With a surge of stream processing applications novel techniques are required for generation and analysis of association rules in streams. The traditional rule mining solutions cannot handle streams because they generally require multiple passes over the data and do not guarantee the results in a predictable, small time. Though researchers have been proposing algorithms for generation of rules f...

The most widespread approach to automatic language identification in the past has been the statistical modeling of phone sequences extracted from speech signals. Recently, we have developed an alternative approach to LID based on n-gram modeling of parallel streams of articulatory features, which was shown to have advantages over phone-based systems on short test signals whereas the latter achi...

We introduce a novel framework for the expression, rapid-prototyping, and evaluation of statistical machine-translation (MT) systems using graphical models. The framework extends dynamic Bayesian networks with multiple connected different-length streams, switching variable existence and dependence mechanisms, and constraint factors. We have implemented a new general-purpose MT training/decoding...

in this paper, a new systematic design methodology has been developed for thes imultaneous energy and water minimization in multiple-contaminant systems that also feature maximum re-use of water. in addition to allowing re-use of water in multiple-contaminant systems, issues about heat losses and flowrate changes inside unit operations have also been incorporated in this new design method. to i...

In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scenario that we consider is quite intuitive; let's assume that a research or financial analyst is se...

As a leading framework for data intensive computing, MapReduce has gained enormous popularity in large-scale data analysis. With the increasing adoption of multi/many core platform, more and more MapReduce tasks are now running on the same node and sharing the same storage resources. The concurrency of tasks raises the issue of I/O stream congestion. We have observed significant throughput drop...