MIS – Chapters 3 – 6 Comprehensive Bullet-Point Notes

Database Systems, Data Warehouses, and Data Marts

• Database & DBMS Fundamentals
  • A database is a centralized or distributed collection of related data that supports any subsequent analysis an information system performs.
  • A database management system (DBMS) is software for creating, storing, maintaining, and accessing database files, making database use more efficient by acting as the intermediary between users/applications and the data.
  • User interaction pattern: request → DBMS engine locates data → returns result; abstracts the underlying file-access method (sequential, random, indexed-sequential).
• Data Hierarchy
  • Field → record → file → database; this hierarchy underlies every data model.
• File-Access Methods
  • Sequential: records processed in entry order; good for large batch operations.
  • Random: any record reachable immediately; ideal when only a few records need frequent access.
  • Indexed Sequential (ISAM): hybrid—small batches random, large batches sequential.
• Views of Data
  • Physical view: how bits are placed on storage media.
  • Logical view: how users logically see/organize data; hides storage details.
• Data Modeling & Integrity
  • A data model defines structure, operations, and integrity rules; guides logical design.
  • Integrity rules (e.g., entity & referential integrity) ensure accuracy/consistency.
• Major Data Models
  • Hierarchical (tree of nodes/branches, single parent), Network (multiple parents), Relational (2-D tables), Object-oriented (encapsulated objects with attributes & methods, supporting inheritance/encapsulation).
  • Relational remains dominant in business systems.
• Relational Concepts
  • Row = record = tuple; Column = field = attribute.
  • Primary key uniquely identifies each record; foreign key cross-references tables and supports 1{:}n or n{:}m relationships.
  • Normalization removes redundancy & keeps only related data per table, improving update efficiency.
• Data Dictionary
  • Stores metadata (field types, default values, validation rules). DBMS consults it for compilation, enforcement, app generation.
• DBMS Software Components
  • Database engine (storage/manipulation/retrieval)
  • Data definition (DDL, data dictionary maintenance)
  • Data manipulation (DML: add, delete, modify, query)
  • Application generation tools (forms, GUIs, menus)
  • Data administration (security, backup/recovery, change mgmt, CRUD privileges)
• Query Languages
  • SQL: 4GL standard with keywords (SELECT, INSERT…).
  • QBE: graphical query construction with AND/OR/NOT—lowers syntactic burden.
• Database Personnel
  • DBA installs DBMS, designs DB, sets security & recovery, tunes performance.

Recent Trends in Database Design

• Data-driven Web sites: dynamic pages created by live DB queries; front-end acts as an interface for retrieval & entry.
• Distributed DBMS (DDBMS)
  • Stores data on multiple servers; improves locality & fault tolerance.
  • Fragmentation (horizontal, vertical, mixed), Replication (full copy at each site), Allocation (hybrid) strategies.
• Object-oriented DB: encapsulates complex data types (images, CAD); methods stored with data for reusability.
• Influence of AI & NLP: conversational query interfaces, automatic semantic extraction, autonomous tuning.

Data Warehousing & Business Intelligence

• Data Warehouse (DW)
  • Integrated, subject-oriented, time-variant, non-volatile repository supporting decision making.
  • Core processes: ETL (Extract, Transform, Load) → cleanses & harmonizes data before loading.
  • Supports OLTP (operational), OLAP (multidimensional analytic), and data mining (pattern discovery).
• Data Marts
  • Department-level, smaller, cheaper, faster to build; same analytical power focused on specific business functions.
• Business Analytics (BA)
  • Statistical & data-mining techniques over DB/DW/DMs to yield actionable insight; vendors: SAS, IBM, SAP, Microsoft, Oracle.
• Big Data & the 3\,Vs
  • Volume, Variety, Velocity require distributed frameworks (Hadoop, Spark) and cloud storage.
  • Business apps: sentiment analysis, real-time fraud detection, IoT telemetry.
• Database Marketing
  • Uses customer DB for targeted promotions, lifetime value modelling, personalized offers; raises privacy & ethical debates about data usage.

Personal, Legal, Ethical, and Organizational Issues of IS

Information Privacy

• Sensitive personal data stored in multiple institutional DBs; cross-matching allows behavioral profiling.
• Workplace monitoring: keystroke, idle time, social media screening; benefits (productivity) vs. privacy rights.
• Acceptable Use Policy (AUP) defines legal/ethical use & sanctions.
• Accountability: identifying responsibility & liability when misuse occurs.
• Nonrepudiation binds parties to digital contracts (e-signatures, PKI).

E-mail, Data Collection, & Censorship

• E-mail risks: spam, phishing, employer monitoring; encryption & spam filters mitigate.
• Cookies & log files silently collect clickstream data; inaccurate self-reported info can cause misrepresentation.
• Censorship: public info may be restricted for policy; private posts protected by free speech.

Ethics in IT

• Ethical ≠ legal; gray areas vary by culture; codes of ethics (ACM, IEEE) guide professionals.
• Management role: enforce ethics training & whistle-blower channels to reduce cyber-fraud.

Intellectual Property

• Two categories: Industrial (patents, trademarks) & Copyrighted materials (literary/artistic).
• Online material (HTML, graphics) covered once storable or printable.
• Threats: software piracy, cybersquatting, patent trolls.

Organizational & Social Issues

• Digital divide: unequal access → educational & economic disparities.
• Virtual organizations: networks of independent firms sharing skills/markets via IT.
• Telecommuting: boosted by data comm; impacts work–life balance, urban congestion.
• Ergonomics: furniture, lighting, keyboards to prevent RSI, vision strain.

Green Computing

• Design, manufacture, use, disposal of IT with minimal environmental impact.
• ICT emits ≈ 2\% of global CO_2 (≈ aviation industry)—initiatives: energy-efficient servers, virtualization, e-waste recycling.

Protecting Information Resources

Computer Crime Tools & Techniques

• Malware-related: viruses, worms, Trojans, logic bombs, blended threats, rootkits.
• Spyware/adware, keyloggers, sniffing (packet capture), spoofing (identity masquerade).
• Social engineering: phishing, pharming, baiting, quid pro quo.
• Computer fraud: unauthorized data use for personal gain.

CIA Triad & Fault Tolerance

• Confidentiality: restrict disclosure to authorized users.
• Integrity: assure data accuracy/completeness.
• Availability: systems operational & recover quickly.
• Fault-tolerant systems (RAID, UPS, failover clusters) guarantee availability.

Security Measures

• Biometric: fingerprints, iris scans; cannot be shared/lost.
• Non-biometric: callback modems, smart cards.
• Physical: locks, guards, cable traps.
• Logical/Access controls: passwords, ACLs.
• Firewalls & IDS: packet filtering, signature recognition; IDS often in front of firewall.
• VPNs: secure Internet tunnels.
• Encryption
  • Symmetric (secret key): same key encrypt/decrypt—fast but key distribution issue.
  • Asymmetric (public key): public/private pair; enabled by PKI; protocols: SSL/TLS.
• E-commerce safeguards: digital signatures, SET.
• Computer Emergency Response Team (CERT): incident coordination.

Planning & Policy

• Security committee, posted policies, software patching, antivirus, fire drills, off-site backups.
• Business continuity planning (BCP) & disaster recovery plans ensure operations during/after disasters.

Data Communication: Delivering Information Anywhere & Anytime

System Components

• Sender/receiver devices (workstations, laptops, mobile phones, IoT sensors).
• Modems/Routers translate/route signals.
• Communication media: conducted (twisted-pair, coax, fiber) & radiated (RF, microwave, satellite).

Key Concepts

• Data communication = electronic data transfer between locations.
• Bandwidth/Throughput: amount transmitted per second.
• Attenuation: signal loss; amplifiers or repeaters combat.
• Broadband: multiple data pieces simultaneously for higher rates; vs. narrowband \le56\,kbps.
• Protocol: rule set; TCP/IP industry standard; data sent as packets.

Processing Configurations

• Centralized (single host), Decentralized (each unit self-processing), Distributed (central control + remote nodes).

Networks & Topologies

• LAN (office), MAN (city), WAN (countries).
• Topologies: Star (central host), Ring (token passing), Bus (shared backbone), Hierarchical/Tree (organizational), Mesh (fully interconnected) with controllers & multiplexers as needed.

Routing & Devices

• Routing tables (static vs. dynamic) determine best path; routers manage traffic.
• Centralized routing manager vs. distributed routing (each node autonomous).
• Client/Server model
  • Two-tier (client ↔ server) most common; n-tier adds middle tier for scalability.

Wireless & Mobile Networks

• Wireless network: substituting RF/IR for cable; mobile/cellular uses cell sites.
• Efficiency schemes: TDMA (time slots), CDMA (code spreading), frequency reuse.
• Importance: supports telecommuting, IoT, just-in-time logistics.

Wireless Security Techniques

• SSID broadcasting can be hidden.
• WEP basic encryption (weak), EAP authentication, WPA/WPA2 (802.11i) stronger key management.

Convergence Phenomenon

• Integration of voice, video, data enables VoIP, unified messaging, telepresence, streaming analytics; foundation for digital transformation initiatives.

Cross-Chapter Connections & Implications

• Reliance on accurate, available data (Ch.3) underscores need for robust security (Ch.5) and reliable networks (Ch.6).
• Ethical/privacy concerns (Ch.4) intensify as BA & big data (Ch.3) mine personal behavior.
• Green computing (Ch.4) intersects with data centers that house warehouses and cloud networks—optimization lowers CO_2.
• Virtual organizations and telecommuting (Ch.4) depend on secure wireless/data-comm infrastructure (Ch.6).
• Database marketing (Ch.3) must balance personalization with compliance to privacy laws & AUP (Ch.4).