{"id":"416ec458-ce74-45d3-ab6f-a8b30a087041","arxiv_id":"2507.19154","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":2.0,"correctness_risk":"low","formal_verification":"none","parameter_count":0,"one_line_summary":"A narrative review of big data storage and integration practices in energy systems, identifying scalability, real-time, and regulatory gaps and recommending P2P and data-space architectures.","lead":"This paper surveys how energy companies store and combine large data streams, from classic databases to NoSQL, cloud, blockchain, and peer-to-peer systems. It reviews known limits of each approach and offers recommendations for more open, regulated data sharing in the energy sector.","discovery_kind":"review","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The review's negative gap claims (e.g., 'none available for energy systems' for P2P data management) are an artifact of a keyword search that never queried for P2P/DHT terms, so the corpus cannot support them.","rationale":"The reader identified the representativeness of the 53-article corpus as the weakest assumption. My stress-test pass sharpens this into a concrete, testable failure: the corpus was assembled with keywords that cannot retrieve the very technologies the paper claims are absent. The paper's negative assertions about P2P/DHT availability in energy systems are load-bearing because they motivate the paper's most distinctive recommendation, the DHT-based DERA extension. The error is methodological rather than internal inconsistency, and it is correctable by targeted re-searching. The survey still has value as an orientation document: its comparative tables, background on DERA and data spaces, and synthesis of storage and integration limitations are useful, and the paper transparently describes its rapid-review method. However, the strongest claims about missing technologies should be tempered or verified before the paper is treated as a reliable guidebook. This does not change the conditional verdict: the paper should be accepted only with a revised search strategy, explicit acknowledgment of search-term limitations, and qualified negative statements. I agree with the reader that the burden is on the evidence base, not on the qualitative synthesis content.","tokens_in":40098,"tokens_out":2022,"duration_ms":21166,"concrete_test":"Run a targeted literature search in Web of Science, Scopus, and IEEE Xplore covering 2016–2025 using the paper's original terms plus technology-specific queries, e.g., ('peer-to-peer' OR 'P2P' OR 'distributed hash table' OR 'DHT' OR 'structured overlay') AND ('smart grid' OR 'energy data' OR 'power system' OR 'virtual power plant' OR 'energy trading') AND ('data management' OR 'data storage' OR 'data integration'), and separately ('query processing' OR 'data integration') AND ('energy' OR 'smart grid' OR 'power system'). If any retrieved study reports an energy-sector P2P/DHT data management implementation or a query-processing data integration approach, then the paper's 'none available' and 'we did not find' statements are false, and Table 3 and §4.2 must be revised accordingly.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central value is identifying gaps in big data storage and integration for energy systems, and several strong negative claims drive its recommendations. In §1.2 the authors list the search keywords used: 'big data,' 'big data management,' 'big data management in energy systems,' 'big data storage,' 'big data storage in energy systems,' 'big data integration,' 'large-scale data management,' and 'large-scale data integration.' Notably absent are 'peer-to-peer,' 'P2P,' 'distributed hash table,' 'DHT,' 'structured overlay,' 'blockchain,' 'NoSQL,' and 'data space.' Yet §3.4.5 states that 'the implementations of P2P data management are scarce, and based on the current search, none are available for energy systems,' and Table 3 lists 'No case' for P2P data management in energy systems. This is not an evidence-based conclusion about the literature; it is the expected null result of failing to search for the technology under review. The same concern applies to §3.5.2's strong statement that 'we did not find approaches that support query processing while addressing efficient data integration challenges.' A single relevant energy-sector P2P/DHT or query-processing-integration study would invalidate these claims, and because the search strategy guarantees their absence, the claims cannot be considered load-bearing evidence. The review's proposed DHT-based integration architecture (§4.2) is therefore grounded in a gap that may not exist.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":40352,"tokens_out":6863,"duration_ms":65457,"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":[{"comment":"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.","section":"§1.2, §3.4.5, Table 3"},{"comment":"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.","section":"§1.2, Figure 2"},{"comment":"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.","section":"§4.2, §4.3, Figure 10"}],"minor_comments":[{"comment":"The title contains a typo, 'Associate d Challenges,' which should read 'Associated Challenges.'","section":"Title/Abstract"},{"comment":"The phrase 'communication latency„ time costs' contains a stray character after 'latency' and should be cleaned up.","section":"§3.2.1"},{"comment":"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.","section":"§1.2"},{"comment":"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.","section":"Table 3"}],"recommendation":"major_revision","confidential_remarks":"The paper is within the journal's scope as a survey, but the current version's gap claims exceed the evidence base. I see this as fixable: the authors can add targeted searches, narrow the absence claims, and enumerate the corpus. If they decline to do so, the claims in §3.4.5, §3.5.2, and Table 3 should be downgraded to observations about the sampled articles, and the 'No case' entries removed."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a useful narrative review of big data storage and integration in energy systems, and the authors deserve credit for assembling a clear map of a scattered literature. The storage-by-technology summaries, the regulatory material on DERA, IDSA, and OFGEM, and Table 3's comparison of approaches are all genuinely handy for practitioners and for researchers entering the area. The paper does not present a new mechanism or result, but it does identify a real gap in prior reviews: most earlier surveys stop at storage or analytics and do not treat integration and regulatory compliance together. That synthesis is the paper's actual contribution, and it is reasonable.\n\nThe soft spots are real but not fatal to the survey as a whole. The biggest one is the one the stress-test note flags, and it lands on reading the paper. The keyword list in §1.2 never includes 'peer-to-peer', 'P2P', 'distributed hash table', 'DHT', 'blockchain', 'NoSQL', or 'data space'. Yet §3.4.5 says P2P data management implementations are 'scarce, and based on the current search, none are available for energy systems', and Table 3 lists 'No case' for P2P in energy systems. That is not an evidence-based gap; it is the null result of a search that did not query for the technology. The same overreach appears in §3.5.2's claim that no approaches support query processing while addressing efficient data integration. A single relevant energy-sector P2P/DHT or query-integration study would invalidate these statements, and the search as described cannot find one. The proposed DHT-based DERA extension in §4.2 is therefore built on ground that may not exist.\n\nOther concerns are minor. Fifty-three articles selected by title and abstract screening is a thin base for a survey that makes comparative and scarcity claims, though the authors do state they are doing a rapid scoping review rather than a full systematic one. The recommendation sections are qualitative and untested, which is fine for a survey but should be labeled as directions, not solutions. The citation pattern is honest: the authors' own prior work appears only as domain context and is not load-bearing.\n\nWho is this for? Practitioners and new researchers who want a broad, readable orientation to storage, integration, and regulatory frameworks for energy data. It is not a field-reshaping technical contribution. It deserves a serious referee, but the revision should either expand the search to cover P2P/DHT terms or soften the negative claims to match what the search can actually support.","headline":"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.","tokens_in":40871,"tokens_out":2292,"would_cite":true,"duration_ms":24535,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Energy big data is stored well but integrated poorly, review finds","keywords":["big data management","energy systems","data storage","data integration","NoSQL","Hadoop","blockchain","peer-to-peer data management"],"falsifier":"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.","tokens_in":1474,"feed_emoji":"⚡","tokens_out":1754,"duration_ms":75091,"temperature":0.7,"pith_summary":"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.","feed_headline":"Energy big data is stored well but integrated poorly, review finds","feed_subtitle":"A 53-article gap analysis points to hybrid storage and data-space governance as the path forward.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Earlier energy big-data survey that supplies the baseline of what previous reviews covered and the gaps in NoSQL, storage, and integration that this review extends.","marker":"[10]"},{"why":"Systematic review of NoSQL big-data storage tools that supplies the storage-technology catalogue and its cross-domain limitations.","marker":"[13]"},{"why":"Smart-grid big-data management architecture that documents the dominant Hadoop and cloud practices and their real-time limitations.","marker":"[32]"},{"why":"Big-data value chain roadmap for energy and transport that provides the organising lifecycle framework used throughout the review.","marker":"[39]"},{"why":"Permissioned-blockchain data management work that anchors the discussion of decentralised storage's scalability and confidentiality trade-offs.","marker":"[92]"},{"why":"European energy data-exchange reference architecture that forms the regulatory and interoperability layer of the recommendations.","marker":"[121]"},{"why":"Blueprint for a common European energy data space that provides evidence on data-space integration and governance challenges.","marker":"[132]"},{"why":"Data best-practice guidance that supplies the nine data-management principles used for regulatory compliance discussion.","marker":"[133]"}],"fun_headline_variants":["Energy big data: storage yes, integration no","Big data in energy lacks real-time integration","Energy data: 53 studies show integration gap","P2P data management unexplored in energy systems","Energy big data integration is the missing link"],"cache_read_input_tokens":43008,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["Energy big data: storage yes, integration no","Big data in energy lacks real-time integration","Energy data: 53 studies show integration gap","P2P data management unexplored in energy systems","Energy big data integration is the missing link"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000187,"raw_usage":{"total_tokens":1294,"prompt_tokens":875,"completion_tokens":419,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":491,"completion_tokens_details":{"reasoning_tokens":349}},"tokens_in":491,"tokens_out":419,"duration_ms":4072,"temperature":1.0,"reasoning_tokens":349,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T17:58:57.586241+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Smart-grid big-data management architecture that documents the dominant Hadoop and cloud practices and their real-time limitations."},{"cited_title":"Xiang, Large scale graph data processing technology on cloud computing environments, in: 2023 International Conference on Networking, Informatics and C omputing (ICNETIC), 2023, pp","cited_arxiv_id":null,"evidence_quote":"Permissioned-blockchain data management work that anchors the discussion of decentralised storage's scalability and confidentiality trade-offs."},{"cited_title":"Turkmayali, N","cited_arxiv_id":null,"evidence_quote":"Data best-practice guidance that supplies the nine data-management principles used for regulatory compliance discussion."}],"review_version":2}