Efficient reverse k-nearest neighbor estimation

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. In addition, many applications require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate a fast answer with high recall. In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.     

Extinction and retraining of simultaneous and successive flavor conditioning

In three experiments, rats received pairings of flavor conditioned stimuli with polycose unconditioned stimuli, in either a simultaneous or a sequential relation. Both temporal relations produced excellent conditioned increases in consumption of the flavors. Separate presentation of the flavors resulted in extinction in both cases. However, restoring the pairing of the flavor with polycose resulted in reconditioning only with the sequential, not with the simultaneous, relation.     

Price Indices for Artists – A Proposal

This paper proposes a price index for artists adjusted for the quality of the paintings, based on the ratio between the average market price and the average pre-sale estimate for paintings. We apply this methodology to a group of selected artists and schools presented in auctions worldwide in the period 1990–2001. A comparison with quality unadjusted and hedonic indices is also conducted.JEL Classification: C10, Z11     

ONIX: Is there a return on investment for all publishers?

Summary What will be the future of ONIX? Some argue that wider industry acceptance will lead to increased efficiencies in the transfer of book data, which will ultimately benefit book sales. ONIX still has to overcome many challenges: its implementation cost is high, whole-salers and aggregators are still using different spreadsheets and information sources. Furthermore, MS Excel as an alternative solution is widely available and accepted by the industry. Small publishers need more education and a straightforward demonstration of ONIX’s return on investment. An outreaching of the helping hand of professional organizations is strongly suggested to foster the adoption process among small players. It is the role of the AAP and BISG to get their memberships to think about what is good for all members as a whole throughout the industry. As an organization BISG can recommend, educate, and promote the standard. It cannot, however, bring pressure or demand organizations use it. Publishers, retailers, and wholesalers make those decisions for themselves and quite apparently the majority do not yet see the rewards. Market players need to determine the cost versus benefit and as long as distributors and retailers agree on receiving other formats, ONIX will never take a strong hold in the marketplace. After all, publishers should compete against each other with good books and not with multiple computer codes.