Exam Details
| Subject | data warehousing & data mining | |
| Paper | ||
| Exam / Course | mca | |
| Department | ||
| Organization | Gujarat Technological University | |
| Position | ||
| Exam Date | June, 2017 | |
| City, State | gujarat, ahmedabad |
Question Paper
1
Seat No.: Enrolment
GUJARAT TECHNOLOGICAL UNIVERSITY
MCA SEMESTER-IV • EXAMINATION SUMMER 2017
Subject Code: 640005 Date: 08-06-2017
Subject Name: Data Warehousing Data Mining
Time: 10:30 am 01:00 pm Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1
What is Data Warehousing? Discuss The Difference between OLAP and OLTP
07
Explain the Concept of hierarchy with Diagram
07
Q.2
Explain the following Term in Brief
1. Integrated
2. Data Mart
3. Data Transformation
4. Correlations
5. Class Comparison
6. FP
7. IF-THEN Rules
07
What is Pre-Processing? Why Data is Dirty Discuss How Handle Dirty Data With help of Pre-Processing
07
OR
Discuss Followings Short note
1. Three-Tier Data Warehouse architecture
2. Data Reduction
07
Q.3
Discuss Decision Tree Induction Algorithm
07
What is box-plot Data Analysis Explain With Example with Diagram
07
OR
Q.3
Explain in Details A.O.I
07
Discuss in Brief Mean, Median, Inter Quartile Range
07
Q.4
Discuss Apriori Algorithm with Proper Example
07
Discuss linear Regression and Non Linear Regression
07
OR
Q.4
What is Classification and Prediction? also Discuss Basics of Surprised and Unsurprised Learning
07
Explain Major Issues in Data Mining
07
Q.5
What is Cluster Analysis? Why Cluster Analysis Important for Data also Discuss Types of Cluster Analysis
07
Discuss Application of Financial Data Analysis
07
2
OR
Q.5
Explain and give the Important of Outlier analysis of Cluster with different Outlier Techniques
07
Discuss Application of Biological Data Analysis
07
Seat No.: Enrolment
GUJARAT TECHNOLOGICAL UNIVERSITY
MCA SEMESTER-IV • EXAMINATION SUMMER 2017
Subject Code: 640005 Date: 08-06-2017
Subject Name: Data Warehousing Data Mining
Time: 10:30 am 01:00 pm Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1
What is Data Warehousing? Discuss The Difference between OLAP and OLTP
07
Explain the Concept of hierarchy with Diagram
07
Q.2
Explain the following Term in Brief
1. Integrated
2. Data Mart
3. Data Transformation
4. Correlations
5. Class Comparison
6. FP
7. IF-THEN Rules
07
What is Pre-Processing? Why Data is Dirty Discuss How Handle Dirty Data With help of Pre-Processing
07
OR
Discuss Followings Short note
1. Three-Tier Data Warehouse architecture
2. Data Reduction
07
Q.3
Discuss Decision Tree Induction Algorithm
07
What is box-plot Data Analysis Explain With Example with Diagram
07
OR
Q.3
Explain in Details A.O.I
07
Discuss in Brief Mean, Median, Inter Quartile Range
07
Q.4
Discuss Apriori Algorithm with Proper Example
07
Discuss linear Regression and Non Linear Regression
07
OR
Q.4
What is Classification and Prediction? also Discuss Basics of Surprised and Unsurprised Learning
07
Explain Major Issues in Data Mining
07
Q.5
What is Cluster Analysis? Why Cluster Analysis Important for Data also Discuss Types of Cluster Analysis
07
Discuss Application of Financial Data Analysis
07
2
OR
Q.5
Explain and give the Important of Outlier analysis of Cluster with different Outlier Techniques
07
Discuss Application of Biological Data Analysis
07
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Subjects
- advance database management system
- advanced biopharmaceutics & pharmacokinetics
- advanced medicinal chemistry
- advanced networking (an)
- advanced organic chemistry -i
- advanced pharmaceutical analysis
- advanced pharmacognosy-1
- advanced python
- android programming
- artificial intelligence (ai)
- basic computer science-1(applications of data structures and applications of sql)
- basic computer science-2(applications of operating systems and applications of systems software)
- basic computer science-3(computer networking)
- basic computer science-4(software engineering)
- basic mathematics
- basic statistics
- big data analytics (bda)
- big data tools (bdt)
- chemistry of natural products
- cloud computing (cc)
- communications skills (cs)
- computer aided drug delivery system
- computer graphics (cg)
- computer-oriented numerical methods (conm)
- cyber security & forensics (csf)
- data analytics with r
- data mining
- data structures (ds)
- data visualization (dv)
- data warehousing
- data warehousing & data mining
- database administration
- database management system (dbms)
- design & analysis of algorithms(daa)
- digital technology trends ( dtt)
- discrete mathematics for computer science (dmcs)
- distributed computing (dc1)
- drug delivery system
- dynamic html
- enterprise resource planning (erp)
- food analysis
- function programming with java
- fundamentals of computer organization (fco)
- fundamentals of java programming
- fundamentals of networking
- fundamentals of programming (fop)
- geographical information system
- image processing
- industrial pharmacognostical technology
- information retrieving (ir)
- information security
- java web technologies (jwt)
- language processing (lp)
- machine learning (ml)
- management information systems (mis)
- mobile computing
- molecular pharmaceutics(nano tech and targeted dds)
- network security
- object-oriented programming concepts & programmingoocp)
- object-oriented unified modelling
- operating systems
- operation research
- operations research (or)
- pharmaceutical validation
- phytochemistry
- procedure programming in sql
- programming skills-i (ps-i-fop)
- programming skills-ii (ps-oocp)
- programming with c++
- programming with java
- programming with linux, apache,mysql, and php (lamp)
- programming with python
- search engine techniques (set)
- soft computing
- software development for embedded systems
- software engineering
- software lab (dbms: sql & pl/sql)
- software project in c (sp-c)
- software project in c++ (sp-cpp)
- software quality and assurance (sqa)
- statistical methods
- structured & object oriented analysis& design methodology
- system software
- virtualization and application of cloud
- web commerce (wc)
- web data management (wdm)
- web searching technology and search engine optimization
- web technology & application development
- wireless communication & mobile computing (wcmc)
- wireless sensor network (wsn)