Introduction: Many advanced, data-intensive applications, such as scientific research and engineering design, need to store, access, and analyze complex but relatively structured data objects. These objects cannot be represented as simple and uniformly structured records (i.e., tuples) in data relations. Such application requirements have motivated the design and development of object-relational and object-oriented database systems. Both kinds of systems deal with the efficient storage and access of vast amounts of disk-based complex structured data objects.
These systems organize a large set of complex data objects into classes, which are in turn organized into class/subclass hierarchies. Each object in a class is associated with (1) an object-identifier, (2) a set of attributes that may contain sophisticated data structures, set- or list-valued data, class composition hierarchies, multimedia data, and (3) a set of methods that specify the computational routines or rules associated with the object class. There has been extensive research in the field of database systems on how to efficiently index, store, access, and manipulate complex objects in object-relational and object-oriented database systems. Technologies handling these issues are discussed in many books on database systems, especially on object-oriented and object-relational database systems.
One step beyond the storage and access of massive-scaled, complex object data is the systematic analysis and mining of such data. This includes two major tasks: (1) construct multidimensional data warehouses for complex object data and perform online analytical processing (OLAP) in such data warehouses, and (2) develop effective and scalable methods for mining knowledge from object databases and/or data warehouses. The second task is largely covered by the mining of specific kinds of data (such as spatial, temporal, sequence, graph- or tree-structured, text, and multimedia data), since these data from the major new kinds of complex data objects. As in Chapters 8 and 9, in this chapter we continue to study methods for mining complex data. Thus, our focus in this section will be mainly on how to construct object data warehouses and perform OLAP analysis on data warehouses for such data.
A major limitation of many commercial data warehouse and OLAP tools for multidimensional database analysis is their restriction on the allowable data types for dimensions and measures. Most data cube implementations confine dimensions to nonnumeric data, and measures to simple, aggregated values. To introduce data mining and multidimensional data analysis for complex objects, this section examines how to perform generalization on complex structured objects and construct object cubes for OLAP and mining in object databases.
To facilitate generalization and induction in object-relational and object-oriented databases, it is important to study how each component of such databases can be generalized, and how the generalized data can be used for multidimensional data analysis and data mining.
