We have developed a mechanism that supports
trading database tuples in a multiagent system. The mechanism enables
negotiation and evaluation of database-based information goods. Our research
focuses on two aspects of the mechanism. The first aspect is performing
automatic schema-matching between the buyer’s and seller’s databases. The
second aspect is dynamic pricing of information goods.
We propose methods for
schema-matching and different policies for dynamic pricing of information
have developed a test-bed that simulates a multi-agent system where each agent
uses the offered mechanism and have evaluated the system performance when
sellers use different pricing policies in two market environments, namely
competitive and non-competitive. The investigated pricing policies include two
novel pricing policies that implement negotiation and price discrimination
across consumers and compared them to two policies known in the art, which
implement dynamic posted pricing. We offer a schema-matching methodology in
which a given set of heuristics is partitioned based on their complexity. Less
complex heuristics are utilized in generating a top-K set of possible mappings.
These mappings are analyzed for identifying possible points of failure and
verified using the more complex heuristics. The complex heuristics uses
statistical analysis of the buyer’s and seller’s databases to refine the
generated mapping. The
evaluation concentrates on two main issues. The first is evaluation of the
offered methodology for schema-matching and the second is evaluating the
different pricing policies.
We have empirically demonstrated the superiority of the
offered pricing policies in maximizing sellers' gains. We have additionally
identified equilibria profiles of these policies. Evaluating the schema
matching methodology, our experiments show a significant increase in the precision
of mappings at a relatively low computational cost.