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DNA databases have significant commercial value. Direct-to-consumer genetic testing companies have built databanks using samples and information voluntarily provided by customers. As the price of genetic analysis falls, there is growing interest in building such databases by paying individuals for their DNA and personal data. This paper maps the ethical issues associated with private companies paying for DNA. We outline the benefits of building better genomic databases and describe possible concerns about crowding out, undue inducement, exploitation, and commodification. While certain objections deserve more empirical and philosophical investigation, we argue that none currently provide decisive reasons against using financial incentives to secure DNA samples. [ABSTRACT FROM AUTHOR]

In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic has attracted the attention of international research groups because a very promising vein of research has emerged: the application of some measures of data complexity in the pattern classification algorithms. This paper aims to analyze the response of the Customized Naïve Associative Classifier (CNAC) in data taken from the business area when some measures of data complexity are introduced. To perform this analysis, we used classification datasets from real-world related to business, 22 in total; then, we computed the value of nine measures of data complexity to compare the performance of the CNAC against other algorithms of the state of the art. A very important aspect of performing this task is the creation of an artificial dataset for meta-learning purposes, in which we considered the performance of CNAC, and then we trained a decision tree as meta learner. As shown, the CNAC classifier obtained the best results for 10 out of 22 datasets of the experimental study. [ABSTRACT FROM AUTHOR]

Data-intensive systems in e-governance collect and process data to ensure conformance to a set of business rules. Testers meticulously verify data in test databases, extracted from different steps of a live production stream , for correct application of business rules. We simplify the process by allowing testers to model a test domain on a relational database and automatically generate test cases representing data interactions satisfying combinatorial interaction coverage criteria. This paper also introduces test cases with self-referential interactions, which is a necessity in real-world databases. We verify these test cases using our human-in-the-loop tool, Depict. Depict, with expert assistance, generates complex SQL queries for test cases and produces a visual report of test case satisfaction. We apply the approach to two scenarios: 1) simplify and optimize a periodic archiving operation and 2) verify fault codes within the testing environment of the Custom directorate's TVINN system. [ABSTRACT FROM AUTHOR]

System-related engineering tasks are often conducted using process models. In this context, it is essential that these models do not contain structural or terminological inconsistencies. To this end, several automatic analysis techniques have been proposed to support quality assurance. While formal properties of control flow can be checked in an automated fashion, there is a lack of techniques addressing textual quality. More specifically, there is currently no technique available for handling the issue of lexical ambiguity caused by homonyms and synonyms. In this paper, we address this research gap and propose a technique that detects and resolves lexical ambiguities in process models. We evaluate the technique using three process model collections from practice varying in size, domain, and degree of standardization. The evaluation demonstrates that the technique significantly reduces the level of lexical ambiguity and that meaningful candidates are proposed for resolving ambiguity. [ABSTRACT FROM PUBLISHER]

Location-based applications utilize the positioning capabilities of a mobile device to determine the current location of a user, and customize query results to include neighboring points of interests. However, location knowledge is often perceived as personal information. One of the immediate issues hindering the wide acceptance of location-based applications is the lack of appropriate methodologies that offer fine grain privacy controls to a user without vastly affecting the usability of the service. While a number of privacy-preserving models and algorithms have taken shape in the past few years, there is an almost universal need to specify one's privacy requirement without understanding its implications on the service quality. In this paper, we propose a user-centric location-based service architecture where a user can observe the impact of location inaccuracy on the service accuracy before deciding the geo-coordinates to use in a query. We construct a local search application based on this architecture and demonstrate how meaningful information can be exchanged between the user and the service provider to allow the inference of contours depicting the change in query results across a geographic area. Results indicate the possibility of large default privacy regions (areas of no change in result set) in such applications. [ABSTRACT FROM AUTHOR]

Background: Validation studies of secondary datasets used to characterize neighborhood food businesses generally evaluate how accurately the database represents the true situation on the ground. Depending on the research objectives, the characterization of the business environment may tolerate some inaccuracies (e.g. minor imprecisions in location or errors in business names). Furthermore, if the number of false negatives (FNs) and false positives (FPs) is balanced within a given area, one could argue that the database still provides a "fair" representation of existing resources in this area. Yet, traditional validation measures do not relax matching criteria, and treat FNs and FPs independently. Through the field validation of food businesses found in a Canadian database, this paper proposes alternative criteria for validity. Methods: Field validation of the 2010 Enhanced Points of Interest (EPOI) database (DMTI Spatial®) was performed in 2011 in 12 census tracts (CTs) in Montreal, Canada. Some 410 food outlets were extracted from the database and 484 were observed in the field. First, traditional measures of sensitivity and positive predictive value (PPV) accounting for every single mismatch between the field and the database were computed. Second, relaxed measures of sensitivity and PPV that tolerate mismatches in business names or slight imprecisions in location were assessed. A novel measure of representativity that further allows for compensation between FNs and FPs within the same business category and area was proposed. Representativity was computed at CT level as ((TPs +|FPs-FNs|)/(TPs+FNs)), with TPs meaning true positives, and |FPs-FNs| being the absolute value of the difference between the number of FNs and the number of FPs within each outlet category. Results: The EPOI database had a "moderate" capacity to detect an outlet present in the field (sensitivity: 54.5%) or to list only the outlets that actually existed in the field (PPV: 64.4%). Relaxed measures of sensitivity and PPV were respectively 65.5% and 77.3%. The representativity of the EPOI database was 77.7%. Conclusions: The novel measure of representativity might serve as an alternative to traditional validity measures, and could be more appropriate in certain situations, depending on the nature and scale of the research question. [ABSTRACT FROM AUTHOR]

Abstract: Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Existing algorithms typically assume that data characteristics are stable over time. Their main focus is therefore to mine association rules in an efficient manner. However, the world constantly changes. This makes the characteristics of real-life entities represented by the data and hence the associations hidden in the data change over time. Detecting and adapting to the changes are usually critical to the success of many business organizations. This paper presents the problem of mining changes in association rules. Given a set of database partitions, each of which contains a set of transactions collected in a specific time period, a set of association rules is discovered in each database partition. We propose to perform data mining in the discovered association rules so as to reveal the regularities governing how the rules change in different time periods. Since the nature of many real-life entities is rather fuzzy, we propose to use linguistic variables and linguistic terms to represent the changes in the discovered association rules. In particular, fuzzy decision trees are built to discover the changes in the discovered association rules. The fuzzy decision trees are then converted to a set of fuzzy rules, called fuzzy meta-rules because they are rules about rules. By doing so, the changes hidden in the data can be revealed and presented to human users in a comprehensible form. Furthermore, the discovered changes can also be used to predict any change in the future. To evaluate the performance of our approach, we make use of a set of synthetic datasets, which are database partitions collected in different time periods. A set of association rules is discovered in each dataset. Fuzzy decision trees are constructed in the discovered association rules in order to reveal the changes in these rules. The experimental results show that our approach is very promising. [Copyright &y& Elsevier]