Syllabus of Fifth Semester
Syllabus for AKTU Btech CS/IT Branch (DBMS WT DAA).
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- Database Management System (BCS 501)
- Web Technology (BCS 502)
- Design and Analysis of Algorithm (BCS 503)
- Data Analytics (BCS 052)
- Data Warehousing and Data Mining (BCS 058)
DataBase Management System (BCS 501)
| Unit | Topic |
|---|---|
| I | Introduction : Overview, Database System vs File System, Database System Concept and Architecture, Data Model Schema and Instances, Data Independece and Database Language and Interfaces, Data Definitions Language, DML, Overall Database Structure. Data Modeling Using the Entity Relationship Model: ER Model concepts, Notation for ER Diagram, Mapping Constraints, Keys, Concepts of SUper Keys, Candidate Key, Primary Key, Generalization, Aggregation, Reduction of an ER Diagrams of Tables, Extended ER Model, Relationship of Higher Degree. |
| II | Relational data Model and Language : Relational Data Model Concepts, Integrity Constraints, Entity Integrity, Refential Integrity, Keys Constraints, Domain Constraints, Relational Algebra, Relational Calculus, Tuple and Domain Calculus. Introduction on SQL: Characteristics of SQL, Advantage of SQL. SQL Data Type and Literals. Types of SQL Commands. SQL Operations and Their Procedure. Tables, Views and Indexes. Queries and Sub Queries. Aggregate Functions. Insert, Update and Delete Operations, Joins, Unions, Intesection, Minus, Cursors, Triggers, Procedures in SQL/PL SQL. |
| III | Data Base Design & Normalization : Functional dependencies, normal forms, first, second & third normal forms, BCNF, inclusion Dependence, loss less join decompositions, normalization using FD, MVD, and JDs, alternative approaches to database design. |
| IV | Transaction Processing Concept : Transaction System, Testing of Serializability, Serializability of shedules, Conflict & View Serializable Schedule, Recoverability, Recovery from Transaction Failures, Log Based Recovery, Checkpoints, Deadlock Handling. Distributed Database: Distributed Data Storage, Concurrency Control, Directory System. |
| V | Concurrency COntrol Techniques : Concurrency Control, Locking Tecniques for Concurrency Control, Time Stamping Protocols for Concurrency Control, Validation Based Protocol, Multiple Granularity, Multi Version Schemes, Recovery with Concurrent Transection, Case Study of Oracle. |
Web Technology (BCS 502)
| Unit | Topic |
|---|---|
| I | Introduction : Introduction and Web Development Strategies, History of Web and Internet, Protocols Governing Web, Writing Web Projects, COnnecting to INternet, Introduction to Internet services and tools, Introduction to client-server computing. Web Page Designing : HTML: List, Table, Images, Frames, forms, XML: Document types defination (DTD), XML schemes, Object Models, presenting and using XML, Using XML, Processors: DOM and SAX. |
| II | CSS : Creating Style Sheet, CSS Properties, CSS Styling (Background, Text Format, Controlling Fonts), Working with block elements and objects, Working and Lists and Tables, CSS Id and Class, Box Model (Introduction, Border properties, Padding properties, Margin properties) CSS Advanced (Grouping, Dimentions, Display, Positioning, FLoating, Align, Pseudo class, Navigation Bar, Image Sprites, Attribute sector), CSS Color, Creating page Layout and Site Designs. |
| III | Scripting : Java script: Introduction, documents, forms, statements, functions, objects, introduction to AJAX. Networking: Internet Addressing, InetAddress, Factory Methods, Instance Methods, TCP/IP Client Sockets, URL, URL connection, TCP/IP server Sockets, Datagram. |
| IV | Enetrprise Java Bean : Creating a JavaBeans, JavaBeans Properties, Types of beans, Stateful Session bean. Stateless Session bean, Entity bean. Node.js: Introduction, Environment Setup, REPL TErminal, NPM (Node Package Manager) Callbackes Concept, Events, Packaging, Express Framework, Resful API. Node.js with MongoDB: MongoDB Create Databse, Create Collection, Insert, delete, update, join, sort, query. |
| V | Servlets : Servlet Overview and rchitechture, Interface Servlet and the Servlet Life Cycle, Handling HTTP get Requests, Handling HTTP post Requests, Redirecting Requests to Other Resources, Session Tracking, Cookies, Session Tracking with Http Session. Java Server Pages (JSP): Introduction, Java Server Pages Overview, A First Java Server Page Example, Implicits Objects, Scripting, Standard Actions, Directives, Custom Tag Libraries. |
Design and Analysis of Algorithm (BCS 503)
| Unit | Topic |
|---|---|
| I | Introduction : Algorithms, Analyzing Algorithms, Comlexity of Algorithms, Groth of Functions, Performance Measurments, Sorting and Order Statistics - Shell Sort, Quick Sort, Merge Sort, Heap Sort, Comaparison of Sorting Algorithms, Sorting in Linear Time. |
| II | Advanced Data Structures : Red-Black Trees, B - Trees, Binomial Heao, Fibonacci Heaps, Tries, Skip List. |
| III | Divide and Conqure : with Examples Such as Sorting, Matrix Multiplication, Convex Hull and Searching. Greedy Methods with Examples Such as Oprtimal Reliablity Allocation, Knapsack, Minimum Spanning Trees - Prim's and Kruskal's Algorithms, Single Source Shortest Paths - Dijkstra's and Bellman Ford Algorithms. |
| IV | Dynamic Programming : with Examples Such as Knapsack. All Pair Shortest Paths - Warshal's and Floyd's Algorithms, Resouce Allocation Problem. Backtracking, Branch and Bound with Examples Such as Travelling Salesman Problem, Graph Coloring, n-Queen Problem, Hamiltonian Cycles and Sum of Subsets. |
| V | Selected Topics : Algebric Computation, Fast Furier Transform, String Matching, Theory of NP-Completeness, Approximation Algorithms and Randomized Algorithms. |
Data Analytics (BCS 052)
| Unit | Topic |
|---|---|
| I | Introduction to Data Analytics: Sources and nature of data, classification of data (structured, semi-structured, unstructured), characteristics of data, introduction to Big Data platform, need of data analytics, evolution of analytic scalability, analytic process and tools, analysis vs reporting, modern data analytic tools, applications of data analytics. Data Analytics Lifecycle: Need, key roles for successful analytic projects, various phases of data analytics lifecycle — discovery, data preparation, model planning, model building, communicating results, operationalization. |
| II | Data Analysis: Regression modeling, multivariate analysis, Bayesian modeling, inference and Bayesian networks, support vector and kernel methods, analysis of time series: linear systems analysis & nonlinear dynamics, rule induction, neural networks: learning and generalisation, competitive learning, principal component analysis and neural networks, fuzzy logic: extracting fuzzy models from data, fuzzy decision trees, stochastic search methods. |
| III | Mining Data Streams: Introduction to streams concepts, stream data model and architecture, stream computing, sampling data in a stream, filtering streams, counting distinct elements in a stream, estimating moments, counting oneness in a window, decaying window, Real-time Analytics Platform (RTAP) applications, Case studies: real time sentiment analysis, stock market predictions. |
| IV | Frequent Itemsets and Clustering: Mining frequent itemsets, market based modelling, Apriori algorithm, handling large data sets in main memory, limited pass algorithm, counting frequent itemsets in a stream, clustering techniques: hierarchical, K-means, clustering high dimensional data, CLIQUE and ProCLUS, frequent pattern based clustering methods, clustering in non- euclidean space, clustering for streams and parallelism. |
| V | Frame Works and Visualization: MapReduce, Hadoop, Pig, Hive, HBase, MapR, Sharding, NoSQL Databases, S3, Hadoop Distributed File Systems, Visualization: visual data analysis techniques, interaction techniques, systems and applications. Introduction to R: R graphical user interfaces, data import and export, attribute and data types, descriptive statistics, exploratory data analysis, visualization before analysis, analytics for unstructured data |
Data Warehousing and Data Mining (BCS 058)
| Unit | Topic |
|---|---|
| I | Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept |
| II | Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design, |
| III | Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction: Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. |
| IV | Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method — Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. |
| V | Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining. |