Data Structures and Algorithms
E672019
Data Structures and Algorithms is a foundational computer science textbook that systematically introduces core data organization techniques and algorithmic strategies for efficient problem solving.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Data Structures and Algorithms canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7539987 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Data Structures and Algorithms Context triple: [Alfred V. Aho, coAuthorOf, Data Structures and Algorithms]
-
A.
Algorithms + Data Structures = Programs
"Algorithms + Data Structures = Programs" is a classic computer science textbook by Niklaus Wirth that systematically teaches how combining appropriate data structures with algorithms leads to effective and efficient programs.
-
B.
Department 2: Algorithms and Data Structures
Department 2: Algorithms and Data Structures is a research division of the Max Planck Institute for Informatics focused on the theoretical foundations and practical design and analysis of algorithms and data structures.
-
C.
Introduction to Algorithms
Introduction to Algorithms is a widely used, comprehensive textbook on algorithms and data structures, renowned for its rigorous yet accessible coverage of theoretical and practical topics in computer science.
-
D.
The Science of Computing
"The Science of Computing" is a foundational work by Peter J. Denning that explores the principles, theory, and practice underlying computer science as a scientific discipline.
-
E.
“Macroscopic Data Structure Analysis and Optimization”
“Macroscopic Data Structure Analysis and Optimization” is Chris Lattner’s PhD thesis, in which he develops compiler techniques to analyze and optimize large-scale data structure usage for improved program performance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Data Structures and Algorithms Target entity description: Data Structures and Algorithms is a foundational computer science textbook that systematically introduces core data organization techniques and algorithmic strategies for efficient problem solving.
-
A.
Algorithms + Data Structures = Programs
"Algorithms + Data Structures = Programs" is a classic computer science textbook by Niklaus Wirth that systematically teaches how combining appropriate data structures with algorithms leads to effective and efficient programs.
-
B.
Department 2: Algorithms and Data Structures
Department 2: Algorithms and Data Structures is a research division of the Max Planck Institute for Informatics focused on the theoretical foundations and practical design and analysis of algorithms and data structures.
-
C.
Introduction to Algorithms
Introduction to Algorithms is a widely used, comprehensive textbook on algorithms and data structures, renowned for its rigorous yet accessible coverage of theoretical and practical topics in computer science.
-
D.
The Science of Computing
"The Science of Computing" is a foundational work by Peter J. Denning that explores the principles, theory, and practice underlying computer science as a scientific discipline.
-
E.
“Macroscopic Data Structure Analysis and Optimization”
“Macroscopic Data Structure Analysis and Optimization” is Chris Lattner’s PhD thesis, in which he develops compiler techniques to analyze and optimize large-scale data structure usage for improved program performance.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
computer science textbook
ⓘ
non-fiction book ⓘ |
| coversConcept |
Big-O notation
NERFINISHED
ⓘ
Big-Omega notation NERFINISHED ⓘ Big-Theta notation NERFINISHED ⓘ NP-completeness NERFINISHED ⓘ algorithm design techniques ⓘ array data structures ⓘ asymptotic analysis ⓘ backtracking ⓘ balanced search trees ⓘ divide and conquer ⓘ dynamic programming ⓘ graph algorithms ⓘ graph data structures ⓘ greedy algorithms ⓘ hash tables ⓘ hashing ⓘ heaps ⓘ linked lists ⓘ pattern matching ⓘ queues ⓘ recurrence relations ⓘ search algorithms ⓘ sorting algorithms ⓘ space complexity ⓘ stacks ⓘ string algorithms ⓘ time complexity ⓘ tree data structures ⓘ |
| educationalLevel | undergraduate ⓘ |
| field | computer science ⓘ |
| focusesOn |
algorithmic strategies
ⓘ
data organization techniques ⓘ efficient problem solving ⓘ |
| goal |
build strong algorithmic thinking skills
ⓘ
teach efficient problem solving ⓘ |
| intendedAudience |
computer science students
ⓘ
practicing programmers ⓘ software engineering students ⓘ |
| medium |
digital
ⓘ
print ⓘ |
| pedagogicalApproach |
problem-solving oriented
ⓘ
systematic introduction of core concepts ⓘ |
| topic |
algorithms
ⓘ
data structures ⓘ |
| usedFor |
technical interview preparation
ⓘ
university courses ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Data Structures and Algorithms Description of subject: Data Structures and Algorithms is a foundational computer science textbook that systematically introduces core data organization techniques and algorithmic strategies for efficient problem solving.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.