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Sequence Data Mining (Advances in Database Systems)

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Sequences are an important type of data which occur frequently in many scientific, medical, security, business and other applications. For example, DNA sequences encode the genetic makeup of humans and all species, and protein sequences describe the amino acid composition of proteins and encode the structure and function of proteins. Moreover, sequences can be used to capture how individual humans behave through various temporal activity histories such as weblogs and customer purchase histories. Sequences can also be used to describe how organizations behave through sales histories such as the total sales of various items over time for a supermarket, etc.

Huge amounts of sequence data have been and continue to be collected in genomic and medical studies, in security applications, in business applications, etc. In these applications, the analysis of the data needs to be carried out in different ways to satisfy different application requirements, and it needs to be carried out in an efficient manner. Sequence data mining provides the necessary tools and approaches for unlocking useful knowledge hidden in the mountains of sequence data. The purpose of this book is to present some of the main concepts, techniques, algorithms, and references on sequence data mining.

This introductory chapter has four goals. First, it will provide some example applications of sequence data. Second, it will define several basic/generic concepts for sequences and sequence data mining. Third, it will discuss the major issues of interest in data mining research. Fourth, it will give an overview of the entire book.
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