MCA-227 Web Intelligence and Big Data Notes cover all 4 units of this Core Elective III paper in a 77-page book: 91 solved short answers, 49 long-answer exam questions, 11 solved numericals, 27 diagrams and 6 big data case studies. They follow the GGSIPU MCA syllabus that applies to batches from A.S. 2025-26 onwards.
Written by Prateek Talwar, Founder, Unnati Educations · Reviewed by Ritik Rawat, B.Tech, Senior Full Stack Developer · Checked against the MCA Scheme of Examination and Detailed Syllabus (BoS USIC&T, 28/07/2025) · Updated 14 October 2026 · Editorial policy
Scheme: MCA (02 Years), A.S. 2025-26 onwards | Applies to: batches admitted from A.S. 2025-26 | Verified against: MCA Scheme of Examination and Detailed Syllabus, BoS USIC&T 28/07/2025
MCA-227 Web Intelligence and Big Data at a glance: GGSIPU MCA Sem 3 credits, units and exam pattern
Units I and II of MCA-227 read like a search-engine course; Units III and IV like data engineering on NoSQL and Hadoop.
| Code and title | MCA-227 Web Intelligence and Big Data |
|---|---|
| Course and semester | MCA, third semester |
| Category | Core Elective III (choose one of MCA-227 to MCA-247) |
| Credits and hours | L 3, T/P 0, 3 credits; 10 hours per unit |
| Marks | Out of 100; pass mark 40. Internal and end-term split not printed in the MCA PDF |
| End-term pattern | Q1 compulsory, short answers from all units, 12 marks; then one of two questions per unit, 12 marks each (12 + 48 = 60 marks) |
| Units | 4 |
| Pre-requisites | Web Technology; Database Management Systems |
| Applies to | Batches from A.S. 2025-26 onwards |
| Paired lab | MCA-267 Lab based on Core Elective III: L 0, P 2, 1 credit |
Course outcomes climb from explaining web intelligence (BTL2) to adapting Hadoop, Pig and Hive (BTL6). It sits with the other GGSIPU MCA Semester 3 papers on one list.
MCA-227 Web Intelligence and Big Data Notes unit-wise: 91 solved short answers and 11 solved numericals across 4 units
These MCA-227 Web Intelligence and Big Data IPU notes run to 77 pages; each unit ends with exam questions and a Quick Revision Table. Notes for the other papers are under GGSIPU MCA notes. Sample pages from all four units are in the sample PDF above.
MCA-227 Unit 1: Introduction to Web Intelligence and Information Retrieval
Unit 1 (pages 5 to 23) covers the bow-tie web graph and web intelligence, then stop words, the Porter stemmer's five phases, the term-document matrix, the inverted index and the Boolean, vector space and probabilistic (BIM, BM25) models. It has 4 solved numericals, 22 short answers and 12 long-answer questions.
| Document | q·d | cos(q, d) | Rank |
|---|---|---|---|
| D3 (both terms) | 0.181 | 0.96 | 1 |
| D4 (short, one term) | 0.091 | 0.50 | 2 |
| D1 (long, one term) | 0.181 | 0.49 | 3 |
| D2 (no term) | 0 | 0.00 | 4 |
Quick look: cosine ranking for the query "web search" (from page 17)
- Document
- D3 (both terms)
- q·d
- 0.181
- cos(q, d)
- 0.96
- Rank
- 1
- Document
- D4 (short, one term)
- q·d
- 0.091
- cos(q, d)
- 0.50
- Rank
- 2
- Document
- D1 (long, one term)
- q·d
- 0.181
- cos(q, d)
- 0.49
- Rank
- 3
- Document
- D2 (no term)
- q·d
- 0
- cos(q, d)
- 0.00
- Rank
- 4
D4 beats D1 with a smaller dot product because cosine divides by vector length.
MCA-227 Unit 2: Web Search Engines, Multimedia Search, Text and Image Pre-processing and Classification
Unit 2 (pages 24 to 42) is the most calculation-heavy unit: the crawler and robots.txt, indexing, PageRank and HITS, multimedia search, then data cleaning, histogram equalisation, median filtering, linear regression, ID3, K-means and Naive Bayes, each worked by hand. It has 6 solved numericals, 23 short answers and 12 long-answer questions.
| Iteration | Cluster 1 | Cluster 2 | New centroids |
|---|---|---|---|
| 1 | {2, 3, 4} | {10 ... 30} | 3 and 18 |
| 2 | {2, 3, 4, 10} | {11 ... 30} | 4.75 and 19.6 |
| 3 | {2 ... 12} | {20, 25, 30} | 7 and 25 |
| 4 | {2 ... 12} | {20, 25, 30} | No change, stop |
Quick look: K-means on {2, 3, 4, 10, 11, 12, 20, 25, 30}, k = 2 (from page 38)
- Iteration
- 1
- Cluster 1
- {2, 3, 4}
- Cluster 2
- {10 ... 30}
- New centroids
- 3 and 18
- Iteration
- 2
- Cluster 1
- {2, 3, 4, 10}
- Cluster 2
- {11 ... 30}
- New centroids
- 4.75 and 19.6
- Iteration
- 3
- Cluster 1
- {2 ... 12}
- Cluster 2
- {20, 25, 30}
- New centroids
- 7 and 25
- Iteration
- 4
- Cluster 1
- {2 ... 12}
- Cluster 2
- {20, 25, 30}
- New centroids
- No change, stop
MCA-227 Unit 3: Understanding Big Data and Introduction to NoSQL
Unit 3 (pages 43 to 59) covers the 5 Vs, analytics types, the EMC lifecycle, ACID against BASE, Redis, MongoDB and Cypher examples and the Lambda Architecture. It has 6 case studies (Google to Aadhaar), 22 short answers and 13 long-answer questions.
| Choice | Trade-off | Examples |
|---|---|---|
| CP | May refuse requests during a partition | HBase, MongoDB |
| AP | May return stale data | Cassandra, DynamoDB |
| CA | Single site only | MySQL on one server |
Quick look: CAP theorem choices (from page 49)
- Choice
- CP
- Trade-off
- May refuse requests during a partition
- Examples
- HBase, MongoDB
- Choice
- AP
- Trade-off
- May return stale data
- Examples
- Cassandra, DynamoDB
- Choice
- CA
- Trade-off
- Single site only
- Examples
- MySQL on one server
MCA-227 Unit 4: Introduction to Hadoop, HDFS, MapReduce, Hadoop Ecosystem and Apache Spark
Unit 4 (pages 60 to 77) covers Hadoop daemons, HDFS read and write pipelines, rack awareness, a word-count trace, Hadoop Streaming, HBase, Sqoop, Flume, Pig, Hive and Spark RDDs. It has 13 code listings, 1 solved numerical, 24 short answers and 12 long-answer questions.
Quick look: Hadoop Streaming mapper in Python (from page 68)
#!/usr/bin/env python3
import sys
for line in sys.stdin:
for word in line.strip().split():
print(f"{word}\t1")Hadoop sorts these word 1 pairs by key before the reducer sums them.
MCA-227 exam corner: 91 short-answer solutions and 49 long-answer questions by unit
| Unit | Short answers (solved) | Long-answer questions | Numericals / case studies |
|---|---|---|---|
| I | 22 | 12 | 4 numericals |
| II | 23 | 12 | 6 numericals |
| III | 22 | 13 | 6 case studies |
| IV | 24 | 12 | 1 numerical |
| Total | 91 | 49 | 11 numericals, 6 case studies |
Exam questions in the MCA-227 notes by unit
- Unit
- I
- Short answers (solved)
- 22
- Long-answer questions
- 12
- Numericals / case studies
- 4 numericals
- Unit
- II
- Short answers (solved)
- 23
- Long-answer questions
- 12
- Numericals / case studies
- 6 numericals
- Unit
- III
- Short answers (solved)
- 22
- Long-answer questions
- 13
- Numericals / case studies
- 6 case studies
- Unit
- IV
- Short answers (solved)
- 24
- Long-answer questions
- 12
- Numericals / case studies
- 1 numerical
- Unit
- Total
- Short answers (solved)
- 91
- Long-answer questions
- 49
- Numericals / case studies
- 11 numericals, 6 case studies
The MCA-227 notes solve 91 short-answer questions and list 49 long-answer questions across 4 units, plus 11 worked numericals.
One question per unit, with answer pointers:
- Unit I: 1,000 documents, 50 relevant; the engine returns 40, 30 relevant. Find P, R, F1 and accuracy. Pointer: P 0.75, R 0.60, F1 0.667, accuracy 0.97, high only because TN = 940.
- Unit II: Min-max normalise 73,600 (min 12,000, max 98,000). Pointer: 61,600 / 86,000 = 0.716.
- Unit III: What is impedance mismatch? Pointer: nested objects split into flat tables and joined back; aggregates remove it.
- Unit IV: Managed against external Hive table. Pointer: dropping an external table removes only metadata; the HDFS files stay.
More practice sits in the MCA-227 important questions.
Aggregate data models in NoSQL: excerpt from page 50 of the MCA-227 notes
Excerpt from page 50
Types of NoSQL databases
NoSQL stores fall into four families. A key-value store keeps each key with an opaque value, like a hash table, and suits caching, sessions and shopping carts (Redis, DynamoDB, Riak, Memcached). A document store keeps a key with a JSON, BSON or XML document and suits catalogues, content and user profiles (MongoDB, CouchDB, Couchbase).
A column-family store maps a row key to column families and then to columns, which suits time-series, IoT and very heavy write loads (Cassandra, HBase, Google Bigtable). A graph store keeps nodes, relationships and properties, and suits social networks, recommendations and fraud detection (Neo4j, Amazon Neptune, JanusGraph).
What an aggregate is
An aggregate is a collection of related objects treated as one unit for data manipulation, consistency and distribution. The term comes from Domain-Driven Design and is used by Sadalage and Fowler in NoSQL Distilled. A typical aggregate is an order together with its line items, shipping address and payment details.
Key-value, document and column-family databases are called aggregate-oriented, because they store and fetch whole aggregates. Relational and graph databases are aggregate-ignorant.
Relational against aggregate: one order
A relational design splits a single order across five normalised tables: Customer, Orders, OrderItem, Address and Payment, joined back by keys whenever the order is read. The aggregate design stores the same order as one unit:
{ "orderId": 99,
"customer": { "id": 1, "name": "Rohan" },
"items": [ { "product": "MCA Guess Paper", "qty": 1, "price": 199 },
{ "product": "Big Data Notes", "qty": 2, "price": 149 } ],
"shippingAddress": { "city": "Delhi", "pin": "110018" },
"payment": { "mode": "UPI", "txnId": "abc123" } }Why aggregates help
An aggregate is a natural unit for distribution: it lives on one node, so sharding and replication are simple. Reading it needs no joins, so it is fast. Updates inside one aggregate are atomic, though transactions across aggregates are not guaranteed. It also matches the objects an application already uses, which cuts down impedance mismatch.
Do the MCA-227 notes cover the full GGSIPU syllabus?
Yes. Every official MCA-227 topic has its own section in the notes; no gaps. The notes also add BM25, the column-family model, Kappa architecture and Oozie.
| Unit | Official topics | Where in the notes | Status |
|---|---|---|---|
| I | Characteristics of the Web, Web structure, Retrieval vs. browsing, long tail, Web Intelligence benefits, ingredients and applications | Sections 1-5, pp. 6-10 | Covered |
| I | Document representation, Stemming, Term-Document Matrix, Standard Document collections, Boolean, Vector space and probabilistic models, Precision and Recall, Confusion Matrix | Sections 6-12, pp. 10-20 | Covered |
| II | Crawler, indexing, queries and ranking, scalability, link analysis; multimedia search | Sections 1-7, pp. 25-32 | Covered |
| II | Text and image pre-processing; Linear Regression, Decision Tree, K-means, Naive Bayes | Sections 8-10, pp. 32-39 | Covered |
| III | Big Data: what, why, applications, analytics, challenges | Sections 1-5, pp. 44-48 | Covered |
| III | NoSQL: aggregate, key-value, document, graph and schema-less models, solutions, case studies | Sections 6-12, pp. 48-56 | Covered |
| IV | Hadoop architecture, working, advantages; HDFS overview, features, architecture | Sections 1-5, pp. 61-65 | Covered |
| IV | MapReduce workflows, Hadoop streaming, HBase, Sqoop, Flume, PigLatin, Hive, Apache Spark | Sections 6-9, pp. 65-74 | Covered |
MCA-227 official topics mapped to the notes
- Unit
- I
- Official topics
- Characteristics of the Web, Web structure, Retrieval vs. browsing, long tail, Web Intelligence benefits, ingredients and applications
- Where in the notes
- Sections 1-5, pp. 6-10
- Status
- Covered
- Unit
- I
- Official topics
- Document representation, Stemming, Term-Document Matrix, Standard Document collections, Boolean, Vector space and probabilistic models, Precision and Recall, Confusion Matrix
- Where in the notes
- Sections 6-12, pp. 10-20
- Status
- Covered
- Unit
- II
- Official topics
- Crawler, indexing, queries and ranking, scalability, link analysis; multimedia search
- Where in the notes
- Sections 1-7, pp. 25-32
- Status
- Covered
- Unit
- II
- Official topics
- Text and image pre-processing; Linear Regression, Decision Tree, K-means, Naive Bayes
- Where in the notes
- Sections 8-10, pp. 32-39
- Status
- Covered
- Unit
- III
- Official topics
- Big Data: what, why, applications, analytics, challenges
- Where in the notes
- Sections 1-5, pp. 44-48
- Status
- Covered
- Unit
- III
- Official topics
- NoSQL: aggregate, key-value, document, graph and schema-less models, solutions, case studies
- Where in the notes
- Sections 6-12, pp. 48-56
- Status
- Covered
- Unit
- IV
- Official topics
- Hadoop architecture, working, advantages; HDFS overview, features, architecture
- Where in the notes
- Sections 1-5, pp. 61-65
- Status
- Covered
- Unit
- IV
- Official topics
- MapReduce workflows, Hadoop streaming, HBase, Sqoop, Flume, PigLatin, Hive, Apache Spark
- Where in the notes
- Sections 6-9, pp. 65-74
- Status
- Covered
Official source: MCA (02 Years) Scheme of Examination and Detailed Syllabus, A.S. 2025-26 onwards (ipu.ac.in). It numbers the units without titles, so the unit headings here use the notes' titles.
Every course in GGSIPU notes is mapped to its official PDF the same way.
Who writes and checks the MCA-227 Big Data notes?
- Prateek Talwar, author: Founder, Unnati Educations; Associate Company Secretary (ACS), ICSI, membership no. 58704, since 15 May 2019; B.Com, University of Delhi (School of Open Learning), 2012. Author profile
- Ritik Rawat, subject reviewer: B.Tech; Senior Full Stack Developer, Unnati Educations.
The 2026-27 edition of our GGSIPU MCA Sem 3 Web Intelligence and Big Data notes is mapped to the scheme approved on 28 July 2025. Last updated 14 October 2026; see our editorial policy.
The same team prepares GGSIPU important questions for every course.
MCA-227 and the GGSIPU MCA Sem 3 papers it connects to
Paired lab: MCA-267, Lab based on Core Elective III (Web Intelligence and Big Data Lab, 0-2-1), listed in the GGSIPU MCA syllabus. Its four outcomes map to theory units:
- Data sets on Hadoop: Unit IV (HDFS commands p. 65, Sqoop p. 70).
- Web scraping and crawling: Unit II (crawler loop, pp. 26-27).
- NoSQL characteristics: Unit III (Redis, MongoDB, Cypher, pp. 51-52).
- ML libraries with Hadoop: Unit II classification (pp. 36-39) and Unit IV streaming (p. 68).
Compulsory papers: MCA-201 Design and Analysis of Algorithms and MCA-203 Artificial Intelligence and Machine Learning, which shares regression and Naive Bayes with Unit II.
Other Core Elective III options: MCA-229 Flutter and Dart, MCA-231 Service Oriented Architecture, MCA-233 Multimedia Technologies, MCA-235 Internet of Things, MCA-237 Soft Computing, MCA-239 Software Quality Management, MCA-241 Digital Image Processing, MCA-243 Compiler Design, MCA-245 Parallel Computing, MCA-247 Numerical and Scientific Computing.
Core Elective II runs from MCA-205 to MCA-225; Open Elective II covers MCA-249 to MCA-255. Labs MCA-261 to MCA-267, MCA-269 Minor Project III and NUES papers MCA-271 and MCA-273 complete the semester.
How to write MCA-227 answers: IR numericals, comparison tables and big data case studies
Numericals in Units I and II (TF-IDF, cosine, PageRank, ID3) follow one layout: formula, table, one-line result. The vector space example uses four steps (IDF, TF-IDF weights, vector lengths, cosine) that suit any ranking question.
Comparisons (PageRank against HITS, RDBMS against NoSQL, Sqoop against Flume, Spark against MapReduce) go in a table with a "Basis" column, then one line on when each fits. Unit III case studies follow the notes' order: Organisation, Problem, Data used, Big Data technology, Outcome. The Unit I tip worth remembering: accuracy looks high in IR because true negatives are huge, so use precision, recall and F1.
Worked example: HDFS blocks (page 65)
A 500 MB file, block size 128 MB, replication 3.
- Blocks = ceiling of 500 / 128 = 4, sized 128 + 128 + 128 + 116 MB; HDFS does not waste the last block's spare 12 MB.
- Replicas = 4 × 3 = 12.
- Raw storage = 500 × 3 = 1,500 MB, about 1.46 GB.
Show the ceiling step and the last block size clearly.
The notes tag short answers as 2.5 marks and long answers as 12.5 marks, but the 2025-26 paper-setter instructions give 12 marks per question, so size long answers for 12. Practise this format on GGSIPU MCA important questions.
MCA-227 Big Data notes FAQs
How do I get the full MCA-227 notes?
Message us on WhatsApp at 9899436384 with the code MCA-227. The full notes come only as a printed book; they are not available as a PDF. Delivery takes 5 to 7 days at most, and the exact time depends on your location. Check the sample PDF on this page first to see the layout and solved answers.
Is MCA-227 compulsory for every GGSIPU MCA student?
No. MCA-227 is one of 11 Core Elective III options in the third semester, and each student picks one. The scheme says an elective needs at least 20 students for classes to run; with fewer, it may still be offered, but the student completes it through an approved MOOC course. Ask your institute which options it offers.
What is the passing mark for MCA-227?
MCA-227 is marked out of 100, and its end-term question pattern adds up to 60 marks (Q1 for 12 plus four unit questions for 12 each). The MCA scheme of examination states pass marks as 40%, which is 40 out of 100 marks. For end-term minimums, re-appear and result rules, read GGSIPU Ordinance 11.
Which MCA-227 unit has the most numericals?
Unit II, with 6 solved numericals: PageRank on a three-page graph, histogram equalisation on a 4×4 image, a least-squares regression line, an ID3 decision tree, K-means with k = 2 and Naive Bayes with Laplace smoothing. Unit I has 4, Unit IV has 1 on HDFS blocks and Unit III has none, but it has 6 case studies.
Do the MCA-227 notes include Hadoop and Spark code?
Yes. Unit IV has 13 code listings: HDFS shell commands, a Java word-count mapper and reducer, an Oozie workflow, Python streaming scripts, HBase shell, Sqoop, a Flume config, two Pig Latin scripts, HiveQL with partitions and a PySpark word count. These are study notes for understanding each tool and its output, not a ready lab record to submit.
Do the MCA-227 notes match the 2025-26 MCA syllabus?
Yes. We checked them against the MCA scheme approved by the BoS of USIC&T on 28 July 2025, which applies from A.S. 2025-26. Every official topic, from the bow-tie model to Apache Spark, has its own section, as the syllabus map shows. If you are from an older MCA batch, compare your syllabus with that table first.
Get the full MCA-227 Web Intelligence and Big Data Notes on WhatsApp
What the full MCA-227 notes book contains
- Part
- Cover and index
- Pages
- 1-4 (4)
- Solved short answers
- -
- Long-answer questions
- -
- Part
- Unit I
- Pages
- 5-23 (19)
- Solved short answers
- 22
- Long-answer questions
- 12
- Part
- Unit II
- Pages
- 24-42 (19)
- Solved short answers
- 23
- Long-answer questions
- 12
- Part
- Unit III
- Pages
- 43-59 (17)
- Solved short answers
- 22
- Long-answer questions
- 13
- Part
- Unit IV
- Pages
- 60-77 (18)
- Solved short answers
- 24
- Long-answer questions
- 12
Format: a printed book of Web Intelligence and Big Data notes in English, 77 pages, with 27 diagrams and a Quick Revision Table per unit. Not available as a PDF. Delivery: within 5 to 7 days at most; the exact time may vary with your location.
Get the full MCA-227 notes book on WhatsApp (4 units, 77 pages)
Unnati Educations is an independent education provider based in Vikaspuri, Delhi. We are not affiliated with, authorised by, or endorsed by Guru Gobind Singh Indraprastha University (GGSIPU). 'GGSIPU' and 'IPU' are used only to describe the syllabus our notes follow. For official information, visit ipu.ac.in.
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