![](bg8.png)
processing (Lian and Chen, 2009; Lian and Chen,
2011; Shen et al., 2014). As already mentioned, only
a fixed amount of recent stream data is computed at
each query evaluation in this model (Babcock et al.,
2002). In contrast, the temporal similarity adopted
here induces a fixed temporal window (i.e., the time
horizon) with a variable amount of stream data.
Streaming similarity search finds all data objects
that are similar to a given query (Kraus et al., 2017).
To some extent, similarity join can be viewed as a
sequence of searches using each arriving object as a
query object. A fundamental difference in this con-
text is that the threshold is fixed for joins, while it can
vary along distinct queries for searches.
Top-k queries have also been studied in the
streaming setting (Shen et al., 2014; Amagata et al.,
2019). Focusing on streams of vectors, Shen et al.
(Shen et al., 2014) proposed a framework supporting
queries with different similarity functions and win-
dow sizes. Amagata et al. (Amagata et al., 2019)
presented an algorithm for kNN self-join, a type top-k
query that finds the k most similar objects for each ob-
ject. This work assumes objects represented as sets,
however the dynamic scenario considered is very dif-
ferent: instead of a stream of sets, the focus is on a
stream of updates continuously inserting and deleting
elements of existing sets.
Finally, duplicate detection in streams is a well-
studied problem (Metwally et al., 2005; Deng and
Rafiei, 2006; Dutta et al., 2013). A common ap-
proach to dealing with unbounded streams is to em-
ploy space-preserving, probabilistic data structures,
such as Bloom Filters and Quotient Filters together
with window models. However, these proposals aim
at detecting exact duplicates and, therefore, similarity
matching is not addressed.
6 CONCLUSIONS AND FUTURE
WORK
This paper presented a new algorithm called SSTR
for set similarity join over set streams. To the best of
our knowledge, set similarity join has not been previ-
ously investigated in a streaming setting. We adopted
the concept of temporal similarity and exploited its
properties to reduce processing cost and memory us-
age. We reported an extensive experimental study on
synthetic and real-world datasets, whose results con-
firmed the efficiency of our solution. Future work is
mainly oriented towards designing a parallel version
of SSTR and an algorithmic framework for seamless
integration with batch processing models.
ACKNOWLEDGEMENTS
This work was partially supported by Brazilian
agency CAPES.
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SSTR: Set Similarity Join over Stream Data
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