The study seeks to address the importance of urban stream ecosystems from the perspective of urban ecology, human health and social well-being in the context of urban planning. The case study area is Paveh stream in the City of Paveh. The data from the case study area were gathered from questionnaire, existing scientific and library studies and by conducting interviews with residents and auth...

Stream-learn is a Python package compatible with scikit-learn and developed for the drifting imbalanced data stream analysis. Its main component generator, which allows producing synthetic that may incorporate each of three concept drift types (i.e., sudden, gradual incremental drift) in their recurring or non-recurring version, as well static dynamic class imbalance. The conducting experiments...

In large companies such as Siemens and Statoil monitoring tasks are of great importance, e.g., Siemens does monitoring of turbines and Statoil of oil behaviour in wells. This tasks bring up importance of both streaming and historical (temporal) data in the Big Data challenge for industries. We present the Optique project that addresses this problem by developing an Ontology Based Data Access (O...

The main idea behind this research relies on analytical data mining functions to handle data streams. Given the characteristics of the data stream, the new methods and techniques for stream data analysis must conduct advanced analysis and data mining over fast and large data streams to capture the trends, patterns and exceptions. Besides, much of such data resides at rather low level of abstrac...

Big data time series data streams are ubiquitous in finance, meteorology and engineering. It may be impossible to process an entire “big data” continuous data stream or to scan through it multiple times due to its tremendous volume. In Heraclitus’s well-known saying, “you never step in the same stream twice,” and so it is with “big data” temporal data streams. Unlike traditional data sets, big ...

We present a stream data generator. The generator is mainly intended for multiple interrelated streams, in particular for objects with temporal properties, which are fed by dependent streams. Such data are e.g. customers their transactions: learning a model of the customers requires considering the stream of their transactions. However, the generator can also be used for conventional stream dat...

in order to determine hydrological behavior and water management of sepidroud river (north of iran-guilan) the present study has focused on stream flow prediction by using artificial neural network. ten years observed inflow data (2000-2009) of sepidroud river were selected; then these data have been forecasted by using neural network. finally, predicted results are compared to the observed dat...