tailieunhanh - Data Streams Models and Algorithms- P5

Data Streams Models and Algorithms- P5: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time. | Multi-Dimensional Analysis of Data Streams Using Stream Cubes 105 high levels such as by region and by quarter of an hour making timely power supply adjustments and handling unusual situations. o One may easily link such multi-dimensional analysis with the online analytical processing of multi-dimensional nonstream data sets. For analyzing the characteristics of nonstream data the most influential methodology is to use data warehouse and OLAP technology 14 11 . With this technology data from different sources are integrated and then aggregated in multi-dimensional space either completely or partially generating data cubes. The computed cubes can be stored in the form of relations or multi-dimensional arrays 1 31 to facilitate fast on-line data analysis. In recent years a large number of data warehouses have been successfully constructed and deployed in applications and data cube has become an essential component in most data warehouse systems and in some extended relational database systems for multidimensional data analysis and intelligent decision support. Can we extend the data cube and OLAP technology from the analysis of static pre-integrated data to that of dynamically changing stream data including time-series data scientific and engineering data and data produced in other dynamic environments such as power supply network traffic stock exchange telecommunication data flow Web click streams weather or environment monitoring The answer to this question may not be so easy since as everyone knows it takes great efforts and substantial storage space to compute and maintain static data cubes. A dynamic stream cube may demand an even greater computing power and storage space. How can we have sufficient resources to compute and store a dynamic stream cube In this chapter we examine this issue and propose an interesting architecture called stream cube for on-line analytical processing of voluminous infinite and dynamic stream data with the following design .