Incremental Evaluation of Continuous Analytic Queries in HIFUN

Zervoudakis, Petros; Kondylakis, Haridimos; Plexousakis, Dimitris; Spyratos, Nicolas

doi:10.1007/978-3-030-44900-1_4

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Finally, we should mention that an interactive system for analytics that uses HIFUN as the intermediate layer, enables the formulation of analytic queries over data sources with different data models and query languages, since there are already mappings of HIFUN to SQL (for relational sources), and to MapReduce using SPARK [46], thus our work enables applying that model over RDF data sources too.…”

Section: Future Workmentioning

confidence: 99%

Towards Interactive Analytics over RDF Graphs

2021

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The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in the Resource Description Framework (RDF) bring new requirements for data analytics tools. Such tools should take into account the special features of RDF graphs, exploit the semantics of RDF and support flexible aggregate queries. In this paper, we present an approach for applying analytics to RDF data based on a high-level functional query language, called HIFUN. According to that language, each analytical query is considered to be a well-formed expression of a functional algebra and its definition is independent of the nature and structure of the data. In this paper, we investigate how HIFUN can be used for easing the formulation of analytic queries over RDF data. We detail the applicability of HIFUN over RDF, as well as the transformations of data that may be required, we introduce the translation rules of HIFUN queries to SPARQL and we describe a first implementation of the proposed model.

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Section: Future Workmentioning

confidence: 99%

Towards Interactive Analytics over RDF Graphs

2021

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Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

et al. 2021

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HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

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Incremental Evaluation of Continuous Analytic Queries in HIFUN

Cited by 2 publications

References 12 publications

Towards Interactive Analytics over RDF Graphs

Towards Interactive Analytics over RDF Graphs

Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

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