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The dramatic rise of streaming time-series data produced in a vari- ety of contexts, such as stock markets, mobile sensing, sensor net- works, data centre monitoring, etc., has fuelled the development of large-scale distributed real-time computation system ...
As various kinds of sensors penetrate our daily life (e.g., sensor networks for environmental monitoring, GPS for localization and navigation), the efficient management of massive amount of sensor data becomes increasingly important at present. Many sensor ...
Infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Model-based sensor data approximation reduces the amount of data for query processing, but all modeled segments need to be scanned, in the worst c ...
A new in silica approach allowing to compute HPLC-UV response coefficients using Time Dependent-Density Functional Theory (TD-DFT) is here reported. Based on the use of a non linear least squares with bound restricted algorithm, this model exploits theoret ...
The dramatic rise of time-series data in a variety of contexts, such as social networks, mobile sensing, data centre monitoring, etc., has fuelled interest in obtaining real-time insights from such data using distributed stream processing systems. One such ...
Infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Model-based sensor data approximation reduces the amount of data for query processing, but all modeled segments need to be scanned, in the worst c ...
The dramatic rise of time-series data produced in a variety of contexts, such as stock markets, mobile sensing, sensor networks, data centre monitoring, etc., has fuelled the development of large-scale distributed real-time computation systems (e.g., Apach ...
In the context of anomaly detection in cyber physical systems (CPS), spatiotemporal correlations are crucial for high detection rate. This work presents a new quarter sphere support vector machine (QS-SVM) formulation based on the novel concept of attribut ...
Computing statistical measures for large databases of time series is a fundamental primitive for querying and mining time-series data [1]–[6]. This primitive is gaining importance with the increasing number and rapid growth of time series databases. In thi ...
As the number of sensors that pervade our lives increases (e.g., environmental sensors, phone sensors, etc.), the efficient management of massive amount of sensor data is becoming increasingly important. The infinite nature of sensor data poses a serious chal ...
