IB Computer Science HL · Year 2 · Lesson 49

OLAP, data mining and distributed databases + A3 synthesis

Alternative databases and analytical systems trade relational structure for scale, specialized data types, distribution or analytical power. Databases organize persistent data so that it can be stored, related and accessed consistently.

Paper 150 minutesA3.4.3 · A3.4.4 · A3.1.1 · A3.2.1 · A3.2.2 · A3.2.3 · A3.2.4…
Use Next or the arrow keys.
Course homeIA hub2027 case study
Today’s targets

What you need to be able to do

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  • ExplainThe role of online analytical processing (OLAP) and data mining for business intelligence
  • DescribeThe features of distributed databases
  • RetrieveReconnect today’s new material to previously taught content from this topic.
A3.4.3A3.4.4A3.1.1A3.2.1A3.2.2A3.2.3A3.2.4A3.2.5A3.2.6A3.2.7A3.3.1A3.3.2A3.3.3A3.3.4A3.3.5A3.3.6A3.4.1A3.4.2
Paper 1 lensMatch the depth of every response to the command term. Previously learned content can move quickly, but retrieval must still be accurate.
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Retrieve

Rapid Recall Deck

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Say the answer aloud before flipping. Mark secure knowledge quickly and spend time on the gaps.

  • What is OLAP used for?: Online analytical processing supports multidimensional, interactive analysis of large data sets for business intelligence and decision-making.
  • Which data-mining techniques must you recognize?: Classification, clustering, regression, association rule discovery, sequential pattern discovery, and anomaly detection.
  • What is the purpose of data mining?: To discover useful patterns, relationships, trends, or unusual cases in large data sets.
  • What is a distributed database?: A database whose data or processing is spread across multiple networked locations while being managed as a coordinated system.
  • Why is data consistency challenging in a distributed database?: Multiple locations may access or update related data concurrently, so replicas/partitions must remain logically compatible despite delays or failures.
  • Which features are associated with distributed databases?: Concurrency control, partitioning, replication, fault tolerance, scalability, security, global query processing, and location/distribution transparency.
  • Why is ACID relevant to distributed transactions?: It helps ensure that transactions remain reliable and preserve valid data even when operations involve multiple locations.
  • How do primary, foreign, and composite keys differ?: A primary key uniquely identifies a row; a foreign key references a key in another table; a composite key uses multiple fields together to identify a row.
  • What are the three levels of database schema?: Conceptual, logical, and physical schemas.
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A3.4.3 + A3.4.4 · Learn

Core knowledge and application

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A3.4.3Explain

The role of online analytical processing (OLAP) and data mining for business intelligence

Alternative databases and analytical systems trade relational structure for scale, specialized data types, distribution or analytical power.

Exam moveGive the mechanism or relationship and make the reasons/causes explicit.
Required detail 1Data mining techniques must include classification, clustering, regression, association rule discovery, sequential pattern discovery, anomaly detection (note: This links to “A4 Machine learning”)
Required detail 2Uses of the techniques in extracting meaningful information from large data sets
Required detail 3Data mining techniques must include classification, clustering, regression, association rule discovery, sequential pattern discovery, anomaly detection

Explain it without notes

Explain: The role of online analytical processing (OLAP) and data mining for business intelligence in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.

  • Data mining techniques must include classification, clustering, regression, association rule discovery, sequential pattern discovery, anomaly detection (note: This links to “A4 Machine learning”)
  • Uses of the techniques in extracting meaningful information from large data sets
  • Data mining techniques must include classification, clustering, regression, association rule discovery, sequential pattern discovery, anomaly detection
A3.4.4Describe

The features of distributed databases

Alternative databases and analytical systems trade relational structure for scale, specialized data types, distribution or analytical power.

Exam moveGive a detailed, accurate account of what happens or what something is like.
Required detail 1The need to maintain data consistency in a distributed database
Required detail 2Role of ACID to ensure reliable processing of transactions in distributed databases
Required detail 3Features of distributed databases: concurrency control, data consistency, data partitioning, data security, distribution transparency, fault tolerance, global query processing, location transparency, replication, scalability

Explain it without notes

Describe: The features of distributed databases in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.

  • The need to maintain data consistency in a distributed database
  • Role of ACID to ensure reliable processing of transactions in distributed databases
  • Features of distributed databases: concurrency control, data consistency, data partitioning, data security, distribution transparency, fault tolerance, global query processing, location transparency, replication, scalability
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Apply

Transfer to a new scenario

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ScenarioAn organization choosing between relational, nosql, spatial, distributed and analytical systems needs a design or technical decision related to today’s topic. Explain what matters and why.
  • Data mining techniques must include classification, clustering, regression, association rule discovery, sequential pattern discovery, anomaly detection (note: This links to “A4 Machine learning”)
  • Uses of the techniques in extracting meaningful information from large data sets
  • The need to maintain data consistency in a distributed database
  • Role of ACID to ensure reliable processing of transactions in distributed databases
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Exam lens

Paper 1 practice

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Build the response before checking notesUse precise terminology and match the required depth.
  1. Explain: The role of online analytical processing (OLAP) and data mining for business intelligence in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.
  2. Describe: The features of distributed databases in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.
Self-checkAnswer the exact command term. For explain, include mechanism/reason; for compare, pair criteria; for discuss/evaluate/justify, build supported reasoning and a conclusion.
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2027 Case Study

Generative AI research checkpoint

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Build a reusable evidence bank for Paper 1 Section B. Keep claims technical, specific and supported.

Research checkpoint

Research training-data ethics: collect one credible example involving copyright/IP and one involving bias.

Challenge focus: Evaluate training-data choices with particular attention to intellectual property, bias and mitigation.

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Homework

Finish the learning cycle

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Exam preparation — main task

Complete targeted 2027 case-study research and cumulative Paper 1/Paper 2 practice. Record evidence and technical vocabulary you can use in extended responses.

Retrieval

Repeat today’s recall deck and revisit any item marked ‘Review again’.

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End of Lesson 49.