Temporal data exchange

Golshanara, Ladan; Chomicki, Jan

doi:10.1016/j.is.2019.07.004

Cited by 6 publications

(12 citation statements)

References 13 publications

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“…Root and internal node contains pointers S i and values K i , the pointer S i refers to nodes with lower values than the corresponding value (K i ), pointer S i+1 references higher (or equal) values. Leaf nodes are directly connected to the file data (using pointers) [18].…”

Section: Index Structures and Access Methodsmentioning

confidence: 99%

“…Other index techniques mostly arise from this category and improve either modeled data group or data shapes to be processed, like inverted B+tree key, its unique version, table, or cluster index. Different structural approaches are based on a bitmap or hash indexes, which cannot be, however, universally used due to specific requirements and limitations [16][17][18].…”

Section: Index Structures and Access Methodsmentioning

confidence: 99%

“…From the physical implementation, it can be defined either as a varray, nested table as the table attribute or by the table itself. Based on the performance evaluation, the most suitable solution provides the direct table [17,18]. The reason is mostly based on indexing.…”

Section: Log Buffermentioning

confidence: 99%

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Data Block and Tuple Identification Using Master Index

Kvet

2020

Sensors

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Relational databases are still very often used as a data storage, even for the sensor oriented data. Each data tuple is logically stored in the table referenced by relationships between individual tables. From the physical point of view, data are stored in the data files delimited by the tablespaces. Files are block-oriented. When retrieving data, particular blocks must be identified and transferred into the memory for the evaluation and processing. This paper deals with storage principles and proposes own methods for effective data block location and identification if no suitable index for the query is present in the system. Thanks to that, the performance of the whole system is optimized, and the processing time and costs are significantly lowered. The proposed solution is based on the master index, which points just to the blocks with relevant data. Thus, no sequential block scanning is necessary for consuming many system resources. The paper analyzes the impact of block size, which can have a significant impact on sensor oriented data, as well.

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Section: Index Structures and Access Methodsmentioning

confidence: 99%

Section: Index Structures and Access Methodsmentioning

confidence: 99%

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Data Block and Tuple Identification Using Master Index

Kvet

2020

Sensors

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“…To the best of our knowledge, temporal data exchange between relational schemas and ontologies has not been studied formally. Even in the relational-torelational setting, [16] is the only publication that formally addresses temporal data exchange, for the case in which each source-to-target dependency uses at most one temporal variable. As time has its own semantics, adding it to data exchange is not a matter of simply adding temporal attributes to data-exchange rules.…”

Section: Introductionmentioning

confidence: 99%

“…As time has its own semantics, adding it to data exchange is not a matter of simply adding temporal attributes to data-exchange rules. Thus, the formal constructs in [16] are rather involved. We use the results of [16] in the experimental validation of our proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Temporal Enrichment and Querying of Ontology-Compliant Data

Cheng

Chirkova

et al. 2020

Communications in Computer and Information Science

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We consider the problem of answering temporal queries on RDF stores, in presence of time-agnostic RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed solution consists of two rule-based domainindependent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology, using SPARQL supplemented with a lightweight easy-to-use formalism for time annotations and comparisons. The algorithm translates the queries into the standard SPARQL form that respects the structure of the temporal RDF information while preserving the semantics of the questions, thus ensuring successful evaluation of the queries on the materialized temporally-enriched RDF data. In this paper we present the algorithms, report on their implementation and experimental results for two application domains, and discuss future work.

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Universal solutions for temporal data exchange

Cheng

Kolaitis

2021

Information and Computation

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Temporal data exchange

Cited by 6 publications

References 13 publications

Data Block and Tuple Identification Using Master Index

Data Block and Tuple Identification Using Master Index

Temporal Enrichment and Querying of Ontology-Compliant Data

Universal solutions for temporal data exchange

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