1,451 research outputs found

    A comparison of statistical machine learning methods in heartbeat detection and classification

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    In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

    Data management in cloud environments: NoSQL and NewSQL data stores

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    : Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages

    Toward Generic Abstractions for Data of Any Model

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    International audienceDigital data sharing leads to unprecedented opportunities to develop data-driven systems for supporting economic activities, the social and political life, and science. Many open-access datasets are RDF graphs, but others are CSV files, Neo4J property graphs, JSON or XML documents, etc. Potential users need to understand a dataset in order to decide if it is useful for their goal. While some datasets come with a schema and/or documentation, this is not always the case. Data summarization or schema inference tools have been proposed, specializing in XML, or JSON, or the RDF data models. In this work, we present a dataset abstraction approach, which () applies on relational, CSV, XML, JSON, RDF or Property Graph data; () computes an abstraction meant for humans (as opposed to a schema meant for a parser); () integrates Information Extraction data profiling, to also classify dataset content among a set of categories of interest to the user. Our abstractions are conceptually close to an Entity-Relationship diagram, if one allows nested and possibly heterogeneous structure within entities

    Effects of automatically updated database documentation on the work tasks of IS professionals and end-users

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    Abstract. This study analysed the effects of automatically generated database documentation on the work tasks of IS professionals and end-users. The effects were analysed with case study approach in one Finnish software company that provides customer information system to their customer companies. The availability of the consistent database documentation was a new situation for both IS professionals in the case company and for the end-users in the customer companies. The case company wished to understand, how the documentation would affect the work tasks of the two stakeholder groups. Database documentation is used for database design and analysis tasks. Database documentation is created in the development phase of an IS and maintained afterwards. Yet, the documentation can end up stagnated and the documentation maintenance is commonly considered a burden for the IS professionals. Database reverse-engineering allows automatic generation of database documentation, thus erasing the manual maintenance of database documentation. The research data was gathered using two separate questionnaires for the end-users and IS professionals. Both qualitative and quantitative data were gathered. The questionnaire’s open questions were analysed using content analysis and closed questions were considered descriptive statistics. According to the findings, database documentation supports both IS professionals’ and end-users’ in their work tasks. Database documentation was considered important for the efficiency of their work tasks. Documentation aids the database users to gain knowledge of the database structure and prevents false interpretations. Database documentation also allows the users to plan work better and to conduct tasks with fewer increments. Additionally, documentation allows a better degree of independent work and gives courage for the employees to familiarize themselves to new parts of the database and information system. The consistent, up-to-date database documentation has positive effects on the work tasks of both IS professionals and end-users. Lower workload, better understanding of the system and less false interpretations indicate that software companies would benefit from documenting their databases in more detail and more consistently, for example, using database reverse-engineering. Consistent database documentation benefits all of the database users

    Reasoning & Querying – State of the Art

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    Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

    Spatial ontologies for architectural heritage

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    Informatics and artificial intelligence have generated new requirements for digital archiving, information, and documentation. Semantic interoperability has become fundamental for the management and sharing of information. The constraints to data interpretation enable both database interoperability, for data and schemas sharing and reuse, and information retrieval in large datasets. Another challenging issue is the exploitation of automated reasoning possibilities. The solution is the use of domain ontologies as a reference for data modelling in information systems. The architectural heritage (AH) domain is considered in this thesis. The documentation in this field, particularly complex and multifaceted, is well-known to be critical for the preservation, knowledge, and promotion of the monuments. For these reasons, digital inventories, also exploiting standards and new semantic technologies, are developed by international organisations (Getty Institute, ONU, European Union). Geometric and geographic information is essential part of a monument. It is composed by a number of aspects (spatial, topological, and mereological relations; accuracy; multi-scale representation; time; etc.). Currently, geomatics permits the obtaining of very accurate and dense 3D models (possibly enriched with textures) and derived products, in both raster and vector format. Many standards were published for the geographic field or in the cultural heritage domain. However, the first ones are limited in the foreseen representation scales (the maximum is achieved by OGC CityGML), and the semantic values do not consider the full semantic richness of AH. The second ones (especially the core ontology CIDOC – CRM, the Conceptual Reference Model of the Documentation Commettee of the International Council of Museums) were employed to document museums’ objects. Even if it was recently extended to standing buildings and a spatial extension was included, the integration of complex 3D models has not yet been achieved. In this thesis, the aspects (especially spatial issues) to consider in the documentation of monuments are analysed. In the light of them, the OGC CityGML is extended for the management of AH complexity. An approach ‘from the landscape to the detail’ is used, for considering the monument in a wider system, which is essential for analysis and reasoning about such complex objects. An implementation test is conducted on a case study, preferring open source applications
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