While data provenance is a relatively well-studied topic in both the fields of databases and workflow systems, its support within stream processing systems presents a new set of challenges. Given the potentially high event rate of the input streams and the low processing latency requirements imposed by many streaming applications, capturing data provenance effectively in a stream processing sys...

Smart home technology is a better choice for the people to care about security, comfort and power saving as well. It is required to develop technologies that recognize the Activities of Daily Living (ADLs) of the residents at home and detect the abnormal behavior in the individual's patterns. Data mining techniques such as Frequent pattern mining (FPM), High Utility Pattern (HUP) Mining were us...

An important component of near-real-time data warehouses is the near-real-time integration layer. One important element in near-real-time data integration is the join of a continuous input data stream with a disk-based relation. For high-throughput streams, stream-based algorithms, such as Mesh Join (MESHJOIN), can be used. However, in MESHJOIN the performance of the algorithm is inversely prop...

Synchronous stream cipher HENKOS is proposed by Marius Oliver Gheorghit to eprint for analyzing the algorithm by cryptologic community. At the first look of HENKOS, it can be observed that a 256 byte secret key is an undesirable quality of the algorithm. Also, the algorithm is not efficient in case of streaming data that has to encrypt instantaneously, this is because the algorithm generates a ...

The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time. This paper describes a data stream classification algorith...

We consider the read/write streams model, an extension of the standard data stream model in which an algorithm can create and manipulate multiple read/write streams in addition to its input data stream. Like the data stream model, the most important parameter for this model is the amount of internal memory used by such an algorithm. The other key parameters are the number of streams the algorit...

Prominence of data streams has dragged the interest of many researchers in the recent past. Mining associative rules generated on data streams for prediction has raised greater research interest in recent years. Associative classification mining has shown better performance over many former classification techniques in Data Mining and Data Stream Mining domains. This paper introduces a new tech...

Data streams have recently attracted attention for their applicability to numerous domains including credit fraud detection, network intrusion detection, and click streams. Stream clustering is a technique that performs cluster analysis of data streams that is able to monitor the results in real time. A data stream is continuously generated sequences of data for which the characteristics of the...