2018/12/27 by Anna Kokolaki, Kokolaki, Anna, Yannis Tzitzikas +1
Computer Science · Decision Sciences · #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Scientific Computing and Data Management #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1812.10734
openalex publication_date 2018/12/27 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
There is a plethora of datasets in various formats which are usually stored\nin files, hosted in catalogs, or accessed through SPARQL endpoints. In most\ncases, these datasets cannot be straightforwardly explored by end users, for\nsatisfying recall-oriented information needs. To fill this gap, in this paper\nwe present the design and implementation of Facetize, an editor that allows\nusers to transform (in an interactive manner) datasets, either static (i.e.\nstored in files), or dynamic (i.e. being the results of SPARQL queries), to\ndatasets that can be directly explored effectively by themselves or other\nusers. The latter (exploration) is achieved through the familiar interaction\nparadigm of Faceted Search (and Preference-enriched Faceted Search).\nSpecifically in this paper we describe the requirements, we introduce the\nrequired set of transformations, and then we detail the functionality and the\nimplementation of the editor Facetize that realizes these transformations. The\nsupported operations cover a wide range of tasks (selection, visibility,\ndeletions, edits, definition of hierarchies, intervals, derived attributes, and\nothers) and Facetize enables the user to carry them out in a user-friendly and\nguided manner, without presupposing any technical background (regarding data\nrepresentation or query languages). Finally we present the results of an\nevaluation with users. To the best of your knowledge, this is the first editor\nfor this kind of tasks.\n