Data aggregation server for managing a multi-dimensional database and database management system having data aggregation server integrated therein

Abstract

Improved method of and apparatus for aggregating data elements in multidimensional databases (MDDB). In one aspect of the present invention, the apparatus is realized in the form of a high-performance stand-alone (i.e. external) aggregation server which can be plugged-into conventional OLAP systems to achieve significant improments in system performance. In accordance with the principles of the present invention, the stand-alone aggregation server contains a scalable MDDB and a high-performance aggregation engine that are integrated into the modular architecture of the aggregation server. The stand-alone aggregation server of the present invention can uniformly distribute data elements among a plurality of processors, for balanced loading and processing, and therefore is highly scalable. The stand-alone aggregation server of the present invention can be used to realize (i) an improved MDDB for supporting on-line analytical processing (OLAP) operations, (ii) an improved Internet URL Directory for supporting on-line information searching operations by Web-enabled client machines, as well as (iii) diverse types of MDDB-based systems for supporting real-time control of processes in response to complex states of information reflected in the MDDB. In another aspect of the present invention, the apparatus is integrated within a database management system (DBMS). The improved DBMS can be used to realize achieving a significant increase in system performance (e.g. deceased access/search time), user flexibility and ease of use. The improved DBMS system of the present invention can be used to realize an improved Data Warehouse for supporting on-line analytical processing (OLAP) operations or to realize an improved informational database system, operational database system, or the like.

Description

RELATED CASES[0001] This is a Continuation-in-part of: copending U.S. application Ser. No. 09 / 514,611 entitled "Stand-Alone Cartridge-Style Data Aggregation Server And Method of And System For Managing Multi-Dimensional Databases using the Same", filed Feb. 28, 2000, and U.S. application Ser. No. 09 / 634,748 entitled "Relational Database Management System Having Integrated Non-Relational Multi-Dimensional Data Store of Aggregated Data Elements" filed Aug. 9, 2000; each said Application being commonly owned by HyperRoll, Limited, and incorporated herein by reference in its entirety.[0002] 1. Field of Invention[0003] The present invention relates to a method of and system for aggregating data elements in a multi-dimensional database (MDDB) supported upon a computing platform and also to provide an improved method of and system for managing data elements within a MDDB during on-line analytical processing (OLAP) operations and as an integral part of a database management system.[0004] 2....

AI Technical Summary

Benefits of technology

0108] Another object of the present invention is to provide a generic plug-in cartridge-type data aggregation component, suitable for all MOLAP systems of different vendors, dramatically reducing their aggregation burdens.

0109] Another object of the present invention is to provide a novel high performance cartridge-type data aggregration server which, having standardized interfaces, can be plugged-into the OLAP system of virtually any user or vendor.

0110] Another object of the present invention is to provide a novel "cartridge-style" (standalone) scalable data aggregation engine which has the capacity to convert long batch-type data aggregations into interactive sessions.

0111] In another aspect, it is an object of the present invention to provide an improved method of and system f

Problems solved by technology

The volume of information that is available to corporations is rapidly increasing and frequently overwhelming.

Building a Data Warehouse has its own special challenges (e.g. using common data model, common business dictionary, etc.) and is a complex endeavor.

However, just having a Data Warehouse does not provide organizations with the often-heralded business benefits of data warehousing.

In all the prior art OLAP servers, the process of storing, indexing and handling MDDB utilize complex data structures to largely improve the retrieval speed, as part of the querying process, at the cost of slowing down the storing and aggregation.

The query-bounded structure, that must support fast retrieval of queries in a restricting environment of high sparcity and multi-hierarchies, is not the optimal one for fast aggregation.

However, requirements (2) and (3) fundamentally limit MOLAP's capability, because to be effective and to meet end-user requirements, MOLAP databases need a high degree of aggregation.

By contrast, the ROLAP system architecture allows the construction of systems requiring a low degree of aggregation, but such systems are significantly slower than systems based on MOLAP system architecure principles.

The resulting long aggregation times of ROLAP systems impose severe limitations on its volumes and dimensional capabilities.

However, prior art MOLAP systems have limited capabilities to dynamically create data aggregations or to calculate business metrics that have not been precalculated and stored in the MDDB.

However, the ROLAP architecture, despite its high volume and dimensionality superiority, suffers from several significant drawbacks as compared to MOLAP:

Full aggregation of large data volumes are very time consuming, otherwise, partial aggregation severely degrades the query response.

SQL is less capable of the sophisticated analytical functionality necessary for OLAP

ROLAP provides limited application functionality

Building a Data Warehouse has its own special challenges (e.g. using common data model, common business dictionary, etc.) and is a complex endeavor.

However, just having a Data Warehouse does not provide organizations with the often-heralded business benefits of data warehousing.

However, the querying component of RDBMS technology suffers from performance and optimization problems stemming from the very nature of the relational data model.

For large multidimensional databases, a naive implementation of these operations involves computational intensive table scans that leads to unacceptable query response times.

For large multi-dimensional databases, a naive implementation of these operations involves computational intensive table scans that typically leads to unacceptable query response times. Moreover, since the fact tables are pre-summarized and aggregated along business dimensions, these tables tend to be very large.

The first performance issue arises from computationally intensive table scans that are performed by a naive implementation of data joining.

However, these indexing schemes suffer from various performance issues as follows:

Since the tables in the star schema design typically contain the entire hierarchy of attributes (e.g. in a PERIOD dimension, this hierarchy could be day>week>month>quarter>year), a multipart key of day, week, month, quarter, year has to be created; thus, multiple meta-data definitions are required (one of each k

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