CHAPTER-1.-FUNDAMENTAL-CONCEPTS-INTRODUCTION-TO-DATA-WAREHOUSE-SYSTEMS (1).pptx

CHAPTER I: FUNDAMENT AL CONCEPTS: INTRODUCTI ON TO DATA WAREHOUSE SYSTEMS  OBJECTIVES At the end of this chapter, the students should be able to:  Explain the fundamental concepts to data warehouse systems.  Discuss the historical overview of data warehousing, s...

CHAPTER I: FUNDAMENT AL CONCEPTS: INTRODUCTI ON TO DATA WAREHOUSE SYSTEMS  OBJECTIVES At the end of this chapter, the students should be able to:  Explain the fundamental concepts to data warehouse systems.  Discuss the historical overview of data warehousing, starting from the early achievements.  Understand the various ways of organizing and managing information for decision making use.  Review the history of decision support systems.  Learn briefly what is data warehouse and see why data warehousing is the viable solution  Describe the field of spatial and spatiotemporal data warehouses  Describe new domains and challenges that are being explored in order to answer the requirements of today’s analytical applications.  BUSINESS INTELLIGENCE  Comprises a collection of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for decision making.  Provide assistance to managers at various organizational levels for analyzing strategic information together with decision-support systems  THE DATA WAREHOUSE  The Data Warehouse is an integrated, subject- oriented, time-variant, non-volatile database that provides support for decision making.  Integrated: The Data Warehouse is a centralized, consolidated database that integrates data retrieved from the entire organization.  Subject-Oriented: The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.  THE DATA WAREHOUSE  The Data Warehouse is an integrated, subject- oriented, time-variant, non-volatile database that provides support for decision making.  Time Variant: The Warehouse data represent the flow of data through time. It can even contain projected data.  Non-Volatile: Once data enter the Data Warehouse, they are never removed. The Data Warehouse is always growing.  THE DATA WAREHOUSE A data warehouse system has the following characteristics:  It provides a centralized utility of corporate data or information assets.  It is contained in a well-managed environment.  It has consistent and repeatable processes defined for loading operational data.  It is built on an open and scalable architecture that will handle future expansion of data.  It provides tools that allow its users to effectively process the data into information without a high degree of technical support. On-Line Analytical Processing (OLAP) is an advanced data analysis environment that supports decision making, business modeling, and operations research activities. OLAP systems are designed to use both operational and Data Warehouse data.  Four Main Characteristics of OLAP: Use multidimensional data analysis techniques Provide advanced database support Provide easy-to-use end user interfaces Support client/server architecture Databases are designed using a conceptual model, such as the entity-relationship (ER) model, which aims at describing an application without taking into account implementation considerations. The resulting design is then translated into a logical model, which is an implementation paradigm for database applications. Finally, physical design particularizes the logical model for a specific implementation platform in order to produce a physical model.  Multidimensional modeling views data as consisting of facts linked to several dimensions.  Multidimensional Data Analysis Techniques  The processing of data in which data are viewed as part of a multidimensional structure.  Multidimensional view allows end users to consolidate or aggregate data at different levels.  Multidimensional view allows a business analyst to easily switch business perspectives.  A fact represents the focus of analysis (e.g., analysis of sales in stores) and typically includes attributes called measures.  Measures are usually numeric values that allow a quantitative evaluation of various aspects of an organization. For example, measures such as the amount or number of sales might help to analyze sales activities in various stores.  Dimensions are used to see the measures from several perspectives.  Hierarchies allow users to explore measures at various levels of detail. Examples of hierarchies are month– quarter–year in the time dimension and city–state– country in the location dimension.  Conceptual Modelling of Data Warehouses  Star schema: A fact table in the middle connected to a set of dimension tables  Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake.  Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation  Data analytics is the process of exploiting the contents of a data warehouse in order to provide essential information to the decision-making process.  Three Main Tools for Data Analytics  Data mining consists in a series of statistical techniques that analyze the data in a warehouse in order to discover useful knowledge that is not easy to obtain from the original data.  Key performance indicators (KPIs) are measurable organizational objectives that are used for characterizing how an organization is performing.  Dashboards are interactive reports that present the data in a warehouse, including the KPIs, in a visual way, providing an overview of the performance of an organization for decision-support purposes.  Spatial and Spatiotemporal Data Warehouse  Spatial data can represent either objects located on the Earth’s surface, such as mountains, cities, and rivers, or geographic phenomena, such as temperature, precipitation, and altitude.  Management of spatial data is carried out by spatial databases or geographic information systems (GISs).  Spatial databases are used to store spatial data located in a two- or three-dimensional space.  Spatial and Spatiotemporal Data Warehouse  Topological relationships between spatial objects, such as intersection, touches, and crosses, are essential in spatial applications.  Spatial data warehouses emerged as a combination of the spatial database and data warehouse technologies.  Spatial OLAP are Spatial data warehouses that provides improved data analysis, visualization, and manipulation.  CHAPTER I: FUNDAMENTAL CONCEPTS: INTRODUCTION TO DATA WAREHOUSE SYSTEMS THANK YOU!