EDBT 2026 Demo / reviewers in the wild / expert

Dimitrije Filipovic

dblp:116/5299 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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

Databases, data management, data science and information retrieval · 2

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
2 papers
Data models and query languages · 32% Data integration and cleaning · 32% Query processing and optimization · 32%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization
query rewriting		 0.322014
Mapping XML to a Wide Sparse Table · IEEE Trans. Knowl. Data Eng. 2014
Mapping XML to a Wide Sparse Table · ICDE 2012
Data integration and cleaning › schema mapping
XML-to-relational mapping		 0.322014
Mapping XML to a Wide Sparse Table · IEEE Trans. Knowl. Data Eng. 2014
Mapping XML to a Wide Sparse Table · ICDE 2012
Data models and query languages
XML		 0.212014
Mapping XML to a Wide Sparse Table · IEEE Trans. Knowl. Data Eng. 2014
Data models and query languages
XML data management		 0.112012
Mapping XML to a Wide Sparse Table · ICDE 2012

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

join reduction · 0.2XPath-to-SQL translation · 0.2sparse table mapping · 0.1
YearPublicationVenuePosition
2014 Mapping XML to a Wide Sparse Table
abstract
XML is commonly supported by SQL database systems. However, existing mappings of XML to tables can only deliver satisfactory query performance for limited use cases. In this paper, we propose a novel mapping of XML data into one wide table whose columns are sparsely populated. This mapping provides good performance for document types and queries that are observed in enterprise applications but are not supported efficiently by existing work. XML queries are evaluated by translating them into SQL queries over the wide sparsely-populated table. We show how to translate full XPath 1.0 into SQL. Based on the characteristics of the new mapping, we present rewriting optimizations that dramatically reduce the number of joins. Experiments demonstrate that query evaluation over the new mapping delivers considerable improvements over existing techniques for the target use cases.
Liang Jeff Chen, Philip A. Bernstein, Peter Carlin, Dimitrije Filipovic, Michael Rys, Nikita Shamgunov, James F. Terwilliger, Milos Todic, Sasa Tomasevic, Dragan Tomic
 IEEE Trans. Knowl. Data Eng.4
2012 Mapping XML to a Wide Sparse Table
abstract
XML is commonly supported by SQL database systems. However, existing mappings of XML to tables can only deliver satisfactory query performance for limited use cases. In this paper, we propose a novel mapping of XML data into one wide table whose columns are sparsely populated. This mapping provides good performance for document types and queries that are observed in enterprise applications but are not supported efficiently by existing work. XML queries are evaluated by translating them into SQL queries over the wide sparsely-populated table. We show how to translate full XPath 1.0 into SQL. Based on the characteristics of the new mapping, we present rewriting optimizations that minimize the number of joins. Experiments demonstrate that query evaluation over the new mapping delivers considerable improvements over existing techniques for the target use cases.
Liang Jeff Chen, Philip A. Bernstein, Peter Carlin, Dimitrije Filipovic, Michael Rys, Nikita Shamgunov, James F. Terwilliger, Milos Todic, Sasa Tomasevic, Dragan Tomic
 ICDE4