In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact tha...
Data stream classification poses many challenges, most of which are not addressed by the state-of-the-art. We present DXMiner, which addresses four major challenges to data stream ...
Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Kh...
Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for class...
Abstract--The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two s...
Mohammad M. Masud, Qing Chen, Latifur Khan, Charu ...
This paper proposes MISSA, a novel middleware to facilitate the development and provision of stream-based services in emerging pervasive environments. The streambased services util...
—Duplicates in data streams may often be observed by the projection on a subspace and/or multiple recordings of objects. Without the uniqueness assumption on observed data elemen...
With the rapid development of information technology, many applications have to deal with potentially infinite data streams. In such a dynamic context, storing the whole data stre...
Abstract— In conventional wireless systems with layered architectures, the physical layer treats all data streams from upper layers equally and apply the same modulation and codi...
—Many emerging information processing applications require applying various fork and join type operations such as correlation, aggregation, and encoding/decoding to data streams ...
Haiquan (Chuck) Zhao, Cathy H. Xia, Zhen Liu, Dona...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Ouliter deTector (SPOT), t...