Data streams are ubiquitous. Examples range from sensor networks to financial transactions and website logs. In fact, even market basket data can be seen as a stream of sales. Detecting changes in the distribution a stream is sampled from is one of the most challenging problems in stream mining, as only limited storage can be used. In this paper we analyse this problem for streams of transactio...

in this paper, modeling and optimization of fischer-tropsch synthesis is considered in a fixed-bed catalytic reactor using an industrial fe-cu-k catalyst. a one dimensional pseudo-homogenous plug flow model without axial dispersion is developed for converting syngas to heavy hydrocarbons. the effects of temperature, pressure, h2 to co ratio in feed stream, and co molar flow on the mass flow rat...

Stream clustering is a fundamental problem in many streaming data analysis applications. Comparing to classical batchmode clustering, there are two key challenges in stream clustering: (i) Given that input data are changing continuously, how to incrementally update clustering results efficiently? (ii) Given that clusters continuously evolve with the evolution of data, how to capture the cluster...

Evolving data streams are ubiquitous. Various clustering algorithms have been developed to extract useful knowledge from evolving data streams in real time. Density-based clustering method has the ability to handle outliers and discover arbitrary shape clusters whereas grid-based clustering has high speed processing time. Sliding window is a widely used model for data stream mining due to its e...

A listener is thought to be able to organise musical notes into groups within musical streams/voices. A stream segment is a relatively short coherent sequence of tones that is separated horizontally from co-sounding streams and, vertically from neighbouring musical sequences. This paper presents a novel algorithm that discovers musical stream segments in symbolic musical data. The proposed algo...

In Data Stream Management Systems (DSMS) semi-stream processing has become a popular area of research due to the high demand of applications for up-to-date information (e.g. in real-time data warehousing). A common operation in stream processing is joining an incoming stream with disk-based master data, also known as semi-stream join. This join typically works under the constraint of limited ma...

Due to recent advances in data collection techniques, massive amounts of data are being collected at an extremely fast pace. Also, these data are potentially unbounded. Boundless streams of data collected from sensors, equipments, and other data sources are referred to as data streams. Various data mining tasks can be performed on data streams in search of interesting patterns. This paper studi...

Tail-MAC, A predecessor to the VMPC-MAC, algorithm for computing Message Authentication Codes for stream ciphers is described along with the analysis of its security. The proposed algorithm was designed to employ some of the data already computed by the underlying stream cipher in the purpose of minimizing the computational cost of the operations required by the MAC algorithm. The performed ana...

––Data de-duplication is a very simple concept with very smart technology associated in it. The data blocks are stored only once, de-duplication systems decrease storage consumption by identifying distinct chunks of data with identical content. They then store a single copy of the chunk along with metadata about how to reconstruct the original files from the chunks, this takes up the less stora...