MIS 4330- Chapter 13 – Flashcards
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Algorithm
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A process or set of operations in a calculation. Most commonly used in data mining is neural networks, decision trees, rules induction, genetic algorithm, classification, regression trees, memory- based reasoning, nearest neighbor, clustering, and etc.
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Attribute Hierarchy
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A top-down data organization that is used for two main purposes: aggregation and drill-down/ roll up aggression
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Business intelligence
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A comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and and analyze data with the purpose of generating and presenting information to support business decision making (business need).
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Cube Cache
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In multidimensional OLAP, the shared, reserved memory area where data cubes are held. This is also used to speed up data access.
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Dashboard
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A web-based system that presents key business performance indicators (KPIs) in a single, integrated view with clear and concise graphs.
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Data Analytics
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A subset of BI functions that encompasses a wide range of mathematical, statistical, and modeling techniques with the purpose of extracting knowledge from data.
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Data Cube
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The multidimensional data structure used to store and manipulate data in a multidimensional DBMS. These are static , meaning they must be created before they are used, so they cannot be created by an ad hoc query.
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Data Extraction
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A process used to extract and validate data from an operational database and external data sources prior to their placement in a data warehouse.
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Data Filtering
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A process used to extract and validate data from an operational database and external data sources prior to their placement in a data warehouse.
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Data Mart
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A small, single-subject data warehouse subset that provides decision support to small group of people.
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Data mining
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A process that employs automated tools to analyze data in a data warehouse and other sources and to proactively identify possible relationships and anomalies.
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Data Store
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The component of the decision support system that acts as a database for storage of business data and business model data.
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Data Warehouse
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A integrated, subject-oriented, time- variant, nonvolatile, collection of data that provides support for decision making, according to Bill Inmon, the acknowledged father of the data warehouse.
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Decision Support System (DSS)
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An arrangement of computerized tools used to assist managerial decision making within a business
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Dimension tables
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In a data warehouse, tables used to search, filter, or classify facts within a star schema. The fact table is in a one-to-many relationship with dimension tables.
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Dimensions
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In a star schema design, qualifying characteristics that provide additional perspectives to a give fact.
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Drill down
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Lower levels of aggregation. Primarily used in a decision support to focus on specific geographic areas, business types, needs, and etc...
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End-user presentation tool
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A data analysis tool that organizes and presents selected data complied by the end-user query tool.
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End-user query tool
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A data analysis tool that organizes and presents selected data complied by the end-user query tool
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Exploratory Analytics
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Data analysis that provides ways to discover relationships, trends and patterns among data.
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Extraction, transformation, and Loading (ETL)
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In data warehouse environment, the integrated processes of getting data from original sources into the data warehouse. This includes retrieving data from original data sources, manipulation the data into a appropriate form, and storing the data in the data warehouse.
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Fact Table
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The star schema that contains facts linked an classified through their common dimensions. A fact table is in a one-to-many relationship with each associated dimension table.
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Facts
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The measurements that represent a specific business aspect or activity. For example, sale figures are numeric measurements. facts are usually units, cost, prices, and revenues.
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KPIs (Key Performance Indicators)
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Quantifiable numeric or scale-based measurements that assess a company's effectiveness or success in reaching strategic and operational goals. Example: turnover rates, sales by promotions, earning per share, and etc.
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Master Data Management (MDM)
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A collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization.
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Materialized View
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A dynamic table that not only contain the SQL query command to generate rows but stores the actual rows. If this is created for the first time, they query is run and the summary rows are stored in the table. Rows are automatically updated when the base tables are updated.
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Metrics
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Numeric facts that measure a business characteristic of interest to the end user.
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Multidimensional Database Management System
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A system that uses proprietary techniques to store data in a matrix like arrays of n dimensions known to cubes.
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Multidimensional online analytical processing
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An extension of online analytical processing to multidimensional database management systems
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Online Analytical Processing (OLAP)
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Decision support system tools that use multidimensional data analysis techniques. This creates an advanced data analysis environment that support decision making, business modeling, and operations research.
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Partitioning
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The process of splitting a table into subsets of rows or columns
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Periodicity
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Information about the time span of data stored in a table, usually expressed as current year only, previous years, or all years.
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Portal
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A unified, single point of entry for information distribution
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Predictive analytics
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Advance statistical and modeling techniques to predict future business outcomes with great accuracy.
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Relational online analytical processing (ROLAP)
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Analytical processing functions that use relational database and familiar relational query tools to store and analyze multidimensional data.
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Replication
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The process of creating and imagine duplicate versions of a database. This is used to place copies in different locations and to improve accesss time and fault tolerance
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Roll Up
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An OLAP extension used with the GROUP BY clause to aggregate data by different dimensions. This is the exact opposite of drilling down the data.
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Slice and Dice
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The ability to cut slices off a data cube to perform a more detailed analysis
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Snowflake Schema
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A data modeling technique used to map multidimensional decision support data into a relational database. This is known to have a one-to-many relationship with one or more dimension tables.
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Very Large Databases (VLDBs)
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Databases that contain huge amount of gigabyte, terabyte, and petabyte ranges are not unusual.
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In business intelligence framework, data are captured from a production system and placed in ___? A) Decision support system B) Portal C) Data warehouse D) Dashboard
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C) Data Warehouse
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__ tools focus on the strategic and tactical use of information. A) Business B) Relational database management C) Business Intelligence D) Networking
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C) Business Intelligence
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From a data analyst's point of view, decision support data differ from operational data in three main areas: time span, granularity, and etc... A) Usability B) Dimensionality C) Transaction Processing D) Sparsity
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B) Dimensionality
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Operational data are commonly stored in many tables, and stored data represent information about give __ only. A) Transaction B) Database C) Table D) Concept
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A) Transaction
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___ can server as a test vehicle for companies exploring the potential benefits of a data warehouse A) Data networks B) Data mart C) Data cubes D) OLAPs
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B) Data mart
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The basic star schema has four components: facts, ___, attributes, and attribute hierarchies. A) Keys B) Relationships C) Cube D) Dimensions
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D) Dimensions
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In a start schema representation, a fact table is related to each dimension in a ____ relationship A) many-to-one B) many-to-many C) one-to-many D) one-to-one
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A) or C)
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Facts and dimension tables are related by ___ keys. A) Shared B) Primary C) Foreign D) linked
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C) foreign keys
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A ___ schema is a type of star schema in which dimension tables can have their own dimension tables. A) snowflake B) star flake C) dimension D) Matrix
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A) Snowflake
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Decision support data tend to be non-normalized, ___, pre-aggrated A) Unique B) Duplicated C) Optimized D) Sorted
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B) Duplicated
