Operator-aware Approach for Boosting Performance in RDF Stream Processing

Danh Le-Phuoc

Abstract


To enable efficiency in stream processing, the evaluation of a query is usually performed over bounded parts of (potentially)
unbounded streams, i.e., processing windows “slide” over the streams. To avoid inefficient re-evaluations of already evaluated parts
of a stream in respect to a query, incremental evaluation strategies are applied, i.e., the query results are obtained incrementally
from the result set of the preceding processing state without having to re-evaluate all input buffers. This method is highly efficient
but it comes at the cost of having to maintain processing state, which is not trivial, and may defeat performance advantages of the
incremental evaluation strategy. In the context of RDF streams the problem is further aggravated by the hard-to-predict evolution
of the structure of RDF graphs over time and the application of sub-optimal implementation approaches, e.g., using relational
technologies for storing data and processing states which incur significant performance drawbacks for graph-based query patterns.
To address these performance problems, this paper proposes a set of novel operator-aware data structures coupled with incremental
evaluation algorithms which outperform the counterparts of relational stream processing systems. This claim is demonstrated through
extensive experimental results on both simulated and real datasets.

Full Text: Untitled
Type of Paper: Research paper
Keywords: Continuous queries, Linked Stream Data, Linked Data, Semantic Web, stream processing
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