{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp09_archetype_breadth150_20260804","research_id":"eoa_inverse_innovation_exp09_light_prior_art_20260804","cell_id":"predictive_residual_processing__computer_science","search_lanes":{"direct_problem_and_intervention":{"queries":["distributed tracing expected trace residual reconstruct deviations compression","distributed trace compression reconstruct full traces common data synchronized service backend"],"source_ids":["SRC1","SRC2"],"no_result_note":null},"synonyms_and_historical_terms":{"queries":["distributed trace template delta encoding compression repetitive spans","microservice trace compression templates reconstruct traces redundancy research","distributed application log template control flow graph expected behavior deviations"],"source_ids":["SRC1","SRC4"],"no_result_note":null},"products_practices_and_standards":{"queries":["OpenTelemetry sampling head tail sampling official","OpenTelemetry tail sampling processor policy documentation"],"source_ids":["SRC2","SRC3"],"no_result_note":null},"component_combination":{"queries":["distributed tracing normal model structural anomaly expected graph deviations timing","distributed tracing model synchronization reconstruction anomaly full trace fallback","trace storage deduplication distributed tracing repeated spans product"],"source_ids":["SRC1","SRC3","SRC4"],"no_result_note":null}},"sources":[{"source_id":"SRC1","title":"Tracezip: Efficient Distributed Tracing via Trace Compression","publisher":"Chen, Pu, and Zheng via arXiv","url":"https://arxiv.org/abs/2502.06318","source_type":"PRIMARY_RESEARCH","claims_supported":["Distributed traces contain significant redundancy from repeatedly transmitted identical data.","Tracezip maintains a service-side Span Retrieval Tree, sends lightweight span representations, and reconstructs complete traces at the backend.","Its differential-update mechanism synchronizes the shared retrieval structure between services and backend, closely matching the proposal's reconstructive shared-state mechanism."]},{"source_id":"SRC2","title":"Sampling","publisher":"OpenTelemetry","url":"https://opentelemetry.io/docs/concepts/sampling/","source_type":"OFFICIAL_GUIDANCE","claims_supported":["High-volume tracing commonly uses sampling to balance data volume and usefulness.","Head sampling cannot use the completed trace and can therefore miss errors or other critical information.","Tail sampling uses most or all spans but is stateful, operationally costly, and can itself lose information under resource pressure.","Regulatory requirements can make dropping trace data inappropriate."]},{"source_id":"SRC3","title":"Tail Sampling Processor","publisher":"OpenTelemetry Collector Contrib","url":"https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/processor/tailsamplingprocessor/README.md","source_type":"FIRST_PARTY_PRODUCT","claims_supported":["The official collector implements policy-based sampling using latency, status, attributes, span count, rate limits, byte limits, and composite rules.","It supports a force-sample escape hatch and requires complete trace colocation for effective decisions.","Resource limits and premature eviction can cause traces to be dropped before sampling."]},{"source_id":"SRC4","title":"Anomaly detection using program control flow graph mining from execution logs","publisher":"IBM Research","url":"https://research.ibm.com/publications/anomaly-detection-using-program-control-flow-graph-mining-from-execution-logs","source_type":"PRIMARY_RESEARCH","claims_supported":["Earlier work mined templates and template sequences from distributed-application logs into a control-flow graph representing healthy baseline behavior.","Observed deviations from the expected graph were used to flag runtime anomalies.","This establishes older adjacent practice for model-relative structural deviation detection across distributed components."]}],"problem_evidence":{"status":"SUPPORTED","finding":"Official OpenTelemetry guidance documents the volume-versus-usefulness tradeoff, head sampling's inability to preserve all error-bearing traces, tail sampling's resource costs, and the possibility of premature loss. Tracezip independently reports substantial repeated trace content and targets the same collection-overhead problem through reconstructable compression.","source_ids":["SRC1","SRC2","SRC3"]},"closest_prior_art":[{"name":"Tracezip","source_ids":["SRC1"],"overlap":"It exploits repetitive distributed traces, keeps shared reconstructive state at services and backend, transmits lightweight representations instead of repeated span data, synchronizes that state through differential updates, and reconstructs complete traces at the backend. This collides with the proposal's central expected-state-plus-residual transport mechanism.","remaining_difference":"The proposal gives the shared state explicit request-class and deployment-version semantics, prioritizes structural, outcome, missingness, and timing deviations by consequence, and adds independent raw audits, a correction replay buffer, protected classes, and validity-triggered full export. The retained Tracezip description does not establish that complete governed anomaly-and-audit loop."},{"name":"Baseline control-flow-graph anomaly detection","source_ids":["SRC4"],"overlap":"It learns normal distributed execution structure and reports deviations from that expected structure, covering the proposal's prediction/comparison layer under older log-mining terminology.","remaining_difference":"It detects anomalies from collected logs rather than using deviations as a reversible telemetry encoding synchronized with a collector."},{"name":"OpenTelemetry tail sampling with escape-hatch policies","source_ids":["SRC2","SRC3"],"overlap":"It selects completed traces using error, latency, attribute, rate, and composite policies and can force retention for designated traces.","remaining_difference":"It makes keep-or-drop decisions rather than reconstructing suppressed expected structure from shared model state and residuals."}],"prior_art_disposition":"SUBSTANTIAL_COLLISION","contrastive_claim_remaining":"Compared with Tracezip's redundancy-oriented synchronized compression, an explicitly deployment-versioned semantic trace model plus consequence-weighted structural, outcome, missingness, and timing residuals, independent random full-trace audits, replay-based model correction, and validity-triggered full export will improve predefined diagnostic-question accuracy at an equal fully accounted telemetry-byte budget. This is a narrow system-level contrast, not a claim that reconstructable residual tracing itself is novel.","contrastive_claim_falsifier":"The contrast fails if Tracezip or another earlier system already contains the same semantic anomaly residuals, independent audit channel, correction replay, and model-validity fallback combination, or if held-out replay shows no diagnostic-accuracy advantage after accounting for residuals, model distribution, synchronization, audits, and full-trace fallbacks.","gates":{"adequate_source_search":{"status":"PASS","rationale":"The bounded search covered direct formulations, compression and delta/template terminology, official sampling practice, a first-party processor, older distributed-log baseline modeling, and combinations of reconstruction, synchronization, and anomaly detection. Four opened sources from at least three publisher groups were retained.","source_ids":["SRC1","SRC2","SRC3","SRC4"]},"supported_problem":{"status":"PASS","rationale":"Primary research and official guidance directly support trace redundancy, collection overhead, sampling tradeoffs, and the risk of missing diagnostically important information.","source_ids":["SRC1","SRC2","SRC3"]},"distinct_testable_claim":{"status":"PASS","rationale":"Although the core reconstructive transport substantially collides with Tracezip, the narrower governed semantic-residual combination can be distinguished experimentally by fully accounted byte cost and predefined diagnostic accuracy.","source_ids":["SRC1","SRC3","SRC4"]},"bounded_next_test":{"status":"PASS","rationale":"An offline replay of 500 preserved traces across two frozen deployment versions is limited, reversible, and capable of comparing reconstruction accuracy, blind spots, overhead, and fallback frequency without changing production instrumentation or retention.","source_ids":["SRC1","SRC2","SRC3"]},"no_obvious_safety_or_authority_stop":{"status":"PASS","rationale":"The access-controlled offline replay preserves originals and does not suppress live evidence. Production use would require service-owner and security/compliance approval, particularly where regulation or protected trace classes prohibit dropping data.","source_ids":["SRC2","SRC3"]}},"screen_survival":false,"world_novelty_boundary":"This four-source screen cannot establish world novelty, patentability, market size, expert acceptance, or realized value. It found a direct reconstructable and synchronized distributed-trace compression system, established sampling and force-retention practice, and older model-relative anomaly detection. Only the narrower governed combination of semantic residual prioritization, independent raw auditing, correction replay, and model-validity fallback remains unresolved, and broader paper, patent, product, and historical searches could eliminate that distinction."}