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CSCI 4144 > Midterm > Flashcards

Midterm Flashcards

1
Q

What are the major challenges to large corporations in terms of information management for supporting decision making?

A

The pyramid

  • Data Mining (Patern Discovery and Evaluation)
  • Data Exploration (OLAP)
  • Data Warehouses/Data Marts (Data selection)
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2
Q

What are the main limitations of conventional information systems, i.e. the DBMS technology, in terms of information queries?

A
  • Data is growing at a phenomenal rate (“the yotta world”)
  • Data types are more mixed and complicated
  • Data rich but information poor!
  • New strategies for Decision Supporting Systems (DSS) or BI (Business Intelligence) system

UNCOVER HIDDEN INFORMATION AND PATTERNS

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3
Q

Name three information process for Decision Support Systems (DSS)

A
  • On Line Transactional (information) Process (OLTP)
  • On Line Analytical (information) Process (OLAP)
  • Knowledge discovery from data (KD)
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4
Q

What is OLTP and what does it do?

A

On Line Transactional (information) Process (OLTP)

Query is viewed as a read-only transaction

Track/record/retrieve original data records of every day business operations for answering “what, when, where” type of questions: Operational databases (Relational DB and SQL)

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5
Q

What is OLAP and what does it do?

A

On Line Analytical (information) Process (OLAP)

Summarization, consolidation, and aggregation

Store & manipulate summaries of various groupings of original data records for answering “what happened to the business” type of questions - Analytical databases: Data warehouses and OLAP

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6
Q

What is KD and what does it do?

A

Knowledge discovery from data

Discover/analyze hidden patterns of abstractive information (knowledge) for answering “why and what to happen next” type of questions: Data Mining (DM)

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7
Q

As a common user of a DB system, such as Dal online or RBC online banking, which type of information process do you deal with & why?

A

On Line Transactional (information) Process (OLTP)

Students only need a ‘view read-only’ data from the database without any manipulation.

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8
Q

For the president of Dalhousie Univ. or the dean of FCS, what type of information they are interested in getting?

A

On Line Analytical (information) Process (OLAP)

The president and dean could benefit from aggregations of Dal’s data such as “total male and female students” or “total first year students”

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9
Q

What is the “data rich but information poor situation?

A

The abundance of data, coupled with the need for powerful data analysis tools

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10
Q

As a store manager of War-Mart or Superstore, what type of information you need to know all the time?

A 
????
Abstractive information (Knowledge discovery)
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11
Q

What is data?

A
  • raw measures
  • unprocessed
  • some relevancy
  • has not structure (per say)
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12
Q

What is information?

A
  • Structured data
  • Processed data that brings meaning
  • Information can be used to answer questions
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13
Q

What is knowledge?

A
  • Laws or rules
  • Generalized to higher levels
  • Corresponds to regularity patterns hidden in datasets
  • Explain what happened
  • Predict what’s next
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14
Q

Use examples to explain the differences between terms Data, Information and Knowledge. How does each term/concept link to business information queries according to three types of information processes?

A

Data –> OLTP
Information –> OLTP
Knowledge –> KD

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15
Q

Why can’t you ask questions and get answers from a dataset and why?

A

They are data, but not information!

You need information, that is, structured data, processed and provides meaning.

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16
Q

Why the IT industry needs to develop DM and DW considering that RDBMS/SQL are already available for storing and querying information?

A
  • Efficient for data retrieval
  • Not efficient for grouping large data sets
  • Difficult to use SQL do define complex queries
  • Analyzing data and exploring relationship are not part of the SQL vocabulary.
  • It is constrained to retrieve information from single database
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17
Q

Why and in which way DW model is more advanced than RDB model in supporting business management queries?

A
  • Integrated data from one or more disparate sources
  • Current and historical summarized data
  • -> Can created reports
  • -> Complex queries
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18
Q

What is data mining (DM)?

A

Finding unknown, valid and actionable knowledge (patterns and regularities) from large data.

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19
Q

What are the basic knowledge discovery tasks?

A
  • Classification
  • Association
  • Clustering
  • Generalization
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20
Q

What are the simple rules for choosing solution tools for getting different types of business information?

A

?????? (Slide 1.3)

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21
Q

What are the two general purposes of DM (or any scientific research)?

A

– Explanation: understanding/explaining about current behaviours

– Prediction: predicting for future outcomes

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22
Q

How the DM technologies are categorized?

A
  • Predictive (Classification and prediction)

- Descriptive (Clustering, summarization and association rules)

23
Q

Can you name three major DM tasks & what is each task about?

A
  • Classification is learning a function that maps (classifies) a data item into one of several predefined classes.
  • Clustering is a common descriptive task where one seeks to identify a finite set of categories or clusters to describe the data.
  • Summarization involves methods for finding a compact description for a subset of data.
24
Q

What is the Empirical Cycle Model (ECM) of scientific research, describe each stage of the process?

A
  1. Observation: we start with a number of observations.
  2. Analysis: we try to find patterns in these observations.
  3. Theory: if we have found some regularities, we formulate a theory (hypothesis) explaining the data.
  4. Prediction: our theory will predict new phenomena that can be verified by new observations.
25
Q

How are ML & DM associated with the ECM?

A

ML and DM both observe data to find patterns and make predictions.

26
Q

Why it says “a discovered knowledge only has temporary value”?

A

This is said because it can’t be proved until it is backup with actual stats.

27
Q

Why a discovered knowledge needs to be corroborated by statistics?

A

To prove it - state if the results are significant or not.

28
Q

What is the main difference and relationship between Information Retrieval (IR) and DM?

A

Information retrieval and data mining bot retrieve information

  • IR gets existing data
  • DM derives new information
29
Q

What are the main differences between statistics analysis and DM?

A
  • Stats forms the hypothesis first then observes and collect data
  • DM observes and collects data, then create hypothesis
30
Q

Give three reasons why data need to be processed for DW and DM tasks.

A
  • No quality data, not quality DM/DW results:
  • Incomplete
  • Noisy
  • Inconsistent
31
Q

What are the typical tasks for DP (provide a brief description for each)?

A
  • Data cleaning: fill in missing values, correct errors, smooth noisy data.
  • Data integration: integration of multiple DBs, data cubes, or files
  • Data transformation: Normalization and aggregation.
  • Data reduction: Obtains reduced representation in volume but produces the
    same or similar analytical results
32
Q

Choose a DM/DW project as example, such as from Dec/Thesis or from the Internet, to examine & explain about its DP tasks.

A

Data cleaning
Data integration
Data transformation
Data reduction

33
Q

Why it was said that businesses are the drivers of DW and OLAP technologies?

A

Because they are data rich, but information poor. Ability to have quick ad hoc queries:

  • Analytical information
  • Aggregation information data
  • Efficient management on enterprise-wide data
  • Focus on the usage of information/analytics (reports!!)
34
Q

For decision makers of a large enterprise to see big pictures of the organization and its business, what two general approaches for integrating information from different databases, can you describe each?

A

Data cleaning, transformation, integration techniques are applied:

– Ensure accurate data with consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

– When data is moved to the warehouse, it is converted

35
Q

What are the general DW properties which differ DW model from Relational DB model, and how they fit the main objectives of DW?

A

A decision support DB that is maintained separately from the organization’s operational DB.

  • Subject-oriented (sales, supplies, customers)
  • Integrated (multiple sources, files)
  • Time variant (historical data 5+ years - DB only 3 months)
  • Non-volatile (separate from actual databases)
36
Q

What are the 3 typical DW schemas (describe each)?

A

-Star schema (fact table)
- Snowflake schema
(normalized star schema)
- Fact constellations (multiple fact tables)

37
Q

What are the four OLAP operations for a data cube?

A
  • Roll up (summarize)
  • Drill down (reverse roll-up)
  • Slice (project)
  • Dive (select)
38
Q

What is the “Muti-Dimensional Space” model for DW/OLAP technology, and how it is described by the data cube lattice (know how to draw it)?

A

This logical space is also called a hypercube (data cube). Each dimension of the cube represents an aspect of the possible business events which is divided into discrete values representing attribute domain of the dimension.

39
Q

What are “Cuboids” in a MDS model? How to calculate the total number of cuboids in a DW? How to estimate the complexity of a DW design?

A

???????

40
Q

How different a DW model is compared with a conventional DB model and why?

A

DW is subject-oriented, whereas the DB model is just data store for retrieval not for information.

41
Q

What are the main differences between OLAP and OLTP process operations for answering users’ queries?

A
  • OLTP uses SQL queries

- OLAP Operations (tools) enable users to analyze multidimensional data interactively from multiple perspectives.

42
Q

What is the difference between logic dimensional space for OLAP analysis and the physical space for 3D computer graphics?

A

As distinguished from physical dimensions, which are based on angles and limited to three, logical dimensions have no such limits.

43
Q

How to define OLAP queries? (It may be answered based on the Starnet query model.)

A

????
Ad hoc business queries

creating dimensions (e.g. day, week, month, year)

44
Q

What is the visualization metaphor for displaying OLAP result of multiple logic space (data cube), and how to best use it?

A

?????????

45
Q

Use the demo example of wealth vs. health analysis (http://www.youtube.com/watch?v=jbkSRLYSojo) to explain how DW/OLAP may be able to support DM applications.

A

Process the data for the warehouse.

Generate cube to roll-up. drill-down, slice and dice.

46
Q

Both DW and DM technologies are about deriving new information from the stored operational data, what are the differences between them (Describe at least two aspects)?

A

?????????

47
Q

Name three major general DM takes. What general property they shared and what are different from each other?

A

????????

48
Q

Why it is said that association pattern mining is a backbone technology for CRM recommender system?

A

Because you are finding relationships between users’ purchases or viewing history is critical for recommendations (Amazon)

49
Q

What is frequent pattern analysis? How to define a rule may be derived from a frequent itemset (provide two examples)?

A

?????????

50
Q

What are the usefulness measures for association rules? Define each measure.

A

???????
Support rate: The percentage of transactions in D containing both X and Y.

Confidence rate: The percentage of transactions containing X also contain Y.

51
Q

What are the two major procedures of mining association rules in the Apriori Algorithm?

A

Find frequent itemsets

Generate association rules

52
Q

What is the apriori knowledge which may help AR mining?

A

???????
Each association rule is a piece of knowledge describing a statistics sound relationship between two particular item sets.

53
Q

How to estimate a search space given n unique items?

A

??????

2^n-1

54
Q

What is the Apriori knowledge/property, and why it is significant for the algorithm?

A

????

CSCI 4144 (3 decks)

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