Abstract: Techniques are disclosed for managing a large set of data items, such as electronic data interchange (EDI) transactions or e-mail messages. A data item in a data set is received. The data item is exchanged between a first entity and a second entity. A relationship measure is determined between the first entity and the second entity relative to hierarchical information between the first entity and the second entity. An importance measure of the data item is determined based on the relationship measure. The importance measure is assigned to the data item. Further, the data items may be aggregated using pattern matching techniques relative to the importance factor, the aggregation of which may be used for intelligent archiving or purging decisions.

Abstract: Techniques are disclosed for managing a large set of data items, such as electronic data interchange (EDI) transactions or e-mail messages. A data item in a data set is received. The data item is exchanged between a first entity and a second entity. A relationship measure is determined between the first entity and the second entity relative to hierarchical information between the first entity and the second entity. An importance measure of the data item is determined based on the relationship measure. The importance measure is assigned to the data item. Further, the data items may be aggregated using pattern matching techniques relative to the importance factor, the aggregation of which may be used for intelligent archiving or purging decisions.