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Applying OLAP technology could solve important issues regarding databases storing information that is obtained from studding our cultural–historical inheritance. According to our research OLAP systems are not being implemented on the databases containing data about the bells. In this sense the proposed system bgBell/OLAP is unique. Its purpose is applying OLAP technology to exploring the data obtained from the client/server system for managing an audio and video archive for valuable Bulgarian bells. The developed system allows analyzing the sizes and the sounds of the bells by years, by locations, by creators, by types, etc.
In this article, we consider also discovering the association rules, being a data mining task (Kantardzic, 2003) in which the goal is to find interesting relationships between the attributes of the analyzed data. Once found, the association rules can be used for supporting decision making in different areas. An association rule shows the frequently occurring patterns of given data items in the database.
In numerous cases the algorithms generate a large number of association rules, often thousands or even millions. It is almost impossible for the end users to encompass or validate such a large number of association rules, limiting the results of the data mining is therefore helpful. The different approaches for reducing the number of the association rules are proposed. With some of them, only those rules that meet intended criteria are generated.
The constraint-based association rule mining aims to find all rules from given dataset, which satisfy the constraints required from the users. In (Ng, Lakshmanan, Han, & Pang, 1998) an algorithm for quickly discovering the association rules by including the constraints about items in the process for generation of the frequent itemsets, is proposed. By applying the constraints of the items and the combinations of the items, the items are chosen, consisting in the sake of the users, the association rules are generated from the obtained frequent itemsets.
Key Terms in this Chapter
Constraint-Based Association Rules: Mining aims finding all rules in a given dataset, which satisfy the constraints specified by the user.
Bell: A sound source (sound-making device). It can be used for many purposes: religious, musical, alarming, etc. From a musical point of view the bell is a percussion instrument. Usually the bell is molded by metal alloy in a form of a hollow cylinder or truncated cone with an open end that resonates as being struck. The chime is produced by a metal piece, named a clapper. The clapper can be movable while the body of the bell is immovable and vice versa. The bell chime is very rich of overtones. In an antiquity (and nowadays) the molding of bells was considered as art and high technology.
Audio/Video Archive: A collection containing records, documents, audio and/or video files, as well as other materials containing valuable information.
Digital Signal Processing: The mathematical manipulation of an information signal to modify or improve it in a sense for future processing, transmission and analysis.
Online Analytical Processing: Specific category software technologies enabling analysts, managers, administrators benefit from the study of the accumulated data. For this purpose, the users can perform quick, easy, interactive access to a variety of views of information that has been transformed from the original data so that it reflects the actual user requirements.
Client/Server System: Distributed software systems, which are usually divided into one server and multiple clients. The server of the system serves the clients and provides centralized access to the data. The communication between a client and a server is initiated by the client.
Data Warehouse: Data warehouse is defined as a subject-oriented, integrated, time-variant and non-volatile collection of data supporting decision making process. Usually, a data warehouse is considered as a database that provides access to all the information of an organization.