Leonhard Rose

dblp:358/2064 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Databases, data management, data science and information retrieval · 1 · 1 since 2021

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
Distributed and cloud data management · 87% Data integration and cleaning · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed and cloud data management
distributed query processing
0.712023
XDB in Action: Decentralized Cross-Database Query Processing for Black-Box DBMSes · Proc. VLDB Endow. 2023
Distributed and cloud data management › federated database
federated query processing
0.712023
XDB in Action: Decentralized Cross-Database Query Processing for Black-Box DBMSes · Proc. VLDB Endow. 2023
Data integration and cleaning › mediator systems
mediator-wrapper architecture
0.212023
XDB in Action: Decentralized Cross-Database Query Processing for Black-Box DBMSes · Proc. VLDB Endow. 2023

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

optimization techniques · 0.7black-box DBMS integration · 0.7
YearPublicationVenuePosition
2023 XDB in Action: Decentralized Cross-Database Query Processing for Black-Box DBMSes
abstract
Data are naturally produced at different locations and hence stored on different DBMSes. To maximize the value of the collected data, today's users combine data from different sources. Research in data integration has proposed the Mediator-Wrapper (MW) architecture to enable ad-hoc querying processing over multiple sources. The MW approach is desirable for users, as they do not need to deal with heterogeneous data sources. However, from a query processing perspective, the MW approach is inefficient: First, one needs to provision the mediating execution engine with resources. Second, during query processing, data gets "centralized" within the mediating engine, which causes redundant data movement. Recently, we proposed in-situ cross-database query processing , a paradigm for federated query processing without a mediating engine. Our approach optimizes runtime performance and reduces data movement by leveraging existing systems, eliminating the need for an additional federated query engine. In this demonstration, we showcase XDB, our prototype for in-situ cross-database query processing. We demonstrate several aspects of XDB, i.e. the cross-database environment, our optimization techniques, and its decentralized execution phase.
Haralampos Gavriilidis, Leonhard Rose, Joel Ziegler, Kaustubh Beedkar, Jorge-Arnulfo Quiané-Ruiz, Volker Markl
Proc. VLDB Endow.2