Data Stream Mining Addresses Research Issues Addressed by the Data Mining Community

Data stream mining is a stimulating field of study that has raised challenges and research issues to be addressed by the database and data mining communities. The following is a discussion of both addressed and open research issues [19].

Handling the continuous flow of data streams
This is a data management issue. Traditional database management systems are not capable of dealing with such continuous high data rate. Novel indexing, storage and querying techniques are required to handle this non stopping fluctuated flow of information streams.

Minimizing energy consumption of the mobile device
Large amounts of data streams are generated in resource-constrained environments. Sensor networks represent a typical example. These devices have short life batteries. The design of techniques that are energy efficient is a crucial issue given that sending all the generated stream to a central site is energy inefficient in addition to its lack of scalability problem.

Unbounded memory requirements due to the continuous flow of data streams
Machine learning techniques represent the main source of data mining algorithms. Most of machine learning methods require data to be resident in memory while executing the analysis algorithm. Due to the huge amounts of the generated streams, it is absolutely a very important concern to deign space efficient techniques that can have only one look or less over the incoming stream.

Required result accuracy
Design a space and time efficient techniques should be accompanied with acceptable result accuracy. Approximation algorithms as mentioned earlier can guarantee error bounds. Also sampling Techniques adopt the same concept as it has been used in VFML.

Transferring data mining results over a wireless ...

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... to the available resources and being able to adjust according to the available resources.

The data stream computing formalization
Mining of data streams is required to be formalized within a theory of data stream computation. This formalization would facilitate the design and development of algorithms based on a concrete mathematical foundation. Approximation techniques and statistical learning theory represent the potential basis for such a theory. Approximation techniques could provide the solution, and using statistical learning theory would provide the loss function of the mining problem. The above issues represent the grand challenges to the data mining community in this essential field. There is a real need inspired by the potential applications in astronomy and scientific laboratories as well as business applications to address the above research problems.