Data Modeling for Content-Based Support Environment (C-BASE): Application on Epilepsy Data Mining

Siadat, Mohammad-Reza; Hamid, Soltanian-Zadeh,; Fotouhi, Farshad; Eetemadi, Ameen; Elisevich, and Kost

doi:10.1109/icdmw.2007.92

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Aslandogan et al [4] proposed a logical model for representing graphic data using an E-R model, and Siadat, Chu et al [13,14] also put forward a data model for unstructured data based on an E-R method. In the content based multimedia retrieval systems, Marcus and Amato et al [15,16] proposed a multilayered data model within which layers separately represent semantic descriptions, lowlevel features and raw data.…”

Section: Related Workmentioning

confidence: 99%

A tetrahedral data model for unstructured data management

Wei

Liu

2010

Sci. China Inf. Sci.

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This paper proposes a tetrahedral data model for unstructured data management. The model defines the four components of unstructured data including: basic attributes, semantic characteristics, low-level features and raw data on its four facets, and the relations between these components. The internal implementation structure of the model and the data query language are designed and briefly introduced. This model provides a unified, integrated and associated description for different kinds of unstructured data, and supports intelligent data services such as associated retrieval and data mining. An example is given to demonstrate how to use the model for describing and manipulating data from a sample video base.With the rapid development of information technology, the quantity of unstructured data has increased dramatically, and accounts for about 80% of the total data in the world. Unlike structured data that is stored in traditional database systems and is described by normal data structures, unstructured data lacks the explicit semantic structure necessary for computerized interpretation [1]. The major types of unstructured data include text, graphics, image, audio and video data. Computer software requires annotations from human beings or machines in order to properly classify and manipulate this data. Hence, the management of unstructured data is far more complex than structured data, and traditional relational database systems are limited to the management of structured data.The management of unstructured data is a new focus in the data management area. Search engines, such as Google and Yahoo, provide fast searching based on keywords; content-based information retrieval technology implements queries using lowlevel feature matching; content management systems provide deep search functions for certain kinds of unstructured data; and ontology-based data query has been proposed. Moreover, traditional database management systems, such as Oracle and DB2, have been extended to support the storage and manipulation of unstructured data. However, most of the above systems are only adequate for handling limited kinds of unstructured data, and only provide limited data retrieval methods. To manage and manipulate large amounts of unstructured data, associated retrieval that is based on multiple retrieval methods and other intelligent data services such as data analysis and data mining are required. Since data in these systems is described either by descriptive text or by lowlevel features, the above intelligent data services cannot effectively be supported. Therefore, in order to

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