust involved in specific knowledge concerned.본 연구는 대학 IT연구센터 육성지원사업의 연구결과로 수행되었음