MicroStrategy Announces DSS Server 3.0; Three-Tier Architecture Results in Exceptional Performance and Scalability for DSS Applications

Business Wire, August 8, 1995

VIENNA, Va.--(BUSINESS WIRE)--Aug. 8, 1995--MicroStrategy, a leading provider of data warehouse and decision support solutions, today announced the release of DSS Server 3.0, a powerful On-Line Analytical Processing (OLAP) server that provides a middle tier between the data warehouse and the end user, and provides a three-tier model for decision support.

The three-tier architecture enabled by DSS Server provides multiple benefits to organizations implementing decision support applications including leveraging existing investments in relational database technology, freeing-up client machines to allow them to process other tasks while analyses are being executed, speeding up development time, and greatly reducing the application maintenance required. DSS Server's three-tier architecture leverages the knowledge and insights of key users by sharing Intelligent Agents over the network. The architecture benefits IT departments by allowing the tracking and maintaining of a repository of usage measurements that enable system architects to understand usage profiles and to proactively manage the data warehouse.

"DSS Server provides organizations with DSS applications which perform extremely well and are scalable to meet the DSS and data warehousing needs of companies not only today, but as they change and grow over time," said Barry Lovalvo, DSS Server product manager. "Functionality such as asynchronous query processing, scheduled agents, rule-based query governing, and usage tracking enable the deployment of DSS applications to hundreds of users throughout the the organization."

"DSS Server enables users to run in a three-tier client/server environment, which is quickly becoming a requirement for high-end decision support, where dozens or hundreds of users are accessing large databases consisting of tens or hundreds of gigabytes of data," said Wayne Eckerson, editor-in-chief of OPEN INFORMATION SYSTEMS at the Patricia Seybold Group. "MicroStrategy has a strong story to tell: it performs complex calculations and multidimensional analyses against relational databases rather than specialized OLAP data stores. It has a powerful technology that is becoming widely deployed."

DSS Server contains an expert SQL Query Engine which dynamically generates RDBMS-specific, performance-optimized SQL queries from end-user information requests. The SQL Query Engine translates DSS Agent reports, selection criteria, and multidimensional reporting preferences into multi-pass SQL queries which run against the data warehouse. Metadata and an expert rule base are used to create the SQL, eliminating the need for either the end user or the application developer to define complex SQL queries.

DSS Server also includes a high-performance OLAP Engine which supports advanced OLAP metrics directly from the data warehouse. With DSS Server's OLAP Engine, users can retrieve derived metrics like contribution analysis, penetration and market share, or advanced comparison metrics like period-to-period (this year vs. last year) and comparable stores. The OLAP Engine also cross-tabulates the resultant data set so that users can view the data multidimensionally.

DSS Server Features:

Rule-Based Query Governor

DSS Server's rule-based query governor opens the data warehouse to thousands of corporate users without impairing application performance. The query governor prioritizes each information request going into the warehouse and sets thresholds that determine when, or if, a job will run. DSS Server's flow control mechanism enables organizations to ensure good response time by managing the number of threads into the warehouse. In addition, by allowing query aborts and query limits, the rule-based governor ensures that the data warehouse is not overloaded.

Background Processing

DSS Server raises overall organizational productivity by processing queries asynchronously. Analyses are processed in the background, freeing users to perform other tasks on their PCs. Users now have more time to analyze and act on the information retrieved from their queries.

Agent Scheduling

With DSS Server, users can create agents that run complex queries in the background, at scheduled times, on a recurring basis, or even after specific events. Scheduled agents automate repetitive tasks for the end user and smooth the load on the data warehouse. In addition to scheduling agents, users can also create alerts that automate the scanning of exception conditions within the data.

Distributed Relational Data Marts

DSS Server greatly reduces analysis execution times by enabling the creation of distributed relational data marts, or specific data subsets of the data warehouse. Agents can be instructed to create the data marts on the data warehouse server, the OLAP server, or any other server. By scheduling an Agent to temporarily store information results in distributed data marts, organizations can reduce network bottlenecks, substitute lower-cost batch cycles for interactive cycles, use lower-cost hardware for processing interactive information requests, and support remote sites.