Extreme Streaming: Business Optimization Driving Algorithmic Challenges
Summary: Extreme streaming for business optimization drives algorithmic challenges in ultra-high-volume warehouses. Ingestion at high rates with concurrent updates; outlines high-level mechanisms fusing streaming analytics, data mining, and voice/text data for churn insights. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 662 | A Framework for Clustering Evolving Data Streams | 2003 | VLDB | 0.00018475968 |
| 13,462 | The Value of Social Media Data in Enterprise Applications | 2012 | SIGMOD | - |
| 13,483 | Theory of Data Stream Computing: Where to Go | 2011 | PODS | - |
| 1,222 | Querying and Mining Data Streams: You Only Get One Look | 2002 | SIGMOD | 0.00013213129 |
| 6,721 | Beyond Analytics: The Evolution of Stream Processing Systems | 2020 | SIGMOD | 4.9492015e-05 |
| 6,918 | Aggregate Profile Clustering for Telco Analytics | 2013 | VLDB | 4.8925595e-05 |
| 8,813 | Real Time Analytics: Algorithms and Systems | 2015 | VLDB | 4.4438508e-05 |
| 4,618 | Approximate Frequency Counts over Data Streams | 2012 | VLDB | 6.0446717e-05 |
| 13,613 | Trends in High Performance Analytics | 2006 | SIGMOD | - |
| 9,140 | Extreme Data Mining | 2008 | SIGMOD | 4.3858893e-05 |