RC4 encryption algorithm [3,12,16] was examined based on the text to be encrypted and the chosen key. It is a symmetric key algorithm and uses stream cipher i.e. data is being encrypted bit by bit. The same algorithm is used for both encryption and decryption. The data stream is XORed with the generated key sequence and the key stream is completely independent of the plaintext used. The weaknes...

The sensor networks, web click stream and astronomical applications generate a continuous flow of data streams. Most likely data streams are generated in a wireless environment. These data streams challenge our ability to store and process them in real-time with limited computing capabilities of the wireless environment. Querying and mining data streams have attracted attention in the past two ...

Recently many researchers have focused on mining data streams and they proposed many techniquesand algorithms for data streams. It refers to the process of extracting knowledge from nonstop fast growing data records. They are data stream classification, data stream clustering, and data stream frequentpattern items and so on. Data stream clustering techniques are highly helpful to cluster the si...

This study focuses on the summary data structure design and optimize the method of calculation of the mesh density and how to effectively deal with the problem of boundary points, combined with the sliding window mechanism and suggest improvements based on the mesh density of the data stream real-time clustering algorithm framework and the various parts of concrete realization of the algorithm.

Big data quickly comes under the spotlight in recent years. As big data is supposed to handle extremely huge amount of data, it is quite natural that the demand for the computational environment to accelerates, and scales out big data applications increases. The important thing is, however, the behavior of big data applications is not clearly defined yet. Among big data applications, this paper...

The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, high sparsity, and high class-imbalance. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers...

We propose a new on-line ε-approximation algorithm for mining closed itemsets from a transactional data stream, which is also based on the incremental/cumulative intersection principle. The proposed algorithm, called LC-CloStream, is constructed by integrating CloStream algorithm and Lossy Counting algorithm. We investigate some behaviors of the LC-CloStream algorithm. Firstly we show the incom...

Aligning coordinated text streams from multiple sources and multiple languages has opened many new research venues on cross-lingual knowledge discovery. In this paper we aim to advance state-of-the-art by: (1). extending coarse-grained topic-level knowledge mining to fine-grained information units such as entities and events; (2). following a novel “Datato-Network-to-Knowledge (D2N2K)” paradigm...

Multi-Pass Models: It is common in graph mining to consider algorithms that may take more than one pass over the stream. There has also been work in the W-Stream model in which the algorithm is allowed to write to the stream during each pass [9]. These annotations can then be utilized by the algorithm during successive passes and it can be shown that this gives sufficient power to the model for...

In this paper we present our experience with an Image Guided Neurosurgery Grid-enabled Software Environment (IGNS-GSE) which integrates real-time acquisition of intraoperative Magnetic Resonance Imaging (IMRI) with the preoperative MRI, fMRI, and DT-MRI data. We describe our distributed implementation of a non-rigid image registration method which can be executed over the Grid. Previously, nonr...