Dissertation Knowledge Discovery In Databases

Dissertation Knowledge Discovery In Databases-8
The module Knowledge Discovery in Databases II covers advanced techniques to handle large data volumes, volatile data streams, complex object descriptions and linked data.These topics are also known as the three major challenges (Volume, Velocity, Variety) in Big Data Analysis.In other words in the traditional data set the values of each object are supposed to be independent from other objects in the same data set, whereas the spatial dataset tends to be highly correlated according to the first law of geography.

The module Knowledge Discovery in Databases II covers advanced techniques to handle large data volumes, volatile data streams, complex object descriptions and linked data.These topics are also known as the three major challenges (Volume, Velocity, Variety) in Big Data Analysis.

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The growing on variety, volume and velocity of public biomedical databases in the last years have generate an explosion of big data in biology and medicine.

Most of these databases comprise structural, molecular and genetic information from different kind of images acquisition modalities and associated metadata having a great potential, not yet exploited, as a source of information and knowledge which could impact biomedical research in different application fields.

In this way, the research proposal is addressing the problem of automatic extraction of knowledge from biomedical image collections.

Specifically, the goal is to devise methods to automatically find: visual patterns that compactly explain the visual richness of biomedical images, relationships between visual patterns, and relationships between visual patterns and their meaning in a particular biomedical context.

The module is directed at master students being interested in developing and designing knowledge discovery processes for various types of applications.

This includes the development of new data mining and data preprocessing methods as well as the ability to select the best suited established approach for a given practical challenge.

One of the main branches of IGIS is the Geographic Knowledge Discovery (GKD) which tries to discover the implicit knowledge in the spatial databases.

The main difference between traditional KDD techniques and GKD techniques is hidden in the nature of spatial data sets.

I think it’s useful to study data mining as it is presented as a process for making discoveries from data.

In this post you will explore authoritative definitions for “Data Mining” from textbooks and papers.

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