Information retrieval (IR) is a field that has been developing in parallel with database systems for many years. Unlike the field of database systems, which has targeted query and transaction processing of structured data, information retrieval is concerned with the organization and retrieval of data from multiple text-based documents.Since information retrieval and database systems each handle different kinds of data, some database system problems are usually not present in information retrieval systems, such as concurrency control, recovery, transaction management, and update. There are some common information retrieval problems that are usually not encountered in traditional database systems, such as unstructured ... Read More

There are several challenging issues regarding the construction and utilization of spatial data warehouses. The first challenge is the unification of spatial information from heterogeneous sources and systems. Spatial data are usually stored in different industry firms and government agencies using various data formats.Data formats are not only structure-specific (e.g., raster- vs. vector-based spatial data, object-oriented vs. relational models, different spatial storage and indexing structures), but also vendor-specific (e.g., ESRI, MapInfo, Intergraph). There has been huge work on the unification and exchange of heterogeneous spatial data, which has paved the way for spatial data integration and spatial data warehouse construction.The ... Read More

A spatial database saves a huge amount of space-related data, including maps, preprocessed remote sensing or medical imaging records, and VLSI chip design data. Spatial databases have several features that distinguish them from relational databases. They carry topological and/or distance information, usually organized by sophisticated, multidimensional spatial indexing structures that are accessed by spatial data access methods and often require spatial reasoning, geometric computation, and spatial knowledge representation techniques.Spatial data mining refers to the extraction of knowledge, spatial relationships, or other interesting patterns not explicitly stored in spatial databases. Such mining demands the unification of data mining with spatial database ... Read More

An object identifier can be generalized as follows. First, the object identifier is generalized to the identifier of the lowest subclass to which the object belongs. The identifier of this subclass can then, in turn, be generalized to a higher level class/subclass identifier by climbing up the class/subclass hierarchy. Similarly, a class or a subclass can be generalized to its corresponding superclass (es) by climbing up its associated class/subclass hierarchy.Because object-oriented databases are organized into class/subclass hierarchies, some attributes or methods of an object class are not explicitly specified in the class but are inherited from higher-level classes of the ... Read More

Multi-relational clustering is the process of partitioning data objects into a set of clusters based on their similarity, utilizing information in multiple relations. In this section, it can introduce CrossClus (Cross-relational Clustering with user guidance), an algorithm for multi-relational clustering that explores how to utilize user guidance in clustering and tuple ID propagation to avoid physical joins.There is one major challenge in multi-relational clustering is that there are too many attributes in different relations, and usually, only a small portion of them are relevant to a specific clustering task.Consider the computer science department database. It can order to cluster students, ... Read More

Multi-relational data mining (MRDM) methods search for designs that contain several tables (relations) from a relational database. Each table or relation represents an entity or a relationship, described by a set of attributes. Links between relations show the relationship between them.There is one method to apply traditional data mining methods (which assume that the data reside in a single table) is propositionalization, which converts multiple relational data into a single flat data relation, using joins and aggregations.This can lead to the generation of a huge, undesirable “universal relation” (involving all of the attributes). Furthermore, it can result in the loss ... Read More

There are several challenges of link mining which are as follows −Logical versus statistical dependencies − Two types of dependencies reside in the graph link structures (representing the logical relationship between objects) and probabilistic dependencies (representing statistical relationships, such as the correlation between attributes of objects where, generally, such objects are logically related).The coherent handling of these dependencies is also a challenge for multi-relational data mining, where the data to be mined exist in multiple tables. It should search over the several possible logical relationships between objects, furthermore the standard search over probabilistic dependencies among attributes. This takes a huge ... Read More

There are several tasks of link mining which are as follows −Link-based object classification − In traditional classification approaches, objects are classified depending on the attributes that define them. Link-based classification predicts the category of an object depends not only on its attributes, but also on its links, and the attributes of linked objects.Web page classification is a well-identified instance of link-based classification. It predicts the classification of a web page based on word appearance (words that appear on the page) and anchor text (the hyperlink words, that is, the words it can click on when it can click on ... Read More

A social network is a heterogeneous and multi-relational information set described by a graph. The graph is generally very large, with nodes corresponding to objects and edges corresponding to connections describing relationships or connections between objects. Both nodes and connections have attributes. Objects can have class labels. Links can be one-directional and are not needed to be binary.A social network is a heterogeneous and multi-relational information set described by a graph. The graph is generally very large, with nodes corresponding to objects and edges corresponding to connections describing relationships or connections between objects. Both nodes and connections have attributes. Objects ... Read More

The discovery of frequent substructures usually consists of two steps. In the first step, it can make frequent substructure candidates. The frequency of every candidate is tested in the second step. Most studies on frequent substructure discovery focus on the optimization of the first step because the second step involves a subgraph isomorphism test whose computational complexity is excessively high (i.e., NP-complete).There are various methods for frequent substructure mining which are as follows −Apriori-based Approach − Apriori-based frequent substructure mining algorithms send the same features with Apriori-based frequent itemset mining algorithms. The search for frequent graphs begins with graphs of ... Read More