Name
Abstract | ||
|---|---|---|
Data stream processing systems have become ubiquitous in academic [1, 2, 5, 6] and commercial [11] sectors, with application areas that include financial services, network traffic analysis, battlefield monitoring and traffic control [3]. The append-only model of streams implies that input data is immutable and therefore always correct. But in practice, streaming data sources often contend with noise (e.g., embedded sensors) or data entry errors (e.g., financial data feeds) resulting in erroneous inputs and therefore, erroneous query results. Many data stream sources (e.g., commercial ticker feeds) issue "revision tuples" (revisions) that amend previously issued tuples (e.g. erroneous share prices). Ideally, any stream processing engine should process revision inputs by generating revision outputs that correct previous query results. We know of no stream processing system that presently has this capability. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICDE.2006.130 | ICDE |
Keywords | Field | DocType |
stream processing system,input data,data stream source,financial data,erroneous input,stream processing engine,erroneous query result,data entry error,high-level design,data stream processing system,data source,revision processing,process design,engines,financial services,data models,stream processing,writing | Data mining,Data modeling,Traffic analysis,Data stream mining,High-level design,Tuple,Data stream,Computer science,Process design,Stream processing,Database | Conference |
ISBN | Citations | PageRank |
0-7695-2570-9 | 29 | 1.18 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esther Ryvkina | 1 | 900 | 46.17 |
| Anurag S. Maskey | 2 | 215 | 15.53 |
| Mitch Cherniack | 3 | 4128 | 293.66 |
| Stanley B. Zdonik | 4 | 9186 | 1660.15 |