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Database Toolset - BORA - Object Relationship Analysis Toolkit

BORA addresses the pressing problem of analyzing large volumes of non-numerical data accumulated in databases. Data Warehousing technology has increased sharply the volume and diversity of stored data. While numerical data is relatively easy to sort and analyze using modern OLAP tools, non-numerical data analysis provides a challenge. It raises the need for searching databases based on more complex information and logical parameters.

Such problems arise in all types of research and information gathering activities involving large volumes of data. BORA can be used in marketing, social work, law enforcement, medicine, law library science and other fields that use complex non-numerical information.

BORA provides an easy-to-use visual framework to model and analyze semantic relationships between information objects stored in a relational database.

How does it work?

BORA uses the Relationship Information Structures (RIS) method to analyze data with a complex relationship structure based on logical links among data objects.

RIS is a subset of objects connected with each other in a direct or indirect way (via other objects).

The RIS method includes the following steps:

  • dividing the whole set of objects into coherent components according to given criteria
  • providing their graphical representation and analysis based on special algorithms.

BORA contains the following sub-systems:

  • RIS modeling
  • Processing data and forming relationships arrays
  • RIS analysis and visualization

SIX Steps to understand RIS

  1. Defining analysis goals and objectives. Identifying main object and relationship classes.
  2. Analyzing input data structure. Identifying problems impacting data quality. Identifying ways of finding necessary object and relationship classes.
  3. Modeling. Identifying object and relationship classes and their attributes in the database structure.
  4. Processing data. Forming data and relationship arrays in the analytical database, based on the defined model.
  5. Visualization results.
  6. Conducting a visual and algorithmic analysis of graphs.

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