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In this chapter, the authors propose an ontology based approach to classify the anomalous events occurring in a number of hosts, thus filtering the interesting or non-trivial events requiring immediate attention from a set of events. An ontology is developed to structure the domain of anomaly detection. It expresses the semantic relationships among the attributes of an anomaly detection system and events collected by it. The system harnesses the reasoning capability of ontology and that of inference engine to make meaningful assumptions about anomaly events. This enables automatic classification of the reported anomalies based on the functionality and significance of the originating host as well as the associated system resource or parameter.

The Indian Materials Database (IMDB) is a national project aiming to develop a database through compilation of materials property data available in different laboratories in India. The database contains data on mechanical, corrosion, nondestructive evaluation, thermal and optical properties of a wide variety of materials. Selecting the appropriate data modeling technique is crucial for the successful deployment of such a database. Dimensional modeling is a logical design technique to present data in a standard, intuitive framework that allows for high-performance access. Dimensional modeling of data results in a "Star Schema", where the data constitutes a central fact table surrounded by dimension tables. This paper discusses the model and architecture of the material database using a "Snowflake Schema", which is a variation of "Star Schema", where some of the dimensions are normalized into multiple related tables. The database contains a central fact table linked to multiple dimensions namely, 1) Materials 2) Properties of materials 3) Details of experiments conducted on materials and 4) Source from which data is obtained.

The Indian Material Database (IMDB) is a national project aiming to develop a database through compilation of materials property data available in different laboratories in India. Selecting the appropriate data modeling technique is crucial for the successful deployment of such a system. Dimensional modeling is a logical design technique that seeks to present the data in a standard, intuitive framework that allows for high-performance access. Dimensional modeling of data results in a 'star schema', where the data constitutes a central fact table surrounded by dimension tables. This paper discusses the model and architecture of the material database using a 'snowflake schema' which is a variation of 'star schema', where some of the dimensions are normalized into multiple related tables. The database contains a central fact table linked to multiple dimension tables, each of which corresponding to one of the following dimensions 1) the materials 2) the material properties which are studied 3) specifications of the experiments conducted on materials and 4) the source from which data is obtained.