Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-ness to rank them. The second part involves filtering ...

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...

Article history: Received 14 January 2016 Received in revised form 21 October 2016 Accepted 29 October 2016 Available online 23 November 2016

In this paper, we propose a scalable method of proactively replicating a subset of remote datasets for RDF Stream Processing. Our solution achieves a fast query processing by maintaining the replicated data up-to-date before query evaluation. To construct the replication process effectively, we present an update estimation model to handle the changes in updates over time. With the update estima...

Stream reasoning is an exciting multidisciplinary research area that combines stream processing and semantic reasoning. Its goal is to not only process a dynamic data stream, but also to extract explicit and implicit information on-the-fly. One of its challenges is managing history awareness: how much and which historical data should be held and for how long as we continuously query and reason ...

Knowledge embedded in a data stream is likely to be changed as time goes by. Identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most mining algorithms over a data stream are not able to extract the recent change of knowledge in a data stream adaptively. This is because the obsolete information of old data element...

Established statistical representations of data clusters employ up to second order statistics including mean, variance, and covariance. Strategies for merging clusters have been largely based on intraand inter-cluster distance measures. The distance concept allows an intuitive interpretation, but it is not designed to merge from the viewpoint of probability distributions. We suggest an alternat...

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that a...

In this paper, we describe several enhancement techniques to improve the state-of-the-art stream prefetcher. First, the enhanced stream prefetcher takes streams with long stride into consideration to avoid wasteful prefetches. Second, accessing a node in a tree or graph structure may have a different direction than the traversal direction through the structure. The enhanced stream prefetcher el...

The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Many applications rely directly or indirectly on finding the frequent items, and implementations are in use in large scale industrial systems. However, there has not been mu...