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Title: Facilitating the exploitation of Linked Open Statistical Data: JSON-QB API requirements and design criteria
Authors: Zeginis, Dimitris
Kalampokis, Evangelos
Bill, Roberts
Moynihan, Rick
Tambouris, Efthimios
Tarabanis, Konstantinos
Editors: Capadisli, Sarven
Cotton, Franck
Dong, Xin Luna
Guha, Ramanathan V.
Haller, Armin
Hitzler, Pascal
Kalampokis, Evangelos
Kejriwal, Mayank
Sivakumar, D.
Szekely, Pedro
Troncy, Raphaël
Witbrock, Michael
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Linked data
Statistical data
data cube
Issue Date: 2017
Volume Title: Joint Proceedings of the International Workshops on Hybrid Statistical Semantic Understanding and Emerging Semantics, and Semantic Statistics co-located with 16th Extended Semantic Web Conference (ISWC 2017)
Abstract: Recently, many organizations have opened up their data for others to reuse. A major part of these data concern statistics such as demographic and social indicators. Linked Data is a promising paradigm for opening data because it facilitates data integration on the Web. Recently, a growing number of organizations adopted linked data paradigm and provided Linked Open Statistical Data (LOSD). These data can be exploited to create added value services and applications that require integrated data from multiple sources. In this paper, we suggest that in order to unleash the full potential of LOSD we need to facilitate the interaction with LOSD and hide most of the complexity. Moreover, we describe the requirements and design criteria of a JSON-QB API hat (i) facilitates the development of LOSD tools through a style of interaction familiar to web developers and (ii) offers a uniform way to access LOSD. A proof of concept implementation of the JSON-QB API demonstrates part of the proposed functionality.
Appears in Collections:Department of Applied Informatics
Department of Business Administration

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