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REVIEW 4 major objections 8 minor 60 references

Domain-Specific Data Quality Analysis Using Technology-Independent Query Templates

T0 review · 4 major / 8 minor · reviewed 2026-07-31 · grok-4.5

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Pith's one-line read Domain experts can define technology-independent quality analyses from reusable templates without writing queries.

desk verdict Useful multi-tech quality-template systems paper with open artifacts; abstract overclaims independence and “exceeds query languages” in ways the evidence only partly supports. read the letter →

arxiv 2607.24151 v1 pith:TLZZ5NOI submitted 2026-07-27 cs.DB cs.IR

Arno Kesper , Lukas Sebastian Hofmann , Markus Matoni , Gabriele Taentzer This is my paper · ORCID
classification cs.DBcs.IR
keywords DataQualityModel-DrivenEngineeringQueryTemplatesConstraintsXMLRDFNeo4jDomainExperts
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Data quality rules are domain-specific, yet writing them as XQuery, SPARQL, Cypher or SQL queries forces domain experts to rely on data engineers and to re-implement the same rules for every database technology. This paper argues that most quality checks follow recurring logical patterns that can be captured once as technology-independent templates. The Quality Pattern Model (QPM) lets a data engineer define a generic template; the system then adapts it to XML, RDF or Neo4j and lets a domain expert fill in schema-specific parameters through a form-based interface. The resulting instance is compiled automatically into an executable query. Evaluations on cultural-heritage collections show that a library of roughly thirty such templates covers the bulk of real requirements, matches or exceeds ordinary query languages (via optional custom operators), and can be used by domain experts without IT support.

What carries the argument

The Quality Pattern Model (QPM): a three-level model-driven language in which a generic template is first adapted to a database technology and then fully parameterized into an executable QPM instance that compiles to XQuery, SPARQL or Cypher (optionally extended by external string-to-boolean functions).

What would settle it

Take a fresh corpus of quality requirements from another domain or institution; if substantially more than a few percent cannot be realized by any existing or reasonably extended QPM template, or if domain experts systematically fail to select and parameterize the right templates, the central claim fails.

Watch

Extended reading notes

Core claim

A modest library of generic first-order and graph-pattern templates, automatically specialized to concrete database technologies and parameterized by domain experts, is sufficient to express the great majority of practical data-quality constraints and can be used by those experts without writing technology-specific queries.

Load-bearing premise

That a small set of reusable logical templates plus simple parameter filling is enough for most real quality rules, and that domain experts can choose and fill those templates from informal knowledge of their schema.

Editorial extensions

If this is right

  • The same quality constraint can be written once and executed against XML, RDF and graph stores without re-implementation.
  • Institutions can maintain a shared template library that grows only when a genuinely new kind of requirement appears.
  • Custom operators (e.g., URL or spelling checks) become available inside ordinary quality analyses without leaving the template framework.
  • Domain experts can own the definition and maintenance of quality rules instead of depending on continuous IT mediation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the template library proves stable across domains, quality-rule exchange between institutions becomes possible without shared schemas or query dialects.
  • The same three-level abstraction could be applied to other recurring analytic tasks (lineage checks, privacy rules) that today are re-coded per technology.
  • Success hinges on whether natural-language parameter hints remain accurate once schemas grow large and heterogeneous; that is a testable next experiment.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The paper presents QPM (Quality Pattern Model), a model-driven framework in which reusable, technology-independent "generic templates" for data-quality analyses are automatically adapted to specific database technologies (XML/XQuery, RDF/SPARQL, Neo4j/Cypher) and then parameterized by domain experts into executable instances. A web frontend, Constrainify, supports analysis definition and execution, currently for XML/LIDO only. The evaluation formalizes ~130 requirements from the Deutsche Digitale Bibliothek into 116 QPM instances using 30 generic templates (96% of the in-scope requirements), applies the templates to three real collections (a ~700k-record LIDO XML archive, Wikidata, and the Regesta Imperii Neo4j graph), compares expressiveness with common query languages, and reports a qualitative usability study with eight domain experts. The abstract's two headline claims are that QPM "matches and even exceeds the expressiveness of common database query languages" and that the tool lets domain experts define analyses "independently, without requiring support of IT experts."

Significance. If the claims are properly scoped, this is a solid and useful systems contribution. Technology-independent quality-constraint templates address a real and recurring cost in multi-technology institutions, and the cultural-heritage grounding is unusually concrete: a requirements catalog derived from interviews and workshops with 19 experts, a formalization of real DDB delivery requirements into 116 machine-executable constraints, and runs on three genuinely different production-scale datasets including the 120M-entity Wikidata. The authors also ship reproducible artifacts (backend and frontend on Zenodo/GitHub/GitLab, template library, constraint collections, user-study materials), which substantially strengthens the empirical claims. The honest threats-to-validity discussion in §6.3 is welcome. The work is weakened, however, by headline claims that are stated without the scoping the paper's own evidence requires; the underlying system is sound, but the framing must be brought into line with what is demonstrated.

major comments (4)
  1. [Abstract; §6.2.1; §6.2.3; §6.1.2] Abstract, §6.2.3, §7.3, §8: The claim that QPM 'matches and even exceeds the expressiveness of common database query languages' is stated unscoped, but the paper's own evidence supports it only in disjoint configurations. 'Matches' holds only for the quality-analysis-relevant retrieval subset: §6.2.1 concedes QPM lacks arithmetic, grouping, ordering, and predefined function calls, so it matches a proper subset of XQuery/SPARQL/Cypher, not the languages as such (contrast the stronger wording 'For pure data retrieval, QPM matches and exceeds...' in §6.2.3). 'Exceeds' rests entirely on the custom-operator extension of §5.4, which (a) is implemented only for XQuery/Java — §6.1.2 and Table 4 show that 9 of the 30 templates are XML-exclusive precisely because they rely on custom operators, so on RDF and Neo4j the claim reduces to 'matches a subset'; and (b) supports only string→boolean methods
  2. [Abstract; §4.1; §5.1; §5.4; §5.5; §6.3] The 'exceeds' mechanism is in direct tension with the second headline claim of domain-expert independence 'without requiring support of IT experts' (Abstract). Every capability that takes QPM beyond query languages (EA and NLP columns in Tables 3 and 5) requires someone to write, deploy, and maintain Java functions — i.e., exactly the data-engineer involvement the framework promises to eliminate. Likewise, the §4.1 workflow already assigns Template Creation to data engineers, the Template Creation System is only 'a rough prototype' (§5.1), and Constrainify currently supports XML only (§5.5). The user study (§6.3) exercised instantiation of pre-built templates, not the full pipeline. The paper should present this honestly as a division-of-labor story (engineers amortized over template creation; experts autonomous at instantiation time) rather than an independence claim, and temper the abs
  3. [§6.1.2; §5.4] RQ1 claims applicability 'in practice' on collections up to 120M entities, but the evaluation reports no performance data whatsoever: no runtimes, no result-set sizes, no memory behavior. This matters especially for the §5.4 custom-operator path, which materializes a complete intermediate result structure (all candidate results plus per-result values) outside the database before Java filtering; the pushdown argument is qualitative only. A small table of execution times and result counts per template/technology (even for a subset of the 21 cross-technology templates) would substantially strengthen RQ1 and clarify whether the XML-only extension is viable at DDB/Wikidata scale.
  4. [§3.2, Table 3; §6.2.1] Table 3 marks external access (EA) and NLP as unsupported (✗) in all four query languages. This is at best misleading: SPARQL 1.1 has SERVICE for federated/external access, XQuery and most XQuery processors support extension/external functions, and Cypher has user-defined procedures and functions (APOC). None of these negate QPM's contribution (a uniform, technology-independent mechanism), but the comparison as printed overstates the baseline gap that the 'exceeds' claim depends on. The table and §3.2 text should be corrected or qualified (e.g., 'not standardized/portable across languages').
minor comments (8)
  1. [§6.1.1] §6.1.1: The 96% figure should state its denominator explicitly. 130 constraints minus 9 out-of-scope leaves 121; 116/121 = 96%. As written, '130... 116... 96%' invites confusion. Also clarify the discrepancy between 'nine out-of-scope' and 'five constraints remained unrealized' in one place.
  2. [§4.2] §4.2: Broken sentence — 'A generic QPM template is instantiated to a QPM template by adapting it to a specific database technology. infers parameters regarding the addressing of structure elements...' The subject of the second sentence is missing and the terminology (generic template vs. template vs. instance) needs care here since §4.2 is where the three-level model is defined.
  3. [§3.2] §3.2: 'The expressiveness of all these different algebras strictly exceeds first-order logic' is asserted without support and is not standard usage (SQL/SPARQL retrieval cores are essentially FOL plus aggregation/counting). Either cite a result or rephrase (e.g., 'extend first-order logic with aggregation and traversal primitives').
  4. [§5.5] §5.5: The natural-language parameter mapping in Constrainify ('the expert could use the words “person” or “painter” to search for the LIDO field') is a notable feature but its mechanism is never described — string matching, synonym table, embeddings? One or two sentences, plus failure behavior when mapping is ambiguous, would help.
  5. [Table 4; §6.1.2] Table 4: 'Abstract Templates' row (30/21/21) is confusing alongside 'Generic Templates' (30/30/30); the text explains this only in passing ('only 21 of them are applicable to RDF and Neo4j'). Rename the row or add a footnote defining the distinction between generic, technology-adapted, and instantiated templates as used in the table.
  6. [§7.1] §7.1: The GraphQ discussion cites an EMNLP semantic-parsing IR as if it were a deployed unified graph query language; a sentence clarifying that it is a parsing intermediate representation rather than a query language per se would make the comparison fairer.
  7. [Figures] Figures 4, 9–13 are central to understanding the template/instance distinction and the UI, but several are low-resolution screenshots with small text; please ensure legibility at print scale. Fig. 1's color legend ('blue/orange/green') is hard to verify in grayscale.
  8. [§6.3] §6.3: Consider reporting the questionnaire instrument's items/scores (even aggregated) rather than only qualitative impressions; with n=8 this is cheap and would make 'no significant usability barriers' more checkable. The reference data [42] helps, but a summary belongs in the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: constructive systems paper whose claims are checked against external requirements, databases, and users, not forced by self-definition or fitted inputs.

full rationale

QPM is introduced as an engineered artifact (metamodel, technology adapters, compilation to XQuery/SPARQL/Cypher, optional Java string→boolean hooks, Constrainify UI). The load-bearing evaluation steps are external: formalizing DDB delivery/format documents into 130 constraints and covering 96% with 30 templates (§6.1.1); instantiating templates on independent collections (DDK/DDB LIDO XML, public Wikidata RDF, Regesta Imperii Neo4j) (§6.1.2); feature comparison to standard query languages (§3.2, §6.2.1); and a qualitative user study with eight LIDO-experienced domain experts (§6.3). Prior author work ([13] XML-only prototype; [19] quality-problem catalog from interviews/workshop) is cited as background and as one expressiveness checklist, not as a uniqueness theorem or fitted constant that forces the multi-technology or usability claims. There is no fitted parameter renamed as a prediction, no self-definitional identity presented as derivation, and no ansatz smuggled in as external fact. Tension between “exceeds query languages” (via custom operators) and “without IT experts” is a scope/validity concern, not circularity. Score 0 is the honest finding.

Assumptions & free parameters 0 free parameters · 5 assumptions · 2 invented entities

The central claim rests on engineering design choices and domain premises about recurring quality patterns and comparable query-language power, not on fitted physical constants. Load-bearing premises are the adequacy of FOL+graph patterns (+optional external Java predicates) for quality work, and the usability of template selection by non-programmers.

assumptions (5)
  • domain assumption Common quality problems recur as structural patterns that can be captured by a finite library of generic templates independent of schema and technology.
    Motivated by the cultural-heritage catalog and Table 1 (§2); underpins the entire template workflow.
  • domain assumption XQuery, SPARQL, Cypher (and SQL) have comparable expressiveness for the quality-analysis operators needed (FOL, traversal, count, comparison, RE).
    Stated via feature comparison in §3.2 Table 3; justifies a single generic language compiling to all three.
  • domain assumption Domain experts can select and parameterize templates with only informal understanding of templates and schema, given NL-assisted UI mapping.
    Core of Use Case Analysis Definition (§4.1) and RQ3 user study (§6.3).
  • standard math First-order logic with graph/tree structure patterns plus count/comparison/RE is an adequate core semantics for quality constraints.
    QPM language design in §4–5; standard for constraint checking.
  • ad hoc to paper Custom operators as external string→boolean (or similar) functions after query pushdown correctly extend quality analysis beyond native QL expressiveness.
    §5.4 architecture; currently proven only for XQuery+Java and used to justify ‘exceeds’ claim.
invented entities (2)
  • Quality Pattern Model (QPM) / generic–technology-specific–instance template stack independent evidence
    purpose: Unify technology-independent quality-analysis templates with automatic compilation to concrete query languages.
    Central invented artifact of the paper; evaluated via implementation and case studies rather than external prior existence as a standard.
  • Constrainify web frontend for analysis definition and quality reports independent evidence
    purpose: Let domain experts instantiate templates and run analyses without writing queries.
    UI layer realizing the claimed independence from IT experts; currently XML/LIDO-focused.

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Cite this review

Pith. "Pith review of Domain-Specific Data Quality Analysis Using Technology-Independent Query Templates." pith.science (2026). https://pith.science/paper/TLZZ5NOI

@misc{pith2026260724151,
  author       = {Pith},
  title        = {Pith review of: Domain-Specific Data Quality Analysis Using Technology-Independent Query Templates},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TLZZ5NOI}},
  note         = {Machine review of arXiv:2607.24151}
}
read the original abstract

In an increasingly data-driven world, effectively working with data depends heavily on its quality. Quality analysis is a central aspect of data quality management. As data quality is typically domain- and context-specific, the definition of quality requirements is primarily the responsibility of domain experts. However, domain experts often lack the query language expertise needed to implement quality analyses. Therefore, the process of defining quality analyses results in a resource-intensive workflow that requires the involvement of technical experts, effectively excluding domain experts from independently managing data quality. To address this challenge, we present the Quality Pattern Model framework (QPM), a model-driven approach to define templates for data quality analyses that are independent of specific database technologies and application domains. QPM can eliminate the need for deep technical expertise and prevent the need for defining quality analyses several times for different database technologies. We present a proof-of-concept implementation of this approach for three database technologies: XML, RDF, and Neo4j. We evaluate the expressiveness of our approach, its applicability in the cultural heritage domain, and its usability by domain experts. For this purpose, we conducted a qualitative user study and empirically collected quality problems in a catalog. Our findings suggest that QPM matches and even exceeds the expressiveness of common database query languages. Furthermore, the results indicate that our tool enables domain experts to define template-based quality analyses independently, without requiring support of IT experts.

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Reference graph

Works this paper leans on

60 extracted references · 11 canonical work pages

  1. [1]

    Journal of Management Information Systems12(4), 5–33 (1996) https://doi.org/10.1080/07421222.1996.11518099

    Wang, R.Y., Strong, D.M.: Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems12(4), 5–33 (1996) https://doi.org/10.1080/07421222.1996.11518099

    arXiv 1996

  2. [2]

    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., Chui, M., Brown, B., Bughin, J., et al.: Big data: The next frontier for innovation, competition, and productivity (2011)

    2011

  3. [3]

    Communications of the ACM45(4), 211–218 (2002) https://doi.org/10.1145/505248.506010

    Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Communications of the ACM45(4), 211–218 (2002) https://doi.org/10.1145/505248.506010

    arXiv 2002

  4. [4]

    Strong, D.M., Lee, Y.W., Wang, R.Y.: Data quality in context. Commun. ACM 40(5), 103–110 (1997) https://doi.org/10.1145/253769.253804

    arXiv 1997

  5. [5]

    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases, (1995)

    1995

  6. [6]

    Kontinuierliches Qualit¨ atsmanagement von dynamischen Forschungsdaten zu Objekten der materiellen Kultur unter Nutzung des LIDO Standards

    Pham Thi, T.T., Helfert, M.: Discovering dynamic integrity rules with a rules- based tool for data quality analyzing. In: Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Stu- dents in Computing on International Conference on Computer Systems and Technologies, pp. 89–94 (2010) 27https://www.europea...

    2010

  7. [7]

    Bourret, R., et al.: Xml and databases (1999)

    1999

  8. [8]

    Addison-Wesley, Reading, MA, USA (1997)

    Date, C.J., Darwen, H.: A Guide to SQL Standard, 4th Edition. Addison-Wesley, Reading, MA, USA (1997)

    1997

Show all 60 references
  1. [9]

    In: Reason- ing Web International Summer School, pp

    Arenas, M., Gutierrez, C., P´ erez, J.: Foundations of rdf databases. In: Reason- ing Web International Summer School, pp. 158–204. Springer, Berlin, Heidelberg (2009)

    2009

  2. [10]

    Information Systems140, 102715 (2026) https://doi.org/10.1016/j.is.2026.102715

    Dormehl, J.A., Andrews, R., Kratsch, W., R¨ oglinger, M., Wynn, M.T., Zetzsche, F.: Domain experts in the loop: Leveraging generative artificial intelligence for interactive data validation in process mining. Information Systems140, 102715 (2026) https://doi.org/10.1016/j.is.2...

    2026

  3. [11]

    Information Processing & Management30(1), 9–19 (1994) https://doi.org/10

    Fox, C., Levitin, A., Redman, T.: The notion of data and its quality dimensions. Information Processing & Management30(1), 9–19 (1994) https://doi.org/10. 1016/0306-4573(94)90020-5

    1994

  4. [12]

    Industrial Manage- ment & Data Systems121(12), 2651–2671 (2021) https://doi.org/10.1108/ IMDS-12-2020-0756

    Haug, A.: Understanding the differences across data quality classifications: A literature review and guidelines for future research. Industrial Manage- ment & Data Systems121(12), 2651–2671 (2021) https://doi.org/10.1108/ IMDS-12-2020-0756

    2021

  5. [13]

    In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

    Kesper, A., Wenz, V., Taentzer, G.: Detecting quality problems in research data: A model-driven approach. In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems. MODELS ’20, pp. 354–364. Association for Computing Machine...

    2020

  6. [14]

    https://arxiv.org/abs/2504.01491

    Matoni, M., Kesper, A., Taentzer, G.: How to Define the Quality of Data? A Feature-Based Literature Survey (2025). https://arxiv.org/abs/2504.01491

    2025 arXiv

  7. [15]

    https: //arxiv.org/abs/2602.21249

    Matoni, M., Kesper, A., Taentzer, G.: Quality of Descriptive Information on Cultural Heritage Objects: Definition and Empirical Evaluation (2026). https: //arxiv.org/abs/2602.21249

    2026

  8. [16]

    Communications of the ACM39(11), 86–95 (1996) https://doi.org/10

    Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foun- dations. Communications of the ACM39(11), 86–95 (1996) https://doi.org/10. 1145/240455.240479

    1996

  9. [17]

    ACM Computing Surveys41(3), 1–52 (2009) https://doi.org/10.1145/1541880.1541883

    Batini, C., Cappiello, C., Francalanci, C., Maurino, A.: Methodologies for data quality assessment and improvement. ACM Computing Surveys41(3), 1–52 (2009) https://doi.org/10.1145/1541880.1541883

    2009

  10. [18]

    Data science journal14, 2–2 (2015) 34

    Cai, L., Zhu, Y.: The challenges of data quality and data quality assessment in the big data era. Data science journal14, 2–2 (2015) 34

    2015

  11. [19]

    Zenodo (2020)

    Kesper, A., Matoni, M., R¨ ossel, J., Weidling, M., Wenz, V.: Catalog of Qual- ity Problems for Data, Data Models and Data Transformations. Zenodo (2020). https://doi.org/10.5281/zenodo.3955500

    2020 doi

  12. [20]

    IEEE Data Eng

    Rahm, E., Do, H.H.: Data cleaning: Problems and current approaches. IEEE Data Eng. Bull.23(4), 3–13 (2000)

    2000

  13. [21]

    In: Wang, G., Tsuchiya, T., Xiang, D

    Laranjeiro, N., Soydemir, S.N., Bernardino, J.: A survey on data quality: Clas- sifying poor data. In: Wang, G., Tsuchiya, T., Xiang, D. (eds.) 2015 IEEE 21st Pacific Rim International Symposium on Dependable Computing (PRDC), pp. 179–188. IEEE Computer Society, Los Alamitos, ...

    2015 doi

  14. [22]

    In: Abramowicz, W., Tolksdorf, R

    F¨ urber, C., Hepp, M.: Using SPARQL and SPIN for data quality management on the semantic web. In: Abramowicz, W., Tolksdorf, R. (eds.) Business Infor- mation Systems, 13th International Conference, BIS 2010, Berlin, Germany, May 3-5, 2010. Proceedings. Lecture Notes in Busine...

    2010

  15. [23]

    In: 2nd Int

    Oliveira, P., Rodrigues, F., Henriques, P., Galhardas, H.: A taxonomy of data quality problems. In: 2nd Int. Workshop on Data and Information Quality, pp. 219–233 (2005)

    2005

  16. [24]

    Oliveira, P., Rodrigues, F., Rangel Henriques, P.: A formal definition of data quality problems. (2005)

    2005

  17. [25]

    Data Min

    Kim, W.Y., Choi, B., Hong, E.K., Kim, S., Lee, D.: A taxonomy of dirty data. Data Min. Knowl. Discov.7(1), 81–99 (2003) https://doi.org/10.1023/A: 1021564703268

    2003 doi

  18. [26]

    Introduction to Databases, pp. 1–39. Springer, Singapore (2023). https://doi.org/ 10.1007/978-981-19-3032-4 1 . Huawei Technologies Co., Ltd

    2023 doi

  19. [27]

    International Journal of Engineering Research & Technology1(6), 1–5 (2012)

    Jatana, N., Puri, S., Ahuja, M., Kathuria, I., Gosain, D.: A survey and comparison of relational and non-relational database. International Journal of Engineering Research & Technology1(6), 1–5 (2012)

    2012

  20. [28]

    Journal of Computer and Communications8(4), 59–71 (2020)

    Malik, A., Burney, A., Ahmed, F.: A comparative study of unstructured data with sql and no-sql database management systems. Journal of Computer and Communications8(4), 59–71 (2020)

    2020

  21. [29]

    In: International Conference on Internet Computing (1), pp

    Kappel, G., Kapsammer, E., Retschitzegger, W.: Xml and relational database systems-a comparison of concepts. In: International Conference on Internet Computing (1), pp. 199–205 (2001)

    2001

  22. [30]

    Asian Journal of Natural & Applied Science 4, 49–62 (2015)

    Kheder, M., Rahman, C., Jamal, S.: A comparison of concepts between native 35 xml and relational database systems. Asian Journal of Natural & Applied Science 4, 49–62 (2015)

    2015

  23. [31]

    Applied Sciences (2076-3417)14(21) (2024)

    Lupu, E., Olteanu, A., Ionita, A.D.: Concurrent access performance comparison between relational databases and graph nosql databases for complex algorithms. Applied Sciences (2076-3417)14(21) (2024)

  24. [32]

    International Standard ISO/IEC 39075:2024, ISO/IEC (2024)

    International Organization for Standardization (ISO) and International Elec- trotechnical Commission (IEC): Information technology — database languages — graph query language (gql). International Standard ISO/IEC 39075:2024, ISO/IEC (2024). Published April 2024. https://www.is...

    2024

  25. [33]

    Computer Science and Information Technology5(5), 149–153 (2017)

    Radoev, M.: A comparison between characteristics of nosql databases and tradi- tional databases. Computer Science and Information Technology5(5), 149–153 (2017)

    2017

  26. [34]

    Zenodo (2026)

    Kesper, A.: Constrainify: Template Collection. Zenodo (2026). https://doi.org/ 10.5281/zenodo.20718082

    2026 doi

  27. [35]

    Zenodo (2026)

    Matoni, M., Kesper, A.: Constrainify: Environment. Zenodo (2026). https://doi. org/10.5281/zenodo.20560540

    2026 doi

  28. [36]

    Zenodo (2026)

    Kesper, A., Wenz, V., Hofmann, L.S., Voß, J.: Project-KONDA/pattern-based- quality-analysis: Quality Pattern Model v1.5.0. Zenodo (2026). https://doi.org/ 10.5281/zenodo.20643230

    2026 doi

  29. [37]

    Kleppe, A.: Software language engineering: creating domain-specific languages using metamodels (2008)

    2008

  30. [38]

    Yan, C., Lin, Y., He, Y.: Predicate pushdown for data science pipelines. Proc. ACM Manag. Data1(2) (2023) https://doi.org/10.1145/3589281

    2023 doi

  31. [39]

    Zenodo (2026)

    Matoni, M., Kesper, A., Hofmann, L., Kir´ aly, P., Sch¨ afer, D., Voß, J., Taentzer, G.: Constrainify: Web Application to Specify and Analyze Data Quality. Zenodo (2026). https://doi.org/10.5281/zenodo.18861823

    2026 doi

  32. [40]

    Zenodo (2026)

    Sch¨ afer, D., Matoni, M., Kesper, A.: Constrainify: Constraint Collections f¨ ur die DDB. Zenodo (2026). https://doi.org/10.5281/zenodo.20303799

    2026 doi

  33. [41]

    Morgan Kaufmann, Cambridge, MA, USA (2017)

    Lazar, J., Feng, J., Hochheiser, H.: Research Methods in Human-Computer Inter- action, 2nd Edition. Morgan Kaufmann, Cambridge, MA, USA (2017). https: //www.sciencedirect.com/science/book/9780128053904

    2017

  34. [42]

    Zenodo (2026)

    Matoni, M.: Constrainify User Study: Reference Data. Zenodo (2026). https:// doi.org/10.5281/zenodo.19856977

    2026 doi

  35. [43]

    Zenodo (2026)

    Matoni, M.: Constrainify User Study: Questionnaire. Zenodo (2026). https://doi. 36 org/10.5281/zenodo.20400437

    2026 doi

  36. [44]

    Wohlin, C., Runeson, P., H¨ ost, M., Ohlsson, M.C., Regnell, B., Wessl´ en, A.,et al.: Experimentation in Software Engineering vol. 236. Springer, Berlin, Heidelberg, Germany (2012). https://link.springer.com/book/10.1007/978-3-662-69306-3

    2012 doi

  37. [45]

    CoRRabs/1405.3631(2014) https://doi.org/10.48550/ arXiv.1405.3631

    Ong, K.W., Papakonstantinou, Y., Vernoux, R.: The SQL++ semi-structured data model and query language: A capabilities survey of sql-on-hadoop, nosql and newsql databases. CoRRabs/1405.3631(2014) https://doi.org/10.48550/ arXiv.1405.3631

    2014 doi arXiv

  38. [46]

    In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp

    Nie, L., Cao, S., Shi, J., Sun, J., Tian, Q., Hou, L., Li, J., Zhai, J.: GraphQ IR: Unifying the semantic parsing of graph query languages with one intermediate representation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 5848...

    2022

  39. [47]

    In: Chung, C., Broder, A.Z., Shim, K., Suel, T

    Kontokostas, D., Westphal, P., Auer, S., Hellmann, S., Lehmann, J., Cornelissen, R., Zaveri, A.: Test-driven evaluation of linked data quality. In: Chung, C., Broder, A.Z., Shim, K., Suel, T. (eds.) 23rd International World Wide Web Conference, WWW ’14, Seoul, Republic of Kore...

    2014

  40. [48]

    In: Tuunainen, V.K., Rossi, M., Nandhakumar, J

    F¨ urber, C., Hepp, M.: Swiqa - a semantic web information quality assessment framework. In: Tuunainen, V.K., Rossi, M., Nandhakumar, J. (eds.) 19th Euro- pean Conference on Information Systems, ECIS 2011, Helsinki, Finland, June 9-11, 2011, p. 76 (2011). http://aisel.aisnet.o...

    2011

  41. [49]

    http://spinrdf.org/ (2009)

    Knublauch, H.: SPIN – SPARQL Inferencing Notation. http://spinrdf.org/ (2009)

    2009

  42. [50]

    Bizer, C., Cyganiak, R.: Quality-driven information filtering using the WIQA policy framework. J. Web Semant.7(1), 1–10 (2009) https://doi.org/10.1016/j. websem.2008.02.005

    2009 doi

  43. [51]

    In: Ganzha, M., Maciaszek, L.A., Paprzycki, M

    Oditis, I., Bicevskis, J., Bicevska, Z.: Domain-specific characteristics of data quality. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, Septe...

    2017 doi

  44. [52]

    In: Afrati, F.N., Kolaitis, P.G

    Abiteboul, S.: Querying semi-structured data. In: Afrati, F.N., Kolaitis, P.G. (eds.) Database Theory - ICDT ’97, 6th International Conference, Delphi, Greece, January 8-10, 1997, Proceedings. Lecture Notes in Computer Science, vol. 1186, pp. 1–18. Springer, Berlin, Germany (1...

    1997

  45. [53]

    Nikiforova, A., Bicevskis, J., Bicevska, Z., Oditis, I.: User-oriented approach to data quality evaluation. J. Univers. Comput. Sci.26(1), 107–126 (2020)

    2020

  46. [54]

    Journal of Ambient Intelligence and Humanized Computing9(4), 977–998 (2018) https: //doi.org/10.1007/s12652-017-0498-0

    Karkouch, A., Mousannif, H., Al Moatassime, H., Noel, T.: A model-driven framework for data quality management in the internet of things. Journal of Ambient Intelligence and Humanized Computing9(4), 977–998 (2018) https: //doi.org/10.1007/s12652-017-0498-0

    2018 doi

  47. [55]

    Computer Standards and Interfaces69, 103390 (2019) https: //doi.org/10.1016/j.csi.2019.103390

    J´ acome, S., Lara, J.: Totem: Reconciling multi-level modelling with standard two-level modelling. Computer Standards and Interfaces69, 103390 (2019) https: //doi.org/10.1016/j.csi.2019.103390

    2019

  48. [56]

    Soft- ware & Systems Modeling17(1), 269–294 (2018) https://doi.org/10.1007/ s10270-016-0520-6

    Igamberdiev, M., Grossmann, G., Selway, M., Stumptner, M.: An integrated multi-level modeling approach for industrial-scale data interoperability. Soft- ware & Systems Modeling17(1), 269–294 (2018) https://doi.org/10.1007/ s10270-016-0520-6

    2018

  49. [57]

    The Computer Journal57(1), 36–58 (2012) https://doi.org/10.1093/comjnl/bxs144

    Lara, J., Guerra, E., Cobos, R., Moreno-Llorena, J.: Extending deep meta- modelling for practical model-driven engineering. The Computer Journal57(1), 36–58 (2012) https://doi.org/10.1093/comjnl/bxs144

    2012 doi

  50. [58]

    ACM Trans

    Lara, J.D., Guerra, E., Cuadrado, J.S.: When and how to use multilevel mod- elling. ACM Trans. Softw. Eng. Methodol.24(2) (2014) https://doi.org/10.1145/ 2685615

    2014

  51. [59]

    In: Krogstie, J., Reijers, H.A

    Balaban, M., Khitron, I., Kifer, M., Maraee, A.: Formal executable theory of multilevel modeling. In: Krogstie, J., Reijers, H.A. (eds.) Advanced Information Systems Engineering, pp. 391–406. Springer, Cham, Switzerland (2018)

    2018

  52. [60]

    Technical report, Biodiversity Information Standards (TDWG) (2005)

    Biological Collections Data Task Group, A.: Access to biological collection data (abcd). Technical report, Biodiversity Information Standards (TDWG) (2005). TDWG Standard. http://www.tdwg.org/standards/115 38

    2005

Pith tools

Reviewed July 31, 2026 · model on record in the stance chip above.