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Songyun Duan

Researcher at IBM

Publications -  40
Citations -  1713

Songyun Duan is an academic researcher from IBM. The author has contributed to research in topics: RDF & Query optimization. The author has an hindex of 20, co-authored 40 publications receiving 1616 citations. Previous affiliations of Songyun Duan include Duke University & Microsoft.

Papers
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Journal ArticleDOI

Tuning database configuration parameters with iTuned

TL;DR: ITuned is described, a tool that automates the task of identifying good settings for database configuration parameters and has three novel features: a technique called Adaptive Sampling that proactively brings in appropriate data through planned experiments to find high-impact parameters and high-performance parameter settings.
Proceedings ArticleDOI

Apples and oranges: a comparison of RDF benchmarks and real RDF datasets

TL;DR: This paper compares data generated with existing RDF benchmarks and data found in widely used real RDF datasets and shows that simple primitive data metrics are inadequate to flesh out the fundamental differences between real and benchmark data.
Proceedings ArticleDOI

Scalable Multi-query Optimization for SPARQL

TL;DR: This paper revisits the classical problem of multi-query optimization in the context of RDF/SPARQL and proposes heuristic algorithms that partition the input batch of queries into groups such that each group of queries can be optimized together.
Patent

Predictive Analytics for Semi-Structured Case Oriented Processes

TL;DR: In this article, a method for predictive analytics for a process includes receiving at least one trace of the process, building a probabilistic graph, determining content at each node, modeling each decision node as a respective decision tree, and predicting, for an execution of a process, a path in the probabilism graph from any decision node to a prediction target node of a plurality of prediction target nodes given the content.
Journal ArticleDOI

Scalable Keyword Search on Large RDF Data

TL;DR: This work proposes an effective summarization algorithm to summarize the RDF data and shows that the summaries built lend significant pruning powers to exploratory keyword search and result in much better efficiency compared to previous works.