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Sukhendu Chakraborty

dblp:97/1042 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data models and query languages · 70% Information retrieval · 30%
Theoretical computer science
1 paper
Quantum computing and quantum information · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › document retrieval
text search
0.112010
Pay-as-you-go: an adaptive approach to provide full context-aware text search over document content · SIGMOD Conference 2010
Data models and query languages
XML query languages
0.112010
Pay-as-you-go: an adaptive approach to provide full context-aware text search over document content · SIGMOD Conference 2010
Data models and query languages › XML query languages
XQuery Full Text
0.112010
Pay-as-you-go: an adaptive approach to provide full context-aware text search over document content · SIGMOD Conference 2010
Quantum computing and quantum information › quantum information theory
quantum data compression
0.112007
Efficient and exact quantum compression · Inf. Comput. 2007
Data models and query languages › semistructured data
semi-structured data management
0.012010
Pay-as-you-go: an adaptive approach to provide full context-aware text search over document content · SIGMOD Conference 2010

Methods — techniques the papers use, named apart from their topics

keyword search · 0.1XQuery full text · 0.1
YearPublicationVenuePosition
2010 Pay-as-you-go: an adaptive approach to provide full context-aware text search over document content
abstract
RDBMS provides best performance for querying structured data that starts out with a well-defined schema. However, such a 'schema first, data later' approach does not work for unstructured data or data without much structure. Therefore, RDBMS typically stores such data without any schema in LOB columns (for example, Character Large Object (CLOB) or Binary Large Object (BLOB) columns) and provides Information-Retrieval (IR) style, keyword-based search capability over these LOB columns. Lately, XML as a native datatype (XMLType) in RDBMS has been introduced via the SQL/XML standard. Semi-structured data with or without any schema can be stored into such XMLType columns, and XQuery provides query capability over them. In particular, XQuery full text specification provides the capability of searching keywords within document context. Such full context-aware text search capability is more powerful than pure keyword search, since the user can now provide fine-grained context in which the keywords should occur. However, XML with XQuery full text searching requires that the user first convert her text data into XML and store them into XMLType column. Such massive physical data migration with possible loss of document fidelity and its potential impact on existing production environments are often expensive enough that users are reluctant to adopt the XML/XQuery approach.
Zhen Hua Liu, Thomas Baby, Sukhendu Chakraborty, Junyan Ding, Anguel Novoselsky, Vikas Arora
SIGMOD Conference3
2007 Efficient and exact quantum compression
John H. Reif, Sukhendu Chakraborty
Inf. Comput.2