We present three distributed algorithms to build global inverted files for very large text collections. The distributed environment we use is a high bandwidth network of workstations with a shared-nothing memory organization.The text collection is assumed to be evenly distributed among the disks of the various workstations.Our algorithms consider that the total distributed main memory is considerably smaller than the inverted file to be generated. The inverted file is compressed to save memory and disk space and to save time for moving data in/out disk and across the network. We analyze our algorithms and discuss the tradeoffs among them. We show that, with 8 processors and 16 megabytes of RAM available in each processor, the advanced variants of our algorithms are able to invert a 100 gigabytes collection (the size of the very large TREC-7 collection) in roughly 8 hours. Using 16 processors this time drops to roughly 4 hours.
Reputation is the opinion of the public toward a person, a group of people, or an organization. Reputation systems are particularly important in e-markets, where they help buyers to decide whether to purchase a product or not. Since a higher reputation means more profit, some users try to deceive such systems to increase their reputation. E-markets should protect their reputation systems from attacks in order to maintain a sound environment. This work addresses the task of finding attempts to deceive reputation systems in e-markets. Our goal is to generate a list of users (sellers) ranked by the probability of fraud. Firstly we describe characteristics related to transactions that may indicate frauds evidence and they are expanded to the sellers. We describe results of a simple approach that ranks sellers by counting characteristics of fraud. Then we incorporate characteristics that cannot be used by the counting approach, and we apply logistic regression to both, improved and not improved. We use real data from a large Brazilian e-market to train and evaluate our methods and the improved set with logistic regression performs better, specially when we apply stepwise optimization. We validate our results with specialists of fraud detection in this market place. In the end, we increase by 112% the number of identified fraudsters against the reputation system. In terms of ranking, we reach 93% of average precision after specialists' review in the list that uses Logistic Regression and Stepwise optimization. We also detect 55% of fraudsters with a precision of 100%.
We report on the experience of re-introducing Scrum in a project team that had previously failed to adopt that agile method. We explore the reasons we believe that caused the failure and explain how we approached the team to uncover them. Then, we describe our strategy to avoid incurring in those problems again and to take the team to a higher level of productivity, quality and personal satisfaction. We also present the motivation and the actions taken to go further and scale this scenario to multiple feature-oriented teams using Scrum of Scrums. All these changes occurred in less than one year.
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