In this paper we define a Bayesian framework that uses noisy, but redundant data from a network of sensors that include multiple sensor streams of different types. It merges the data with the contextual and domain knowledge that is provided by both the physical constraints imposed by the local environment and by the people that are involved in the surveillance tasks. The paper also presents the...

In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and ob...

Identity crime has increased enormously over the recent years. Spike detection is important because it highlights sudden and sharp rises in intensity relative to the current identity attribute value (which can be indicative of abuse). This paper proposes the new spike analysis framework for monitoring sparse personal identity streams. For each identity example, it detects spikes in single attri...

In today’s applications, massive, evolving data streams are ubiquitous. To gain useful information from this data, real time clustering analysis for streams is needed. A multitude of stream clustering algorithms were introduced. However, assessing the effectiveness of such an algorithm is challenging, because up to now there is no tool that allows a direct comparison of these algorithms. We pre...

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computin...

The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a few one-pass clustering algorithms have been developed for the data stream problem. Although such methods address the scalability issues of the clustering problem, they are generally blind...

Mining data streams has recently become an important and challenging task for a wide range of services, including credit card fraud detection, sensor networks and web applications. In these applications data do not typically take the form of persistent relations, but tend to arrive in multiple, continuous, rapid and timevarying data streams. Hence, conventional knowledge discovery tools cannot ...

Clustering of data streams finds important applications in tracking evolution of various phenomena in medical, meteorological, astrophysical, seismic studies. Algorithms designed for this purpose are capable of adapting the discovered clustering model to the changes in data characteristics but are not capable of adapting to the user’s requirements themselves. Based on the previous observation, ...