Electronic markets have early emerged as an important topic inside e-commerce research. An e-market is a digital ecosystem intended to provide their users with online services that will facilitate information exchange and transactions. This work presents a characterization and analysis of fixed-price online negotiations. Using actual data from a Brazilian marketplace, we analyze selling practices, considering seller profiles and selling strategies. There are important factors that can be considered when analyzing selling practices, such as the seller's reputation and experience, offer's price, duration, among others. We evaluate which factors impact on the success of selling practices in e-markets, which can be used to support seller's decision and recommend selling practices. Moreover, we investigate some important hypotheses about selling practices in online marketplaces, which allow us to state interesting conclusions, such as: a seller profile can achieve success or not in a trade, depending on the adopted strategy; the offer's price and how it is being advertised are two important success factors.

E-commerce is growing at an exponential rate. In the last decade, there has been an explosion of online commercial activity enabled by World Wide Web (WWW). These days, many consumers are less attracted to online auctions, preferring to buy merchandise quickly using fixed-price negotiations. Sales at Amazon.com, the leader in online sales of fixed-price goods, rose 37% in the first quarter of 2008. At eBay, where auctions make up 58% of the site's sales, revenue rose 14%. In Brazil, probably by cultural influence, online auctions are not been popular. This work presents a characterization and analysis of fixed-price online negotiations. Using actual data from a Brazilian marketplace, we analyze seller practices, considering seller profiles and strategies. We show that different sellers adopt strategies according to their interests, abilities and experience. Moreover, we confirm that choosing a selling strategy is not simple, since it is important to consider the seller's characteristics to evaluate the applicability of a strategy. The work also provides a comparative analysis of some selling practices in Brazil with popular worldwide marketplaces.

Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (RDBMSs) normally support. This approach combines the advantage of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures or indexing techniques to handle top-k queries separately. However, existing methods following this approach fall short of adequately modeling the problem environment and providing consistent results. In this article, the authors propose a cost-based range estimation model for the query-mapping approach. They provide a methodology for trading-off relevant query execution cost components and mapping a top-k query into a cost-optimal range query for efficient execution. Their experiments on real world and synthetic data sets show that the proposed strategy not only avoids the need to calibrate workloads on specific database contents, but also performs at least as well as prior methods.

Online auction services have reached great popularity and revenue over the last years. A key component for this success is the seller. Few studies proposed analyzing how the seller and the auction configuration affect the negotiation results. In this work we propose a methodology to characterize online auctions by the seller's perspective. This methodology is based on: (1) recognizing the characteristics of the variables related to the auction results and (2) capturing the correlation among these variables to identify seller profiles and selling strategies. We applied our methodology to a real case study, using an eBay dataset, to validate two hypotheses about sellers and their practices. These results are useful to understand the complex mechanisms that guide ending prices, success (or failure), and the attraction of bids in online auctions, which can support decision strategies for buyers and sellers.

We develop a real-time estimation approach to predict bidders' maximum willingness to pay in a multiunit ascending uniform-price and discriminatory-price (Yankee) online auction. Our two-stage approach begins with a bidder classification step, which is followed by an analytical prediction model. The classification model identifies bidders as either adopting a myopic best-response (MBR) bidding strategy or a non-MBR strategy. We then use a generalized bid-inversion function to estimate the willingness to pay for MBR bidders. We empirically validate our two-stage approach using data from two popular online auction sites. Our joint classification-and-prediction approach outperforms two other naïve prediction strategies that draw random valuations between a bidder's current bid and the known market upper bound. Our prediction results indicate that, on average, our estimates are within 2% of bidders' revealed willingness to pay for Yankee and uniform-price multiunit auctions. We discuss how our results can facilitate mechanism-design changes such as dynamic-bid increments and dynamic buy-it-now prices.

We deal with the question of how to maintain security of confidential information in a database while answering as many queries as possible. The database is assumed to operate in a query restriction (as opposed to perturbation) mode in which exact answers are given to those queries which, together with those already answered, will not compromise any confidential datum. Those which fail this criterion are not answered. We introduce the concept of interval disclosure where a datum is compromised if the answered queries provide enough information to establish that it is contained in a given interval even if the datum cannot be determined exactly. Models are presented for the problem of deciding whether to answer a query and three techniques, one based on linear programming, are developed and tested.

With the rapid evolution in the computer industry where memory chips of increasing capacity are introduced every year at lower costs coupled with the increasing demand for massive on-line databases, a great deal of research has been done in the area of main memory resident databases. The use of these systems can achieve significant performance improvements in environments with very high volume of transactions by eliminating the need for most I/O functions while performing basic database operations. A primary concern in such systems is how to develop efficient recovery mechanisms to cope with system failures that can erase the contents of the main memory. This paper presents a stochastic model to evaluate the performance of the database system in terms of normal operation, checkpointing and recovery from system failures. Through a multi-variate model important transform results of effective residence times in each of the database states are derived, which allows the computation of first moments of interest, ergodic state probabilities, including the system availability, and reward measures. The model developed in the paper is significantly more general than previous stochastic models for traditional databases thus capturing the special features of recovery of main memory systems. The applicability of the model is supported and verified through numerical experiments. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

In this paper we develop three analytical models for a comprehensive analysis of database recovery. These models, based on semi-Markov stochastic analysis and queueing networks, not only capture the details of modern recovery mechanisms, but take the complex stochastic behavior of the system into account. Furthermore, we use multiple performance measures to analyze different recovery mechanisms, the impact of environment characteristics and the effect of tunable system parameters, thus offering database designers and administrators a better understanding of the recovery system to be designed or managed. A special case of database recovery that has been studied by previous researchers is analyzed in detail; numerical experiments offer evidence of the effectiveness of our approach. The models developed in this paper, however, are applicable to much more general systems and environments.>

A mobile communication system for high-speed thruways is currently under consideration in Japan. The system consists of fixed stations installed at regular intervals (2-5 km) along the thruway which are connected to a host computer. Subscribing customers can communicate with the system as they drive through each station's communication zone of minimal type (40-100 m). The authors develop and analyze a stochastic model for this new communication system. The model closely follows the sequence of operations which actually take place according to a communication protocol for handling collisions, and requires no exponential assumptions for the underlying service times. The tradeoff between the total number of stations installed and the probability of successful completion of a task within z km after the initial submission is investigated. Some numerical results are also given for illustrating the tradeoff, with validation via SIMAN simulation.>