Dissertation Knowledge Discovery In Databases

Dissertation Knowledge Discovery In Databases-59
Intelligent geographic information system (IGIS) is one of the promising topics in GIS field.It aims at making GIS tools more sensitive for large volumes of data stored inside GIS systems by integrating GIS with other computer sciences such as Expert system (ES) Data Warehouse (DW), Decision Support System (DSS), or Knowledge Discovery Database (KDD).

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In order to solve it, the proposed methodology has three main stages: part-based bioimage representation, semantic bioimage representation and biomedical knowledge discovery.

Each stage of methodology state-of-the-art methods from computer vision, image processing, machine learning and data mining will be explored to provide interpretable learning methods supported by high-performance computing.

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 spatial outlier detection is one of the most popular spatial data mining techniques which is used to detect spatial objects whose non-spatial attributes values are extremely different from those of their neighboring objects.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.This model is adapted to be applied to polygonal objects.The proposed model is applied to an existing project for supporting literacy in Fayoum governorate in Arab Republic of Egypt (ARE).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.The mission of KDD is to promote the rapid maturation of the field of knowledge discovery in data and data-mining.Member benefits include KDD discounts, KDD partner discounts, the latest information from KDD, and more.SIGKDD's mission is to provide the premier forum for advancement, education, and adoption of the "science" of knowledge discovery and data mining from all types of data stored in computers and networks of computers.SIGKDD promotes basic research and development in KDD, adoption of "standards" in the market in terms of terminology, evaluation, methodology and interdisciplinary education among KDD researchers, practitioners, and users.


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