Charu C. Aggarwal
E359742
Charu C. Aggarwal is a prominent computer scientist and researcher known for his influential contributions to data mining, machine learning, and high-dimensional data analysis.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Charu C. Aggarwal canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T3425614 — 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: Charu C. Aggarwal Context triple: [SIGKDD Innovation Award, notableRecipient, Charu C. Aggarwal]
-
A.
Rakesh Agrawal
Rakesh Agrawal is a pioneering computer scientist best known for his foundational contributions to data mining and database systems.
-
B.
Laxman Narasimhan
Laxman Narasimhan is an Indian-American business executive best known as the chief executive officer of Starbucks and former CEO of Reckitt Benckiser.
-
C.
Jeffrey D. Ullman
Jeffrey D. Ullman is a prominent American computer scientist known for his foundational contributions to database theory, algorithms, and formal languages, and for coauthoring several classic textbooks in computer science.
-
D.
S. Rao Kosaraju
S. Rao Kosaraju is a computer scientist known for his contributions to algorithm design and graph theory, including early work on strongly connected components algorithms.
-
E.
P. Madhusudan
P. Madhusudan is a computer scientist known for his contributions to formal methods, automata theory, and program verification.
- 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: Charu C. Aggarwal Target entity description: Charu C. Aggarwal is a prominent computer scientist and researcher known for his influential contributions to data mining, machine learning, and high-dimensional data analysis.
-
A.
Rakesh Agrawal
Rakesh Agrawal is a pioneering computer scientist best known for his foundational contributions to data mining and database systems.
-
B.
Laxman Narasimhan
Laxman Narasimhan is an Indian-American business executive best known as the chief executive officer of Starbucks and former CEO of Reckitt Benckiser.
-
C.
Jeffrey D. Ullman
Jeffrey D. Ullman is a prominent American computer scientist known for his foundational contributions to database theory, algorithms, and formal languages, and for coauthoring several classic textbooks in computer science.
-
D.
S. Rao Kosaraju
S. Rao Kosaraju is a computer scientist known for his contributions to algorithm design and graph theory, including early work on strongly connected components algorithms.
-
E.
P. Madhusudan
P. Madhusudan is a computer scientist known for his contributions to formal methods, automata theory, and program verification.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
author
ⓘ
computer scientist ⓘ researcher ⓘ |
| awardReceived |
ACM Fellow
ⓘ
IBM Master Inventor ⓘ IBM Outstanding Innovation Award ⓘ
linked to:
Outstanding Innovation Award from IBM
IBM Outstanding Technical Achievement Award ⓘ IBM Research Division Award ⓘ
linked to:
Outstanding Innovation Award from IBM
IEEE Fellow ⓘ SIAM Fellow ⓘ |
| educatedAt | Massachusetts Institute of Technology ⓘ |
| employer |
IBM T. J. Watson Research Center
ⓘ
linked to:
IBM Thomas J. Watson Research Center
|
| fieldOfWork |
data mining
ⓘ
data streams ⓘ graph mining ⓘ high-dimensional data analysis ⓘ machine learning ⓘ outlier detection ⓘ privacy-preserving data mining ⓘ recommender systems ⓘ social network analysis ⓘ text mining ⓘ |
| genre | scientific literature ⓘ |
| hasAcademicDegree | PhD in Computer Science ⓘ |
| hasCitizenship | United States of America ⓘ |
| hasHIndex | very high h-index in computer science ⓘ |
| hasPublicationType |
books
ⓘ
edited volumes ⓘ research articles ⓘ |
| hasResearchInterest |
algorithm design for big data
ⓘ
large-scale data analysis ⓘ |
| hasRole |
editor for journals in data mining and knowledge discovery
ⓘ
program committee member for data mining conferences ⓘ |
| knownFor |
work on data streams
ⓘ
work on high-dimensional data ⓘ work on outlier detection ⓘ work on privacy-preserving data mining ⓘ work on recommender systems ⓘ work on social network data analysis ⓘ |
| languageOfWorkOrName | English ⓘ |
| memberOf |
Association for Computing Machinery
ⓘ
Institute of Electrical and Electronics Engineers ⓘ |
| notableWork |
Data Classification: Algorithms and Applications
ⓘ
Data Mining: The Textbook ⓘ Managing and Mining Graph Data ⓘ Outlier Analysis ⓘ Privacy-Preserving Data Mining ⓘ Recommender Systems: The Textbook ⓘ Social Network Data Analytics ⓘ |
| occupation | research staff member ⓘ |
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: Charu C. Aggarwal Description of subject: Charu C. Aggarwal is a prominent computer scientist and researcher known for his influential contributions to data mining, machine learning, and high-dimensional data analysis.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.