MIS Modules 03, 06, 07 & 10 – Comprehensive Study Notes

Module 03 – Databases, Big Data & Business Analytics

• Database
  • Collection of related data stored centrally or at multiple sites.
  • Core of nearly every information system; supports transaction processing, decision support, analytics.
  • Example: A university DB storing student, course, and payroll information in one logical repository.

• Database Management System (DBMS)
  • Software layer that creates, stores, maintains, and secures database files.
  • Improves data sharing, integrity, security, and reduces program–data dependence.
  • Popular packages include Oracle, MySQL, Microsoft SQL Server.

• Data Hierarchy
  • Logical structure: fields → records → files → databases.
  • Ensures organized storage and retrieval; mirrors real-world entities.

• Data Dictionary
  • "Metadata repository" holding field names, data types, defaults, validation rules, indexes, relationships.
  • DBA uses it for impact analysis when altering structures.

• Data Model
  • Blueprint that dictates how data are represented (entities, attributes, relationships), manipulated, and kept consistent.
  • Must define:
  – Data structures (tables, objects, nodes)
  – Operations (insert, update, delete, query)
  – Integrity rules (referential, domain, semantic)
  • Choosing the right model (relational, object-oriented, etc.) affects scalability and performance.

• Relational Model
  • Organizes data into two-dimensional tables (relations). Rows = records (tuples), columns = attributes (fields).
  • Mathematical foundation in set theory & relational algebra; enables \sigma (selection), \pi (projection), \bowtie (join) operations.
  • Keys:
  – Primary key uniquely identifies each row.
  – Foreign key enforces referential integrity by pointing to a primary key in another table.

• Hierarchical Model
  • Data organized in a tree; one parent per child.
  • Fast for 1-to-many look-ups (e.g., organizational charts).
  • Drawback: redundancy if many-to-many relationships exist.

• Network Model
  • Extends hierarchical by allowing multiple parents per node, enabling many-to-many links.
  • Uses sets and records; navigation requires explicit path traversal.

• Object-Oriented Database
  • Encapsulates both state (attributes) and behavior (methods) in objects.
  • Supports encapsulation (grouping) and inheritance (reuse).
  • Suitable for complex data (CAD, multimedia, scientific).
  • Querying often via OQL (Object Query Language).

• Encapsulation
  • Bundles data with code manipulating it → simplifies maintenance and modeling of real-world objects like images, graphs.

• Inheritance
  • New object classes derive properties/methods from existing ones → faster development and consistency.

• Logical vs. Physical Views
  • Logical view – how users conceptually see data: tables, forms, reports.
  • Physical view – how data are actually stored on media: blocks, files, indexes.
  • DBMS translates logical requests into physical operations.

• Create, Read, Update, Delete (CRUD)
  • Fundamental operations; access control lists specify which roles can perform them.
  • Example: Customer support rep may Read & Update a ticket, but not Delete records.

• Normalization
  • Process of decomposing tables to eliminate redundancy and anomalies; progresses through 1\text{NF} \rightarrow 2\text{NF} \rightarrow 3\text{NF} \rightarrow ….
  • Benefits: smaller tables, consistent updates, simpler maintenance.

• Distributed DBMS (DDBMS)
  • Spreads data over multiple servers; provides location transparency and improved availability.
  • Three key data-placement strategies:
  – Fragmentation (horizontal, vertical, mixed)
  – Replication (full/partial copies)
  – Allocation (hybrid of both, placing data where accessed most).

• Fragmentation
  • Horizontal – each fragment holds subset of rows.
  • Vertical – each holds subset of columns.
  • Mixed – combination; improves locality but complicates queries.

• Replication
  • Duplicates tables/sites; boosts read performance & fault tolerance; risks consistency challenges handled by concurrency protocols.

• Allocation
  • Sites keep data they access frequently; minimizes WAN latency, balances load.

• Random vs. Sequential Access File Structures
  • Random – direct access to any record (e.g., hashing). Ideal for small, ad-hoc lookups.
  • Sequential – records processed in fixed order; efficient for batch reporting.

• Indexed Sequential Access Method (ISAM)
  • Hybrid; small set → index → random access; large set → sequential scan. Balances performance.

• Structured Query Language (SQL)
  • Declarative 4^{\text{th}}-generation language; keywords like SELECT, INSERT, UPDATE, DELETE.
  • Example query:

  SELECT product_name, SUM(quantity) AS total
  FROM sales
  GROUP BY product_name
  HAVING SUM(quantity) > 100;
  

• Query By Example (QBE)
  • Graphical interface; users fill templates; DBMS converts to SQL.
  • Logical operators (AND, OR, NOT) refine criteria.

• Data-Driven Web Site
  • Web front-end connected to a database; dynamic pages generated via queries.
  • Enables e-commerce catalogs, account dashboards, content management.

• Data Warehouse
  • Subject-oriented, integrated, time-variant, non-volatile repository for decision support.
  • Typically refreshed via ETL pipelines; supports OLAP, data mining.

• Data Mart
  • Department-scale subset of a warehouse (e.g., marketing), cheaper & faster to deploy.

• Data Lake
  • Central storage for raw, structured & unstructured data; schema applied "on read."
  • Supports big-data analytics using Hadoop, Spark.

• Extraction, Transformation & Loading (ETL)
  • Extract \rightarrow Transform \rightarrow Load pipeline prepares disparate data for warehouses.
  – Extract: pull from ERP, CRM, flat files.
  – Transform: cleanse, deduplicate, format, derive.
  – Load: bulk insert into warehouse tables.

• Big Data
  • Characterized by the 5 V’s: Volume, Velocity, Variety, Veracity, Value.
  • Requires parallel processing frameworks (MapReduce), NoSQL stores, in-memory computing.

• Business Analytics (BA)
  • Uses stats, predictive models, data mining to glean insights; feeds dashboards for executives.
  • Tools: Power BI, Tableau, Python libraries, R.

• Online Transaction Processing (OLTP)
  • Real-time, record-level processing for routine tasks (POS, ATM).
  • Emphasizes ACID properties: Atomicity + Consistency + Isolation + Durability.

• Online Analytical Processing (OLAP)
  • Multidimensional analysis (cubes) → slice-and-dice, drill-down, roll-up.
  • Power users examine sales by {\text{product},\text{region},\text{time}} axes simultaneously.

• Data-Mining Analysis
  • Discovers hidden patterns via clustering, association rules (e.g., market basket {\text{diapers}}\rightarrow{\text{beer}}), classification.

• Text-Mining Analysis
  • Extracts sentiment, entities from large text corpora; useful for social-media monitoring.

• Power BI & Tableau
  • Visualization platforms turning raw data into interactive dashboards; enable self-service BI.

• Database Administrators (DBA)
  • Responsibilities: design schema, tune indexes, manage security, backup/restore, ensure uptime.

Module 06 – Data Communications & Networking

• Data Communication
  • Electronic transfer of data between locations; foundation for distributed systems, IoT.

• Components
  • Sender/Receiver
  • Hardware (NICs, routers, modems)
  • Communication media
  • Protocols
  • Software (OS, drivers)

• Communication Media
  • Conducted (guided) – twisted pair, coaxial, fiber optics.
  • Radiated (unguided) – microwave, radio (Wi-Fi, cellular), infrared.

• Bandwidth vs. Throughput
  • Bandwidth – theoretical capacity measured in \text{bps}.
  • Throughput – actual achieved rate under real conditions.

• Narrowband vs. Broadband
  • Narrowband ≤ 56 kbps (legacy voice lines).
  • Broadband transmits multiple signals concurrently; cable, DSL, fiber.

• Attenuation
  • Signal power loss over distance; mitigated by repeaters, amplifiers, fiber.

• Modem (Modulator-Demodulator)
  • Converts digital ↔ analog signals for transmission over legacy phone lines.

• Multiplexer
  • Combines several input signals on one line; saves cost.

• Network Topologies
  • Bus – linear backbone, terminators at ends. Cheap but collision-prone.
  • Ring – nodes form closed loop; token-passing avoids collisions.
  • Star – central hub/switch; easy fault isolation.
  • Mesh – each node connects to all; high redundancy (used in WAN backbones).
  • Hierarchical (Tree) – mix of star plus additional branching levels.

• LAN / MAN / WAN
  • LAN – < 1 km, high speed.
  • MAN – spans city (fiber rings).
  • WAN – global, leased lines, satellites.

• Wireless Technologies
  • Wi-Fi (IEEE 802.11) – 2.4/5 GHz, 120–300 ft.
  • Bluetooth – personal area network (~800 ft).
  • WiMAX (802.16) – 3–30 mi metro coverage.
  • Mobile/Cellular Network – cells served by base stations; supports TDMA, CDMA.

• Multiple Access Techniques
  • FDMA – by frequency bands.
  • TDMA – splits channel into time slots; 3\times voice capacity.
  • CDMA – spread-spectrum code; high security and user density.

• Routing Concepts
  • Router – directs packets; maintains routing table (destination → next hop).
  • Static routes – manually defined (good for small, stable LANs).
  • Dynamic routes – learned via protocols (RIP, OSPF).
  • Centralized routing – one node computes all paths.
  • Distributed routing – each node computes own best path.

• Packet
  • Wrapper of user data + headers (source/dest IP, sequence, checksum); enables store-and-forward networks.

• Protocols
  • Rules for syntax, error control, flow control, \text{bps}.
  • TCP/IP suite underpins the Internet; OSI 7-layer model standardizes exchanges.

• Client/Server & N-Tier Architectures
  • Two-tier: UI ↔ DB server.
  • N-tier: adds application servers to balance load; easier scaling, security segregation.

• Controller & NIC
  • Interface hardware enabling network access; contains MAC address.

• Convergence
  • Integration of voice, video, data; enables VoIP, video conferencing on same IP network.

• DSL, Cable, Fiber
  • DSL uses copper phone lines; asynchronous speeds.
  • Cable shares coax; higher speeds but contention.
  • Fiber – highest capacity, low attenuation.

Module 07 – Internet & Web Technologies

• Internet & Backbone
  • Global "network of networks"; backbone = high-capacity fiber links run by Tier-1 ISPs using BGP routing.

• ARPANET
  • 1969 DoD project; packet-switching proof-of-concept; four original nodes (UCLA, SRI, UCSB, Univ. Utah).

• DNS Protocol
  • Converts human domain names to IP addresses via hierarchy of root, TLD, authoritative servers.

• URL
  • Uniform Resource Locator format: scheme://host:port/path?query#fragment.

• HTML & Hypertext/Hypermedia
  • HTML tags (\<h1>, \<img>, etc.) structure web pages.
  • Hypertext = clickable links; hypermedia extends to audio/video.

• Web 2.0
  • Shift to interactive, user-generated content (wikis, blogs, social networks); enables e-collaboration.

• Blog / Wiki / Podcast
  • Blog = personal journal; wiki = collaborative editing; podcast = downloadable audio series.

• Search Engines & Directories
  • Use crawlers, indexers, and query processors; early Yahoo = manual directory, Google = PageRank algorithm.

• Navigational Tools
  • Browsers (Chrome, Edge), bookmarks, history facilitate "surfing" the Web.

• Instant Messaging, IRC, Discussion Groups, Newsgroups
  • Real-time or asynchronous communication; precursor to modern social media & Slack.

• RSS Feeds
  • XML-based syndication; feed readers aggregate headlines automatically.

• Internet Telephony & VoIP
  • Conveys voice packets over IP; savings vs. PSTN; requires QoS to minimize \text{delay} \le 150\,\text{ms}.

• Internet of Things (IoT), IoE, IIoT
  • IoT – billions of sensor-equipped physical objects online.
  • IoE – adds people, processes, data for holistic connectivity.
  • IIoT – industrial applications (predictive maintenance, SCADA).
  • Smart home = consumer IoT implementation (thermostats, lights).

• Intranet & Extranet
  • Intranet – private corporate network using Internet tech; enhances internal comms.
  • Extranet – extends limited access to business partners (B2B portals).

• Social Networking
  • Platforms (Facebook, LinkedIn) enabling user profiles, relationships graph.

Module 10 – Systems Development & Project Management

• Systems Development Life Cycle (SDLC)
  • Waterfall model:

  1. Planning
  2. Requirements Gathering & Analysis
  3. Design
  4. Implementation
  5. Maintenance
     • Each phase produces deliverables feeding the next.

• Planning Phase
  • Define problem not symptoms; form task force; produce preliminary feasibility study.

• Feasibility Study Dimensions
  • Economic – cost/benefit (ROI, NPV = \sum \frac{CF_t}{(1+r)^t}).
  • Technical – availability/compatibility of tech.
  • Operational – acceptance by users; alignment w/ procedures.
  • Scheduling – can it be done on time?
  • Legal – compliance (Info Privacy Act, GDPR).

• Internal vs. External Users
  • Internal employees vs. external stakeholders; both provide requirements.

• Joint Application Design (JAD)
  • Facilitated workshops merging users, managers, IT to accelerate consensus.

• Computer-Aided Systems Engineering (CASE)
  • Diagramming, code generation, repository tools that automate SDLC tasks; improves documentation.

• Design Phase
  • Choose best alternative; produce detailed specs: data models, UI mock-ups, hardware needs.

• Implementation Phase
  • Code, test, install; deliver training; prepare conversion strategy:
  – Plunge (direct)
  – Parallel
  – Phased-in / Phased-out
  – Pilot

• Maintenance Phase
  • Post-deployment changes, optimizations, patches; absorbs \approx 80\% of life-cycle cost.

• Prototyping Approaches
  • Proof-of-Concept – validates feasibility.
  • Selling – demonstrates benefits to secure funding.
  • RAD – merges planning & analysis, iterative builds.
  • Extreme Programming (XP) – short cycles, pair programming, continual testing.
  • Agile Methodology – incremental delivery, embraces change, limits scope per sprint.

• Low-Code / No-Code Platforms
  • Drag-and-drop components; enable self-sourcing by power users; speed MVP delivery but risk shadow IT.

• Service-Oriented Architecture (SOA)
  • Decomposes functionality into reusable services accessed via open protocols (SOAP, REST). Encourages agility.

• Outsourcing vs. Insourcing vs. Crowdsourcing
  • Outsourcing – hire vendor; focus on core competences, might lose control.
  • Insourcing – internal dev team; better IP retention.
  • Crowdsourcing – open call to large online community; leverages diversity, cost-effective.

• IT Project Management
  • Initiation, planning (Gantt/PERT), execution, monitoring (EV =\text{% complete}\times\text{budget}), closure.
  • Tools: MS Project, Jira.

• Requests for Information (RFI) & Proposal (RFP)
  • Formal procurement docs; RFI = market scan; RFP = solicit bids with specs.

• Conversion Costs & Risk
  • Direct-cutover cheaper but risky; parallel safest but doubles operations temporarily.

• Scheduling Techniques
  • Critical Path = \text{longest path through PERT network}; slack time helps allocate resources.