Conventional stream mining algorithms focus on single and stand-alone mining tasks. Given the single-pass nature of data streams, it makes sense to maximize throughput by performing multiple complementary mining tasks concurrently. We investigate the potential of concurrent semi-supervised learning on data streams and propose an incremental algorithm called CSL-Stream (Concurrent Semi–supervise...

With the rapid development of Internet, the internet of things and other information technology, big data usually exists in cyberspace as the form of the data stream. It brings great benefits for information society. Meanwhile, it also brings crucial challenges on big data mining in the data stream. Recently, academic and industrial communities have a widespread concern on massive data mining p...

High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. The existing sliding window-based HUP mining algorithms over stream data suffer from the level-wise candidate generationand-test problem. Therefore, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve the...

Despite growing interest in data stream mining the most successful incremental learners still use periodic recomputation to update attribute information gains and Gini indices. This note provides simple incremental formulas and algorithms for computing entropy and Gini index from time-changing data streams.

In this paper, we introduce new methods for multiobjective, system-level optimization that have been incorporated into the Lightweight Dataflow for Dynamic Data Driven Application Systems (DDDAS) Environment (LiD4E). LiD4E is a design tool for optimized implementation of dynamic, data-driven stream mining systems using high-level dataflow models of computation. More specifically, we develop in ...

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that a...

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...

Mining data streams has been a focal point of research interest over the past decade. Hardware and software advances have contributed to the significance of this area of research by introducing faster than ever data generation. This rapidly generated data has been termed as data streams. Credit card transactions, Google searches, phone calls in a city, and many others\are typical data streams. ...

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...