Implementation of data mining applications is a challenging and complicated task, and the applications are often built from scratch. In this paper, a component-based application framework, called Smart Archive (SA) designed for implementing data mining applications, is presented. SA provides functionality common to most data mining applications and components for utilizing history information. Using SA, it is possible to build high-quality applications with shorter development times by configuring the framework to process application-specific data. The architecture, the components, the implementation and the design principles of the framework are presented. The advantages of a framework-based implementation are demonstrated by presenting a case study which compares the framework approach to implementing a real-world application with the option of building an equivalent application from scratch. In conclusion, the paper presents a lucid framework for creating data mining applications and illustrates the importance and advantages of using the presented approach.
Abstract.Resistance spot welding is an important and widely used method for joining metal objects. In this paper, various classification methods for identifying welding processes are evaluated. Using process identification, a similar process for a new welding experiment can be found among the previously run processes, and the process parameters leading to high-quality welding joints can be applied. With this approach, good welding results can be obtained right from the beginning, and the time needed for the set-up of a new process can be substantially reduced. In addition, previous quality control methods can also be used for the new process. Different classifiers are tested with several data sets consisting of statistical and geometrical features extracted from current and voltage signals recorded during welding. The best feature setclassifier combination for the data used in this study is selected. Finally, it is concluded that welding processes can be identified almost perfectly by certain features.
Collaborative knowledge discovery is a promising approach by which people with no data analytics expertise could benefit from an analysis of their own personal data by experts. To facilitate effective collaboration between data owners and knowledge discovery experts, we have developed a software platform that uses a domain ontology to represent knowledge relevant to the execution of the collaborative knowledge discovery process. The ontology provides classes representing the main elements of collaborations: collaborators and datasets. Furthermore, the ontology enables the specification of privacy constraints that determine the precise extent to which a given dataset of personal data is shared with a given collaborator. We have developed a client-server software platform that enables users to initiate collaborations, invite experts to join them, create datasets and share them with experts, and create visualisations of data. The collaborations are mediated through the creation, modification and deletion of individuals in the underlying ontology and the propagation of ontology changes to each client connected to the server.
Computational intelligence is making its way into a variety of popular consumer products, including wearable physiological monitors such as activity trackers and sleep trackers. Such products are very convenient for the user, but this convenience is the result of a trade-off that has ethical implications, since in almost all cases it denies the user access to their own raw data underlying the easy-to-understand analyses that the products generate for them. One problem with this is that the user is not made aware of the uncertainty of the conclusions or analyses drawn from the data; another is that it is difficult for the user to reuse his or her data in other contexts, such as to combine data from multiple sources. Even if the user did have full control of the data, this would only solve part of the problem, because most people do not have the special skills required to analyze such data. This overall problem could be solved through collaboration between the data owner and a data analysis expert, though this again introduces further problems, notably that of preserving the data owner's privacy. In this paper we analyze the aforementioned issues pertaining to the ethics of wearable intelligence, propose possible approaches to handling them, and discuss the potential social impact of the technology if the issues can be successfully overcome.
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