  In many real applications such as the data integration, social network
analysis, and the Semantic Web, the entity resolution (ER) is an important and
fundamental problem, which identifies and links the same real-world entities
from various data sources. While prior works usually consider ER over static
and complete data, in practice, application data are usually collected in a
streaming fashion, and often incur missing attributes (due to the inaccuracy of
data extraction techniques). Therefore, in this paper, we will formulate and
tackle a novel problem, topic-aware entity resolution over incomplete data
streams (TER-iDS), which online imputes incomplete tuples and detects pairs of
topic-related matching entities from incomplete data streams. In order to
effectively and efficiently tackle the TER-iDS problem, we propose an effective
imputation strategy, carefully design effective pruning strategies, as well as
indexes/synopsis, and develop an efficient TER-iDS algorithm via index joins.
Extensive experiments have been conducted to evaluate the effectiveness and
efficiency of our proposed TER-iDS approach over real data sets.
