ISOM Quiz III Chpt 9
Terms
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- The general activity of updating (i.e. inserting, modifying, and deleting), querying (i.e. retrieving), and presenting text and number data from databases for operational purposes
- Online transaction processing (OLTP)
- The general activity of querying (i.e. retrieving) and presenting text and number data from data warehouses (and data marts) for analytical purposes
- Online analytical processing (OLAP)
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Model information to support managers and business professionals during the decision-making process
Include On-line Analytical Processing (OLAP) Tools
Excel can be a DSS - Decision Support Systems (DSS)
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A system containing enterprise-wide information presented via simple and easy to use interfaces for quick insight and analysis by executives
Can be OLAP-tool based
E.g. digital dashboards - Executive Information Systems (EIS)
- Three quantitative models typically used by DSS
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-Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model (SOLVER)
-What-if analysis – checks the impact of a change in an assumption on the proposed solution (IF FUNCTION, SCENARIO MANAGER)
-Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output (GOAL SEEK)
- designed to access DW data and they are more capable of true analysis than standard reporting tools typically used to access relational operational data.
- BI tools (a.k.a. OLAP Tools)
- Most BI (OLAP) tools offer the following capabilities
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-Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information (PIVOT TABLES)
-Drill-down – enables users to get details, and details of details, of information
-Slice-and-dice – looks at information from different perspective - integrates information from multiple components and presents it in a unified display
- Digital Dashboard
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-Finding “interesting†patterns in large amounts of data
-The patterns should be:
accurate
meaningful
understandable
actionable
-Intersection of database management, machine learning (artificial intelligence), and statistics< - Data Mining
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-Also known as Market Basket Analysis or Association Rule Mining
-determines which objects or features appear together
finds correlations among variables
-E.g. Beer and diapers tend to be bought together - Affinity Grouping
- Association-rule mining discovers correlations among items within transactions
- Association Rule Mining A.K.A. Market Basket Analysis
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the fraction of transactions containing items X, which also contain items Y.
_______ (Xï‚®Y) = Count (transactions containing X and Y) / Count (transactions containing X) - Confidence
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the fraction of transactions that contain both X and Y items
_______ (Xï‚®Y) = Count (transactions containing X and Y) / Count (all transactions) - Support
- The support measures the ______ of the rule, so we are interested in rules with relatively high support
- significance
- The confidence measures the ______ of the correlation, so rules with low confidence are not meaningful, even if their support is high
- strength
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-gives companies ability to discover and utilize information they already own, and turn it into the knowledge that directly impacts corporate performance
-__ incorporates database management, data warehousing and data mining
-The term__ is a - Business Intelligence
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-Software that enables business users to see and use (analyze) large amounts of complex data.
-Query and Reporting Tools
for retrieving data from databases
-BI (OLAP) Tools
for retrieving data from data warehouses and/or data marts
-Data Mi - Business Intelligence Tools