E- GOVERNANCE
INTRODUCTION TO E-GOVERNANCE AND E-GOVERNMENT
MODELS OF E-GOVERNANCE
SECURITY FOR E-GOVERNMENT
E-GOVERNMENT INFRASTRUCTURE DEVELOPEMENT
Applications of Data Warehousing and Data Mining in Government
CASE STUDY
E-GOVERNANCE PROJECT IMPLEMENTATION
CASE STUDY - E GOVERNANCE IN NEPAL
E-GOVERNANCE OLD QUESTION BANK
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E GOVERNANCE NOTE(BSCCSIT/BCA/IOE)

INTRODUCTION

  1. Data Warehousing:

    • Definition: Data warehousing involves the collection, storage, and management of data from various sources in a centralized repository called a data warehouse.
    • Purpose: The primary purpose of a data warehouse is to provide a consolidated and optimized platform for reporting and data analysis. It acts as a single, reliable source of truth for an organization's historical and current data.
    • Characteristics:
      • Subject-Oriented: Data warehouses are organized around specific subjects, such as sales, finance, or customer data.
      • Integrated: Data from different sources is integrated and transformed into a consistent format within the warehouse.
      • Time-Variant: Data warehouses store historical data, allowing for trend analysis and comparisons over time.
      • Non-volatile: Once data is stored in the warehouse, it is not typically altered or deleted, maintaining a reliable historical record.

  2. Data Mining:

    • Definition: Data mining is the process of discovering patterns, trends, correlations, and valuable insights from large datasets using various techniques, including statistical analysis, machine learning, and artificial intelligence.
    • Purpose: The main goal of data mining is to extract meaningful knowledge from data, uncover hidden patterns, and make predictions or classifications based on that knowledge.
    • Techniques:
      • Classification: Assigning items to predefined categories based on their characteristics.
      • Clustering: Grouping similar items together based on their features.
      • Association Rule Mining: Identifying relationships and associations between variables in the data.
      • Regression Analysis: Predicting numerical values based on historical data.
      • Anomaly Detection: Identifying unusual patterns or outliers in the data.

Relationship between Data Warehousing and Data Mining:

  • Data mining relies on the data stored in a data warehouse to discover patterns and generate insights. The integrated and organized nature of data in a data warehouse makes it a suitable source for data mining activities.
  • Data warehouses provide the historical and current data required for training and validating data mining models, ensuring that the results are based on reliable information.
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