While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

Various graphs such as web or social networks may contain up to trillions of edges.
Compressing such datasets can accelerate graph processing by reducing the amount of I/O …

In massive and rapid graph streams, a useful and important task is to summarize the
structure of graph streams in order to enable efficient and effective graph query processing …

Burst is a common pattern in data streams which is characterized by a sudden increase in
terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive …

As graph analytics often involves compute-intensive operations, GPUs have been
extensively used to accelerate the processing. However, in many applications such as social …

Data stream processing is a fundamental issue in many fields, such as data mining,
databases, network traffic measurement. There are five typical tasks in data stream …

Given a fully dynamic graph, represented as a stream of edge insertions and deletions, how
can we obtain and incrementally update a lossless summary of its current snapshot? As …

The increasing scale of encyclopedic knowledge graphs (KGs) calls for summarization as a
way to help users efficiently access and distill world knowledge. Motivated by the disparity …

Continuous subgraph matching (CSM) algorithms find the occurrences of a given pattern on
a stream of data graphs online. A number of incremental CSM algorithms have been …

Approximate stream processing has attracted much attention recently. Prior art mostly
focuses on characteristics like frequency, cardinality, and quantile. Persistence, as a new …