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REVIEW 3 major objections 4 minor 225 references

Big Data Energy Systems: A Survey of Practices and Associated Challenges

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Energy big data is stored well but integrated poorly, review finds

desk verdict A competent orientation survey of big data storage and integration for energy systems, undercut by a search strategy that cannot support its headline negative claims about P2P and DHT systems. read the letter →

arxiv 2507.19154 v1 pith:XNAMVZYU submitted 2025-07-25 cs.DB cs.DC

Lunodzo J. Mwinuka , Massimo Cafaro , Lucas Pereira , Hugo Morais This is my paper · ORCID
classification cs.DBcs.DC
keywords bigdatamanagementenergysystemsstorageintegrationNoSQLHadoopblockchainpeer-to-peer
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

This review of 53 papers from 2016–2025 argues that energy-system big data management is held back less by a shortage of storage options than by mismatches: each technology solves one problem (consistency, flexibility, scale, decentralisation, elasticity) while creating another (scalability, weak consistency, latency, limited integration, vendor lock-in). It maps these trade-offs onto the big-data value chain and finds that the least-developed links are real-time integration, cross-system data sharing, and regulatory-compliant architecture. If the mapping is right, no single incumbent platform can carry the energy sector, and hybrid designs that combine NoSQL or existing DBMSs with peer-to-peer overlays and data-space style governance are the recommended path.

What carries the argument

The review organises everything around the big-data value chain (acquisition, analysis and processing, storage and curation, services and visualisation) and a comparison table of storage technologies. This value-chain lens is the device that turns a loose set of 53 papers into a gap map: it shows where the chain breaks (integration and real-time access) and which storage options are already in use or missing in energy systems. A data-exchange reference architecture acts as the companion device that pins regulatory and interoperability requirements onto the technical comparison.

What would settle it

Repeat the same 12,290-hit search with additional academic databases and screen for energy-sector P2P or real-time data-integration systems published before 2025; if several such papers meet the inclusion criteria, the claim that these are unaddressed gaps collapses. A simpler check is to identify any utility or grid operator running a DHT-based or fully decentralised data-management layer in production.

Watch

Extended reading notes

Core claim

The paper's central discovery is a mapping, not a measurement: across 53 selected articles from 2016 to 2025, energy-system big data management concentrates on analytics and on a handful of storage technologies (relational databases, NoSQL, Hadoop, cloud, some blockchain), while data integration is comparatively thin and real-time integration is essentially absent. Each storage family has a characteristic bottleneck: relational systems scale poorly, NoSQL gives up consistency, Hadoop is batch-oriented and centralised, blockchain is slow and resource-hungry, and cloud creates vendor lock-in and data fragmentation. No current approach simultaneously delivers scalability, consistency, real-time response, and regulatory compliance, so the authors position peer-to-peer data management with distributed hash tables as the nearest unexploited path to decentralised, scalable, regulatory-friendly data management, noting that their search found no P2P data-management implementation in energy systems.

Load-bearing premise

The review's conclusion that P2P data management, real-time integration, and several related mechanisms are missing from energy systems rests on the assumption that its 53-article sample represents the whole field; if relevant work was missed by the title-and-abstract screening, the gap analysis would be wrong.

Editorial extensions

If this is right

  • No single incumbent storage technology meets the energy sector's needs, so hybrid storage pairing NoSQL or DBMSs with peer-to-peer overlays is forwarded as a balanced route to scalability, security, and performance.
  • Distributed hash tables are identified as a near-unexplored integration mechanism that would let stakeholders keep their own DBMSs while querying datasets from other systems that share a common architecture.
  • Adopting data-space style governance and reference architectures would help energy stakeholders meet FAIR principles and regulatory data-sharing obligations.
  • The absence of P2P data-management implementations in energy systems is treated as a research gap worth filling, not as evidence that the approach is infeasible.
  • Real-time data integration remains unsupported by traditional ETL/ELT processes, pushing future designs toward edge computing and streaming technologies.

Reading between the lines

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

  • Extension the paper leaves untested: hybrid NoSQL/DBMS-plus-P2P designs would need to beat cloud-only storage on measured latency, cost, and consistency in real smart-meter workloads before that recommendation becomes actionable.
  • Inference from the gap analysis: the negative finding that no P2P data-management system exists for energy systems may partly reflect the screening and publication record rather than a true absence, so a grey-literature or industry-case search could change the conclusion.
  • Testable extension: the paper's emphasis on governance implies that data-sharing success in energy depends at least as much on trust agreements, naming standards, and regulatory alignment as on any storage technology, and that prioritisation can be checked by comparing pilot projects that adopt data-space governance against those that adopt only new storage engines.
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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

3 major / 4 minor

Summary. This paper is a rapid/narrative survey of big-data storage and integration practices in energy systems for the period 2016–2025. Starting from keyword searches across Web of Science, Google Scholar, IEEE Explorer, and journal websites, the authors reduce 12,290 hits to 53 articles and synthesize practices for relational databases, NoSQL, Hadoop, GFS, blockchain, P2P, and cloud storage, together with ETL/ELT, data fusion, and data-space integration approaches. The paper also reviews European regulatory and reference frameworks (DERA, IDS-RAM, OFGEM, DESAP) and concludes with recommendations, including a P2P/DHT-enhanced DERA architecture. The stated contribution is a gap analysis identifying weaknesses in scalability, integration, real-time processing, and regulatory compliance, intended as a guidebook for deploying big data technologies in energy systems.

Significance. If the gap analysis were reliable, the survey would be a useful orientation for practitioners and researchers in energy informatics, particularly for its synthesis of NoSQL/Hadoop/cloud limitations and its connection of data-management practice to European reference architectures and data-space initiatives. The paper gives a fair, balanced account of the trade-offs of each storage family and summarizes recent regulatory framework material (DERA 2.0/3.0, IDS-RAM, OFGEM). At the same time, the manuscript offers no machine-checked artifacts and its central claims are descriptive; the value of the survey depends on whether the 53-article corpus can support the strong absence claims that drive its recommendations.

major comments (3)
  1. [§1.2, §3.4.5, Table 3] The search protocol in §1.2 lists eight keyword phrases, all variants of 'big data' and 'large-scale data'; none include 'peer-to-peer,' 'P2P,' 'distributed hash table,' 'DHT,' 'structured overlay,' 'blockchain,' or 'NoSQL.' This makes the strong negative claims in §3.4.5 — that 'the implementations of P2P data management are scarce, and based on the current search, none are available for energy systems' — and Table 3's 'No case' entry for P2P Data Management an expected artifact of the query design rather than an evidence-based conclusion about the literature. The same problem afflicts §3.5.2, where 'we did not find approaches that support query processing while addressing efficient data integration challenges' is presented as a general finding. Because the P2P/DHT recommendations in §4.1–§4.3 are explicitly motivated by these gaps, the gap analysis needs to be re-run with technology-specific search terms, or all absence claims must be restricted to the reviewed corpus and the 'No case' entries must be removed.
  2. [§1.2, Figure 2] The screening pipeline is reported as 12,290 → 298 → 53, but the 53 included articles are never enumerated, and the inclusion/exclusion criteria are summarized only by the diagram in Figure 2. This makes the corpus non-auditable, which is especially consequential for negative claims: a single relevant energy-sector P2P/DHT or query-processing paper would overturn the claimed gaps, yet the reader cannot determine whether such a paper was considered. I recommend providing a supplementary list of the 53 articles (with references and screening decisions), or at minimum a transparent flow table, so that the representativeness of the evidence base can be checked.
  3. [§4.2, §4.3, Figure 10] The proposed DERA extension in §4.2 and §4.3 is presented as filling a gap: 'With DHT platforms, stakeholders would benefit from their existing DBMSs whilst having the advantage of easily accessing datasets from other systems,' and Figure 10 integrates P2P/DHT components into the reference architecture. The novelty of this proposal rests on the asserted absence of P2P data management in energy systems in §3.4.5, which, for the reasons above, the search cannot establish. The architectural idea may be reasonable, but it currently functions as a gap-filling contribution; it should be reframed as an open design direction or justified from the intrinsic properties of P2P systems (autonomy, scalability, fault tolerance), independent of an unsupported absence claim.
minor comments (4)
  1. [Title/Abstract] The title contains a typo, 'Associate d Challenges,' which should read 'Associated Challenges.'
  2. [§3.2.1] The phrase 'communication latency„ time costs' contains a stray character after 'latency' and should be cleaned up.
  3. [§1.2] The methods paragraph does not state the exact date the searches were run, and Figure 2 is schematic; adding the search date and the basis for the 298-article reduction would improve reproducibility.
  4. [Table 3] The row 'Distributed File Systems, i.e., GFS' lists 'Non-existent' for energy applications, while the text in §3.4.6 says 'To the best of the knowledge established in this work, the implementation of GFS in energy systems is non-existent'; the table should carry the same hedge.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the survey's claims are descriptive syntheses of an explicitly qualified corpus, not reductions to fitted inputs or load-bearing self-citations.

full rationale

This paper is a narrative/rapid literature review, not a derivation. Its central claims—that big-data storage and integration approaches have characteristic limitations and that certain technologies are underexplored in energy systems—are supported by the 53-paper corpus and by cited external literature, not by any fitted parameter or by construction. The strongest negative claims are explicitly qualified: §3.4.5 says P2P implementations are 'scarce, and based on the current search, none are available for energy systems,' and §3.5.2 says 'In our review, we did not find approaches that support query processing while addressing efficient data integration challenges.' These are transparent statements about the sample obtained with the listed keywords, not predictions derived from that sample, so the skeptic's search-coverage objection is a correctness/coverage limitation rather than a circularity. The author group's own prior works ([66], [70], [83]) are cited as domain examples and background; none is used as a load-bearing uniqueness theorem, ansatz, or fitted input, and they are externally available published work. No self-definitional relationship, renamed known result, or imported-uniqueness pattern appears. The survey therefore has no circular derivation chain.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper is a review, so it introduces no free parameters or invented entities. The conclusions rest on three premises: the 53-article sample is representative; energy data is big data requiring new management; and the recommended P2P and data-space architectures will transfer benefits to energy systems. The first is documented but not audited; the third is an untested design assumption.

assumptions (4)
  • domain assumption The 53 selected articles are representative of the state of the art in big data management for energy systems.
    The review's trends and gap claims in Sections 3 and 4 generalize from this sample selected by title and abstract screening in Section 1.2. A biased sample would invalidate the conclusions.
  • domain assumption Energy system data volumes and velocities exceed the capacity of traditional relational management.
    Stated in Sections 2.3 and 3.2.1 with citations; it motivates the entire big-data framing of the survey.
  • domain assumption DERA and IDSA data space frameworks are valid models for European energy data exchange.
    The recommendations in Section 4.3 adopt these reference architectures as authoritative without independent validation in this paper.
  • ad hoc to paper Combining P2P overlays with DHTs inside a DERA-style architecture will improve scalability, security, and performance in energy systems.
    Proposed in Sections 4.1 and 4.3 and illustrated in Figure 10; no implementation, simulation, or measurement supports this claim.

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Cite this review

Pith. "Pith review of Big Data Energy Systems: A Survey of Practices and Associated Challenges." pith.science (2026). https://pith.science/paper/XNAMVZYU

@misc{pith2026250719154,
  author       = {Pith},
  title        = {Pith review of: Big Data Energy Systems: A Survey of Practices and Associated Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XNAMVZYU}},
  note         = {Machine review of arXiv:2507.19154}
}
read the original abstract

Energy systems generate vast amounts of data in extremely short time intervals, creating challenges for efficient data management. Traditional data management methods often struggle with scalability and accessibility, limiting their usefulness. More advanced solutions, such as NoSQL databases and cloud-based platforms, have been adopted to address these issues. Still, even these advanced solutions can encounter bottlenecks, which can impact the efficiency of data storage, retrieval, and analysis. This review paper explores the research trends in big data management for energy systems, highlighting the practices, opportunities and challenges. Also, the data regulatory demands are highlighted using chosen reference architectures. The review, in particular, explores the limitations of current storage and data integration solutions and examines how new technologies are applied to the energy sector. Novel insights into emerging technologies, including data spaces, various data management architectures, peer-to-peer data management, and blockchains, are provided, along with practical recommendations for achieving enhanced data sharing and regulatory compliance.

Figures

Figures reproduced from arXiv: 2507.19154 by the authors.

Figure 1
Figure 1. Data sources in smart grids [7]. Unfortunately, the majority of the generated data remains unused, thereby losing its potential. For instance, only 2% to 4% of smart meter-generated data is being used to enhance the efficiency of grid operation [2, 8]. Further￾more, the efficient utilisation of generated data is not realised due to challenges in the interoperability of multiple data management layers, as well as dif… view at source ↗
Figure 2
Figure 2. Study selection criteria. filter sources, Google Scholar, IEEE Explorer, and journals’ websites for extracting actual files. This method was chosen for its efficiency in synthesising a large body of literature within a limited time frame, making it particularly suited for identifying emerging trends and current gaps. Relevant keywords that reflect the anticipated outcomes of this review were selected. Without a spec… view at source ↗
Figure 3
Figure 3. A presentation of 10 Vs of Big Data. Vulnerability, and Visualisation as important aspects to consider in defining big data [42]. This makes the definition of big data a relative concept rather than an absolute definition [7]. A descriptive summary of the most common Vs is presented in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Big data workflow (customised from [39]). [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Big data value chain (customised from [39]). [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: European Data Exchange Reference Architecture (D [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: DERA 3.0 layered architecture and link to the DESAP [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Data warehouse approach. relational databases and other storage architectures [138]. In the context of renewable energy monitoring, Trillo-Montero et al. [139] implemented an orderly, accessible, fast, and space-saving storage system that allows transferring to an RDBM…
Figure 9
Figure 9. Figure 9: Classification of Data Management Applications in [PITH_FULL_IMAGE:figures/full_fig_p024_9.png]
Figure 10
Figure 10. Figure 10: Refined Data Exchange Reference Architecture. [PITH_FULL_IMAGE:figures/full_fig_p025_10.png]

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Pith tools

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