Abstract | ||
---|---|---|
Users are increasingly engaging in buying and selling data over the web. Facilitated by the proliferation of online marketplaces that bring such users together, data brokers need to serve requests where they provide results for user queries over the underlying datasets, and price them fairly according to the information disclosed by the query. In this work, we present a novel pricing system, called QIRANA, that performs query-based data pricing for a large class of SQL queries (including aggregation) in real time. QIRANA provides prices with formal guarantees: for example, it avoids prices that create arbitrage opportunities. Our framework also allows flexible pricing, by allowing the data seller to choose from a variety of pricing functions, as well as specify relation and attribute-level parameters that control the price of queries and assign different value to different portions of the data. We test QIRANA on a variety of real-world datasets and query workloads, and we show that it can efficiently compute the prices for queries over large-scale data. |
Year | DOI | Venue |
---|---|---|
2017 | 10.1145/3035918.3064017 | SIGMOD Conference |
Keywords | Field | DocType |
Data Pricing,Arbitrage,Query Determinacy | SQL,Data mining,Computer science,Web query classification,Arbitrage,Database,Scalability | Conference |
Citations | PageRank | References |
6 | 0.42 | 13 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Shaleen Deep | 1 | 18 | 5.32 |
Paraschos Koutris | 2 | 347 | 26.63 |