A modified Fisher discriminate analysis method for classifying stream data is presented. To satisfy the realtime demand in classifying stream data, this method defines a new criterion for Fisher discriminate analysis. Since the new criterion requires less computation and memory space, it is much faster and more suitable for online processing in stream data environment. It can overcome the probl...

This is a short survey of my work in data streams and related applications, such as communication complexity, numerical linear algebra, and sparse recovery. The goal is give a non-technical overview of results in data streams, and highlight connections between these different areas. It is based on my Presburger lecture award given at ICALP, 2014. 1 The Data Stream Model Informally speaking, a d...

The WLAN is a network that utilizes radio frequency technology. The security of wireless data stream becomes particularly prominent. The WLAN uses RC4 stream encryption algorithm of the WEP protocol to enhance its Security.WEP itself also has fatal Security flaws, tampering with the data for a variety of active attacks. In essence, the problem is not in RC4 itself but in the way to generate the...

The paper presents a new Cyber Physical Stream algorithm for selecting a predominant item from very large collections of data. The algorithm effectively works for frequencies of the predominant items starting from about 2%. The algorithm is focused on querying massive data in Software-Defined Storage combined with Fuzzy indexing method. Experiment results show that Cyber Physical Stream algorit...

We consider the problem of data-stream classification, introducing a stream-classification algorithm, Dynamic Streaming Random Forests, that is able to handle evolving data streams using an entropy-based drift-detection technique. The algorithm automatically adjusts its parameters based on the data seen so far. Experimental results show that the algorithm handles multi-class problems for which ...

A new algorithm is presented, designed to solve tridiagonal matrix problems efficientiy with parallel computers (multiple instruction stream, multiple data stream (MIMD) machines with distributed memory). The algorithm is designed to be extendable to higher order banded diagonal systems.

In the field of real-time data warehousing semistream processing has become a potential area of research since last one decade. One important operation in semi-stream processing is to join stream data with a slowly changing diskbased master data. A join operator is usually required to implement this operation. This join operator typically works under limited main memory and this memory is gener...

Data streaming applications are becoming more and more common due to the rapid development in the areas such as sensor networks, multimedia streaming, and on-line data mining, etc. These applications are often running in a decentralized, distributed environment. The requirements for processing large volumes of streaming data at real time have posed many great design challenges. It is critical t...

Semi-stream processing has become an emerging area of research in the field of data stream management. One common operation in semi-stream processing is joining a stream with disk-based master data using a join operator. This join operator typically works under limited main memory and this memory is generally not large enough to hold the whole disk-based master data. Recently, a number of semi-...

Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream