Stream processing is used in various fields. In the field of big data, stream aggregation is a
popular processing technique, but it suffers serious setbacks when the order of events (eg …

The number of real-time information sources, or so-called streams, has rapidly increased,
leading to a greater demand for complex analyses over streams. Although many stream …

Sliding-window aggregation is one of the core operations in processing and analyzing data
streams, but it seriously suffers from the unordered events or elements from data streams …

Predicting missing attribute values in data streams is useful in boosting the accuracies of
analytical results in many applications. Many algorithms (ie, Distance Likelihood …

Stream processing engines need to process multiple queries over streams simultaneously,
and continuous window aggregation plays a critical role in various applications as a part of …

Purpose–In purpose of this paper is to propose a novel scheme to process XPath-based
keyword search over Extensible Markup Language (XML) streams, where one can specify …

Forecasting time-series data is useful in many fields, such as stock price predicting system,
autonomous driving system, weather forecast, etc. Many existing forecasting models tend to …

Stream processing has been extensively applied in wide varieties of fields. In the meantime,
stream aggregating is a popular processing technique of big data. However, it suffers from …

Predicting missing attribute values in data streams is useful in boosting the accuracies of
analytical results in many applications. Many algorithms (ie, Distance Likelihood …

With the rapid growth of real-time information from various sources, continuous query
processing over data streams has become increasingly important. To extract information …