Question at position 6 The following example is based on Dong & Srivastava (2015). Given two data sources with the following information: Data source 1 models flight departure and arrival time with the relation: Flight(FlightNumber, DepartureTime, ArrivalTime) Data source 2 models flight departure and arrival time with the relation: Flight(FlightNumber, Type, Time) where Type is either "departure" or "arrival". This is an example of which of the following?Semantic ambiguityInstance representation ambiguityData inconsistencyNone of the above.单项选择题
A
Semantic ambiguity
B
Instance representation ambiguity
C
Data inconsistency
D
None of the above.

Question

Question at position 6 The following example is based on Dong & Srivastava (2015). Given two data sources with the following information: Data source 1 models flight departure and arrival time with the relation: Flight(FlightNumber, DepartureTime, ArrivalTime) Data source 2 models flight departure and arrival time with the relation: Flight(FlightNumber, Type, Time) where Type is either "departure" or "arrival". This is an example of which of the following?Semantic ambiguityInstance representation ambiguityData inconsistencyNone of the above.单项选择题

A

Semantic ambiguity

B

Instance representation ambiguity

C

Data inconsistency

D

None of the above.

BlackTom AI · Accepted Answer

Instance representation ambiguity arises when the same real-world facts are represented by different schemas or tuple structures across sources (same semantics, different representation).

1. Relevant concepts
- Instance representation ambiguity: different data sources represent the same entities/attributes using different schemas or formats (e.g., one uses separate columns for departure/arrival, another uses a type-value pair).
- Semantic ambiguity: a term is used with different meanings across sources (same label, different semantics).
- Data inconsistency: conflicting factual values for the same real-world fact (e.g., one source says departure is 9:00, another says 10:00).

2. Step‑by‑step reasoning
- Source 1 schema: Flight(FlightNumber, DepartureTime, ArrivalTime). A single tuple holds both times.
- Source 2 schema: Flight(FlightNumber, Type, Time), where Type ∈ {departure, arrival}. Two tuples represent the two times for each flight.
- For a flight F: Source 1 might have (F, 09:00, 12:00). Source 2 will have (F, 'departure', 09:00) and (F, 'arrival', 12:00).
- The underlying facts (departure at 09:00, arrival at 12:00) are the same; only the representation differs (columnar vs. type-value rows).

3. Intermediate steps / mapping example
- To align Source 2 to Source 1: pivot Type values into columns:
  - From (F,'departure',09:00) + (F,'arrival',12:00) → (F,09:00,12:00).
- No numeric calculation is needed; the step shows how instance representations map.

4. Why the correct answer is correct
- The issue is exactly that the same flight times are encoded in different tuple shapes. This is the textbook case of instance representation ambiguity: identical semantics but different instance-level representation.

5. Why the other options are incorrect
- Semantic ambiguity: not applicable because attribute meanings (departure/arrival times) are consistent across sources.
- Data inconsistency: not applicable because the actual times are not in conflict.
- None of the above: incorrect because there is a known mismatch category—instance representation ambiguity.

6. Conclusion
This example is an instance representation ambiguity, which matches the provided correct answer: Instance representation ambiguity.

类似问题

What are typical use cases for data integration?

Identify the preprocessing steps necessary for integrating multiple data sources: (Select all that apply)

Which one of these statements explains what data integration is?

Question at position 4 According to lecture, data integration often requires which of the following?Defining a standard or common data modelTransforming data into a common modelCombining data at the schema levelCombining data at the record levelAll of the above

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