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Metadata Discovery Problem 2015 |
Metadata is often described as “data about data.” Usage varies from virtualization to data warehousing to statistics. Because of its volume and complexity, metadata has the potential to tax security procedures and processes. The C3E page at http://cps-vo.org/node/13712 describes the Metadata-based Malicious Cyber Discovery Problem and solicits research and papers. The bibliography presented here looks at what has been published in 2015.
Chappell, A.; Weaver, J.; Purohit, S.; Smith, W.; Schuchardt, K.; West, P.; Lee, B.; Fox, P., "Enhancing the Impact of Science Data Toward Data Discovery and Reuse," in Computer and Information Science (ICIS), 2015 IEEE/ACIS 14th International Conference on, pp. 271-277, June 28 2015-July 1 2015. doi: 10.1109/ICIS.2015.7166605
Abstract: The amount of data produced in support of scientific research continues to grow rapidly. Despite the accumulation and demand for scientific data, relatively little data are actually made available for the broader scientific community. We surmise that one root of this problem is the perceived difficulty of electronically publishing scientific data and associated metadata in a way that makes it discoverable. We propose exploiting Semantic Web technologies and best practices to make metadata both discoverable and easy to publish. We share experiences in curating metadata to illustrate the cumbersome nature of data reuse in the current research environment. We also make recommendations with a real-world example of how data publishers can provide their metadata by adding limited additional markup to HTML pages on the Web. With little additional effort from data publishers, the difficulty of data discovery, access, and sharing can be greatly reduced and the impact of research data greatly enhanced.
Keywords: data handling; meta data; semantic Web; HTML pages; data access; data discovery; data publishing; data reuse; data sharing; meta data; science data; semantic Web technologies; Moisture measurement; Ontologies; Resource description framework; Semantics; Soil measurements; Soil moisture; Data Curation; Data Discovery; Data Publishing Recommendations; Digital Data Sharing; Linked Data (ID#: 15-7947)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7166605&isnumber=7166553
Dinata, S.; Dewabharata, A.; Shuo-Yan Chou, "An Ontology-Enabled Service Discovery for Supporting Health Promotion System," in Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39th Annual, vol. 3, pp. 276-281, 1-5 July 2015. doi: 10.1109/COMPSAC.2015.244
Abstract: Health promotion related products and services have grown rapidly in recent years, many devices and services were developed for this endeavor. Consequently, there is a need to represent fragmented functions into a general description and comparable shape in order to provide context-based matching and ranking, which is a substance of the whole services. A service discovery was introduced as a mechanism to support user in reducing the difficulties. There is also an approach to the problem of context matching and ranking of services towards resulted recommendation. This research introduced methods or mechanisms to address the issue of the semantic similarity assessment among services with the recommendation, such as TF/IDF and context analysis. Each service or application is represented by metadata designed by using ontology to allow modifiable and collaborative work in this field. Selection of the most suitable metadata definition language was also the issue covered by this work.
Keywords: health care; meta data; ontologies (artificial intelligence);ubiquitous computing; context-based matching; context-based ranking; fragmented functions; health promotion system; metadata definition language; ontology-enabled service discovery; semantic similarity assessment;Androids;Context; Context-aware services; Metadata; Ontologies; Sensor phenomena and characterization; Health promotion; metadata; mobile and wearable computing; ontology; persuasive technology; service discovery (ID#: 15-7948)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273368&isnumber=7273299
Rauch, M.; Klieber, W.; Wozelka, R.; Singh, S.; Sabol, V., "Knowminer Search - A Multi-visualisation Collaborative Approach to Search Result Analysis," in Information Visualisation (iV), 2015 19th International Conference on, pp. 379-385, 22-24 July 2015. doi: 10.1109/iV.2015.72
Abstract: The amount of information available on the internet and within enterprises has reached an incredible dimension. Efficiently finding and understanding information and thereby saving resources remains one of the major challenges in our daily work. Powerful text analysis methods, a scalable faceted retrieval engine and a well-designed interactive user interface are required to address the problem. Besides providing means for drilling-down to the relevant piece of information, a part of the challenge arises from the need of analysing and visualising data to discover relationships and correlations, gain an overview of data distributions and unveil trends. Visual interfaces leverage the enormous bandwidth of the human visual system to support pattern discovery in large amounts of data. Our Know miner search builds upon the well-known faceted search approach which is extended with interactive visualisations allowing users to analyse different aspects of the result set. Additionally, our system provides functionality for organising interesting search results into portfolios, and also supports social features for rating and boosting search results and for sharing and annotating portfolios.
Keywords: data visualisation; information analysis; information retrieval; user interfaces; data analysis; data visualization; human visual system; interactive user interface; knowminer search approach; multivisualisation collaborative approach; pattern discovery; portfolio annotation; portfolio search; scalable faceted retrieval engine; search result analysis; visual interface; Data mining; Data visualization; Metadata; Portfolios; Search engines; Semantics; Visualization; Search interface; faceted search; multi-visualisation analysis; shared result portfolios (ID#: 15-7949)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7272630&isnumber=7272518
Santana Guimaraes, F.S.; Quaresma, P.; Pampulim Caldeira, C., "Information Audit Based on Ontology," in Information Systems and Technologies (CISTI), 2015 10th Iberian Conference on, pp. 1-7, 17-20 June 2015. doi: 10.1109/CISTI.2015.7170596
Abstract: Information Audit use methods and CAATTS (Computer-Assisted Audit Techniques) to capture, analyze and evaluate organizational information assets, in regular or continuous basis. However, does not exist a widespread adoption of CAATTs and the CAATTs normally does not use a kind of model that allows to capture the structure and semantic of information as part of the data capture process to audit. Based on this problem, this article focuses on the domain Ontology and Metadata used in Data Governance concepts and Data Lineage for its application to Audit Information.
Keywords: auditing; meta data; ontologies (artificial intelligence); semantic Web; CAATTS; computer-assisted audit techniques; data capture process; data governance concepts; data lineage; domain ontology; information audit; information semantic; information structure; metadata; organizational information asset analysis; organizational information asset capture; organizational information asset evaluation; semantic Web; Bismuth; Business; Metadata; Ontologies; Standards; Unified modeling language; Business Intelligence; Data Discovery; Data Governance; Data Lineage; Information Audit; Metadata; Natural Language Processing; Ontology; Semantic Web (ID#: 15-7950)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7170596&isnumber=7170350
Note:
Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to news@scienceofsecurity.net for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.