CP7202 ADVANCED DATABASES EBOOKS

CP7202 ADVANCED DATABASES

OBJECTIVES:
 To learn the modeling and design of databases.
 To acquire knowledge on parallel and distributed databases and its applications.
 To study the usage and applications of Object Oriented database
 To understand the principles of intelligent databases.
 To understand the usage of advanced data models.
 To learn emerging databases such as XML, Cloud and Big Data.
 To acquire inquisitive attitude towards research topics in databases.

UNIT I PARALLEL AND DISTRIBUTED DATABASES 9
Database System Architectures: Centralized and Client-Server Architectures – Server System
Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O Parallelism – Inter
and Intra Query Parallelism – Inter and Intra operation Parallelism – Design of Parallel Systems-
Distributed Database Concepts - Distributed Data Storage – Distributed Transactions – Commit
Protocols – Concurrency Control – Distributed Query Processing – Case Studies

UNIT II OBJECT AND OBJECT RELATIONAL DATABASES 9
Concepts for Object Databases: Object Identity – Object structure – Type Constructors –
Encapsulation of Operations – Methods – Persistence – Type and Class Hierarchies – Inheritance
– Complex Objects – Object Database Standards, Languages and Design: ODMG Model – ODL –
OQL – Object Relational and Extended – Relational Systems: Object Relational features in
SQL/Oracle – Case Studies.

UNIT III INTELLIGENT DATABASES 9
Active Databases: Syntax and Semantics (Starburst, Oracle, DB2)- Taxonomy- Applications-
Design Principles for Active Rules- Temporal Databases: Overview of Temporal Databases-
TSQL2- Deductive Databases: Logic of Query Languages – Datalog- Recursive Rules-Syntax and
Semantics of Datalog Languages- Implementation of Rules and Recursion- Recursive Queries in
SQL- Spatial Databases- Spatial Data Types- Spatial Relationships- Spatial Data Structures-
Spatial Access Methods- Spatial DB Implementation.

UNIT IV ADVANCED DATA MODELS 9
Mobile Databases: Location and Handoff Management - Effect of Mobility on Data Management -
Location Dependent Data Distribution - Mobile Transaction Models -Concurrency Control -
Transaction Commit Protocols- Multimedia Databases- Information Retrieval- Data Warehousing-
Data Mining- Text Mining.

XML Databases: XML-Related Technologies-XML Schema- XML Query Languages- Storing XML
in Databases-XML and SQL- Native XML Databases- Web Databases- Geographic Information
Systems- Biological Data Management- Cloud Based Databases: Data Storage Systems on the
Cloud- Cloud Storage Architectures-Cloud Data Models- Query Languages- Introduction to Big
Data-Storage-Analysis.
                                                                                    TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
 Select the appropriate high performance database like parallel and distributed database
 Model and represent the real world data using object oriented database
 Design a semantic based database to meaningful data access
 Embed the rule set in the database to implement intelligent databases
 Represent the data using XML database for better interoperability
 Handle Big data and store in a transparent manner in the cloud
 To solve the issues related to the data storage and retrieval

EBOOKS

 

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M.E COMPUTER SCIENCE AND ENGINEERING

REGULATION 2013

SEMESTER-II

CP7201 THEORETICAL  FOUNDATIONS OF COMPUTER SCIENCE

CP7201 THEORETICAL FOUNDATIONS OF COMPUTER SCIENCE 

OBJECTIVES:
 To review sets, relations, functions, and other foundations
 To understand propositional and predicate logics and their applications
 To understand lambda calculus and functional programming
 To understand graph structures and their applications
 To understand formal models of computation, computability, and decidability
UNIT I FOUNDATIONS 12
Sets – relations – equivalence relations – partial orders – functions – recursive functions –
sequences – induction principle – structural induction – recursive algorithms – counting –
pigeonhole principle – permutations and combinations – recurrence relations
UNIT II LOGIC AND LOGIC PROGRAMMING 12
Propositional logic – syntax – interpretations and models – deduction theorems – normal forms –
inference rules – SAT solvers – Davis Putnam procedure – binary decision diagrams – predicate
logic – syntax – proof theory – semantics of predicate logic – undecidability of predicate logic -
Normal form – unification – - inferences in first-order logic – logic programming – definite
programs – SLD resolution – normal programs – SLDNF resolution – introduction to Prolog

UNIT III LAMBDA CALCULUS AND FUNCTIONAL PROGRAMMING 12
Lambda notation for functions – syntax – curried functions – parametric polymorphism – lambda
reduction – alpha reduction – beta reduction – beta abstraction – extensionality theorem – delta
reduction – reduction strategies – normal forms – Church-Rosser Theorems – pure lambda
calculus – constants – arithmetic – conditionals – Iteration – recursion – introduction to functional
programming
UNIT IV GRAPH STRUCTURES 12
Tree Structures – Graph structures – graph representations – regular graph structures – random
graphs – Connectivity – Cycles – Graph Coloring – Cliques, Vertex Covers, Independent sets –
Spanning Trees – network flows – matching
UNIT V STATE MACHINES 12
Languages and Grammars – Finite State Machines – State machines and languages – Turing
Machines – Computational Complexity – computability – Decidability – Church's Thesis.
                                                                                                                           TOTAL : 60 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able
 To explain sets, relations, functions
 To conduct proofs using induction, pigeonhole principle, and logic
 To apply counting, permutations, combinations, and recurrence relations
 To apply recursive functions and lambda calculus
 To explain logic programming and functional programming principles
 To apply sequential structures, tree structures, and graph structures
 To explain computational models, computability, and complexity

REFERENCES:
1. Uwe Schoning, “Logic for Computer Scientists”, Birkhauser, 2008.
2. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer, 2003.
3. John Harrison, “Handbook of Practical Logic and Automated Reasoning”, Cambridge
University Press, 2009.
4. Greg Michaelson, “An introduction to functional programming through lambda calculus”,
Dover Publications, 2011.
5. Kenneth Slonneger and Barry Kurtz, “Formal syntax and semantics of programming
languages”, Addison Wesley, 1995.
6. Kenneth H. Rosen, “Discrete Mathematics and its applications”, Seventh Edition, Tata
McGraw Hill, 2011.
7. Sriram Pemmaraju and Steven Skiena, “Computational Discrete Mathematics”, Cambridge
University Press, 2003.
8. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
9. Norman L. Biggs, “Discrete Mathematics”, Second Edition, Oxford University Press, 2002.
10. Juraj Hromkovic, “Theoretical Computer Science”, Springer, 1998.
11. J. E. Hopcroft, Rajeev Motwani, and J. D. Ullman, “Introduction to Automata Theory,
Languages, and Computation”, Third Edition, Pearson, 2008.

EBOOKS: 

http://uploading.com/d3d1e6ba/1-Uwe-Schoning-Logic-for-Computer-Scientists-Birkhauser-2008-pdf

http://www.mediafire.com/download/jr7rorn0gmjb0o4/1.Uwe+Schoning%2C+%E2%80%9CLogic+for+Computer+Scientists%E2%80%9D%2C+Birkhauser%2C+2008.pdf

http://uploading.com/75m584cb/2-M-Ben-Ari-Mathematical-logic-for-computer-science-Second-Edition-Springer-2003-pdf

http://www.mediafire.com/download/u1hpsy8n6c91psq/2.M.+Ben-Ari%2C+%E2%80%9CMathematical+logic+for+computer+science%E2%80%9D%2C+Second+Edition%2C+Springer%2C+2003.pdf 

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