Window aggregations and windowed joins are central operators of modern real-time
analytic workloads and significantly impact the performance of stream processing systems …

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 …

Graph databases, which essentially store network nodes and edge relationships between
them, offer a promising solution for managing the large and dynamic Internet of Things (IoT) …

Predicting time-series data is useful in many applications, such as natural disaster
prevention system, weather forecast, traffic control system, etc. Time-series forecasting has …

An aggregation is a function from a collection of data items to an aggregate value. In
slidingwindow aggregation, the input collection consists of a window over the most recent …

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 …

Sliding-window aggregation is a widely-used approach for extracting insights from the most
recent portion of a data stream. While aggregations of interest can usually be expressed as …

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 …

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 …