Name
Abstract | ||
|---|---|---|
Data mashups provide end-users with an opportunity to create situational applications which aggregate and manipulate data from multiple diverse data sources. A challenging problem is once the data sources are updated and propagate bottom-up to the top level, how to ensure the freshness of mashups. In this paper, an approach is proposed to generate a data mashup scheme and its corresponding synchronous policy guaranteeing the optimal data freshness quality. The paper firstly applies the heuristic transformation rules to select some optimal mashup schemes, and then selects an equivalence mashup by solving the 0-1 integer programming problem. Lastly the paper applies a heuristic algorithm on the mashup scheme to get the operation nodes needed to be materialized and then the synchronous policy. This paper also reports a number of experiments studying the benefits and costs of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-49178-3_33 | ADVANCES IN SERVICES COMPUTING |
Keywords | Field | DocType |
Data services,Mashups,Data freshness,Quality-aware mashups | Mashup,Heuristic,Heuristic (computer science),Computer science,Equivalence (measure theory),Integer programming,Data as a service,Distributed computing | Conference |
Volume | ISSN | Citations |
10065 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guiling Wang | 1 | 832 | 52.06 |
| Shuo Zhang | 2 | 0 | 0.34 |