ABSTRACT
This chapter provides an overview of the key challenges in stream mining and describes stream mining systems, and algorithms designed to overcome these. It discusses the generic challenges that stream mining poses to a variety of data management and data mining problems. The chapter focuses on different algorithms for stream data reduction and stream mining. The key stream processing and mining challenges include stream data management, relational processing on streams, stream indexing, and stream mining. The problem of indexing data streams attempts to create an indexed representation, so that it is possible to efficiently answer different kinds of queries such as aggregation queries or trend-based queries. The types of analysis that need to be performed on streaming data can be arbitrarily complex and may include several sophisticated learning techniques. Stream processing systems are typically designed in the form of distributed systems that are designed to tackle the complex data management and processing issues associated with high-volume data streams.
