Name
Abstract | ||
|---|---|---|
Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous Analytics technology is seamlessly integrated into a full-function database system, creating a powerful and flexible system that can run SQL over tables, streams, and combinations of the two. A continuous analytics system can run many orders of magnitude more efficiently than traditional store-first-query-later technologies. In this paper, we describe the Continuous Analytics approach and outline some of the key technical arguments behind it. |
Year | Venue | Keywords |
|---|---|---|
2009 | CIDR | low latency,database system,data analysis,data warehousing,network effect |
Field | DocType | Citations |
Data warehouse,SQL,Data mining,Database model,Computer science,View,Network effect,Query by Example,Latency (engineering),Analytics,Database | Conference | 44 |
PageRank | References | Authors |
2.45 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael J. Franklin | 1 | 17423 | 1681.10 |
| Sailesh Krishnamurthy | 2 | 1331 | 101.15 |
| Neil Conway | 3 | 458 | 21.46 |
| Alan Li | 4 | 84 | 4.60 |
| a russakovsky | 5 | 44 | 2.45 |
| Neil Thombre | 6 | 84 | 4.26 |
| e hillsdale blvd | 7 | 44 | 2.45 |