Name
Abstract | ||
|---|---|---|
When cloud services become popular, how to consume a cloud service efficiently by an enterprise application, as the client of the cloud service either on a device or on the application tier of the enterprise software stack, is an important issue. Focusing on the consumption of the real-time events service, in this work we extend the Data Access Object (DAO) pattern of enterprise applications for on-demand access and analysis of real-time events. We introduce the notion of Operational Event Pipe for caching the most recent events delivered by an event service, and the on-demand data analysis pattern based on this notion. We implemented the operational event pipe as a special kind of continuous query referred to as Event Pipe Query (EPQ). An EPQ is a long-standing SQL query with User Defined Functions (UDFs) that provides a pipe for the stream data to be buffered and to flow continuously in the boundary of a sliding window; when not requested, the EPQ just maintains and updates the buffer but returns noting, once requested, it returns the query processing results on the selected part of the sliding window buffer, under the request-and-rewind mechanism. Integrating event buffering and analysis in a single continuous query leverages the SQL expressive power and the query engine's data processing capability, and reduces the data movement overhead. By extending the PostgreSQL query engine, we implement this operation pattern as the Continuous Data Access Object (CDAO) - an extension to the J2EE DAO. While DAO provides static data access interfaces, CDAO adds dynamic event processing interfaces with one or more EPQs. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25106-1_8 | OTM Conferences (2) |
Keywords | Field | DocType |
data movement overhead,query processing result,continuous query,continuous access,operational event pipe,long-standing sql query,single continuous query,query engine,cloud service,postgresql query engine,enterprise application,event service,event pipe query | SQL,Data warehouse,Data access object,Sliding window protocol,Enterprise software,Computer science,Complex event processing,User-defined function,Database,Cloud computing | Conference |
Volume | ISSN | Citations |
7045 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiming Chen | 1 | 2010 | 233.16 |
| Meichun Hsu | 2 | 3437 | 778.34 |