Federated query processing on linked data: a qualitative survey and open challenges

Oğuz, Damla; Ergenc, Belgin; Yin, Shi; Dikenelli, Oğuz; Hameurlain, Abdelkader

doi:10.1017/s0269888915000107

Cited by 29 publications

(24 citation statements)

References 54 publications

(116 reference statements)

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“…within a single repository [2, 10,128,117]. In a similar manner to a distributed database, given a query Q and a set of datasets (the sources), the query engine first selects the datasets relevant to the query [121,133] and then chooses between different approaches: aggregating the datasets locally, using distributed processing as in Hadoop [143], or a federated approach [107].…”

Section: Common Search Architecturesmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

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Section: Common Search Architecturesmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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“…To fill this gap, in this paper we focus on methods, supported by special indexes, for performing tasks and measurements that involve more than two datasets. Such indexes and measurements are important: (a) for obtaining complete information about one particular URI (or set of URIs) with their provenance, (b) for obtaining measurements that are useful for dataset discovery and selection [6,17,18], (c) for assessing the connectivity between any set of datasets for quality checking and for monitoring their evolution over time [14], (d) for constructing visualizations [3] that provide more informative overviews and could also aid dataset discovery. Overall, the aforementioned tasks can assist data scientists since according to several studies they currently spend most of their time in collecting and preparing unruly digital data.…”

Section: Introductionmentioning

confidence: 99%

On measuring the lattice of commonalities among several linked datasets

Mountantonakis

Tzitzikas

2016

Proc. VLDB Endow.

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A big number of datasets has been published according to the principles of Linked Data and this number keeps increasing. Although the ultimate objective is linking and integration, it is not currently evident how connected the current LOD cloud is. Measurements (and indexes) that involve more than two datasets are not available although they are important: (a) for obtaining complete information about one particular URI (or set of URIs) with provenance (b) for aiding dataset discovery and selection, (c) for assessing the connectivity between any set of datasets for quality checking and for monitoring their evolution over time, (d) for constructing visualizations that provide more informative overviews. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this paper we introduce indexes (and their construction algorithms) that can speedup such tasks. In brief, we introduce (i) a namespace-based prefix index, (ii) a sameAs catalog for computing the symmetric and transitive closure of the owl:sameAs relationships encountered in the datasets, (iii) a semantics-aware element index (that exploits the aforementioned indexes), and finally (iv) two lattice-based incremental algorithms for speeding up the computation of the intersection of URIs of any set of datasets. We discuss the speedup obtained by the introduced indexes and algorithms through comparative results and finally we report measurements about connectivity of the LOD cloud that have never been carried out so far.

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“…This can be explained as dividing a query into subqueries and sending it to semantic query endpoints. This allows the easy integration of linked data from different semantic query endpoints, in a standardized data model (RDF) [53]. This is one of the powerful features of the semantic web and geospatial semantic web, and it facilitates an easier way to build applications with a suitable combination of heterogeneous data from various resources.…”

Section: Geospatially Linked Data In Digital Gazetteersmentioning

confidence: 99%

A Survey of Geospatial Semantic Web for Cultural Heritage

2019

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The amount of digital cultural heritage data produced by cultural heritage institutions is growing rapidly. Digital cultural heritage repositories have therefore become an efficient and effective way to disseminate and exploit digital cultural heritage data. However, many digital cultural heritage repositories worldwide share technical challenges such as data integration and interoperability among national and regional digital cultural heritage repositories. The result is dispersed and poorly-linked cultured heritage data, backed by non-standardized search interfaces, which thwart users’ attempts to contextualize information from distributed repositories. A recently introduced geospatial semantic web is being adopted by a great many new and existing digital cultural heritage repositories to overcome these challenges. However, no one has yet conducted a conceptual survey of the geospatial semantic web concepts for a cultural heritage audience. A conceptual survey of these concepts pertinent to the cultural heritage field is, therefore, needed. Such a survey equips cultural heritage professionals and practitioners with an overview of all the necessary tools, and free and open source semantic web and geospatial semantic web platforms that can be used to implement geospatial semantic web-based cultural heritage repositories. Hence, this article surveys the state-of-the-art geospatial semantic web concepts, which are pertinent to the cultural heritage field. It then proposes a framework to turn geospatial cultural heritage data into machine-readable and processable resource description framework (RDF) data to use in the geospatial semantic web, with a case study to demonstrate its applicability. Furthermore, it outlines key free and open source semantic web and geospatial semantic platforms for cultural heritage institutions. In addition, it examines leading cultural heritage projects employing the geospatial semantic web. Finally, the article discusses attributes of the geospatial semantic web that require more attention, that can result in generating new ideas and research questions for both the geospatial semantic web and cultural heritage fields.

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Federated query processing on linked data: a qualitative survey and open challenges

Cited by 29 publications

References 54 publications

Dataset search: a survey

Dataset search: a survey

On measuring the lattice of commonalities among several linked datasets

A Survey of Geospatial Semantic Web for Cultural Heritage

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