1 citations · 1 across the 17 of their papers we have counts for
18 papers
A Computer-Assisted Proof of Speed Monotonicity for the Biased Random Walk on a Galton-Watson Tree Beyond the Known Range
Madhulatha Mandarapu, Sandeep Kunkunuru
The speed v(lambda) of the lambda-biased random walk on a supercritical Galton-Watson tree without leaves is conjectured to be nonincreasing on [0,m), where m is the mean offspring…
Same Pattern, Different Answer: A Reference Semantics and Divergence Map for GQL and SQL/PGQ Path Patterns
Madhulatha Mandarapu, Sandeep Kunkunuru
GQL (ISO/IEC 39075:2024) is the first international standard for a graph query language, and SQL/PGQ (ISO/IEC 9075-16:2023) embeds the same pattern-matching core, GPML, in SQL. Bot…
Bounded, Indeterminate, or a Bug: A Condition-Aware Oracle for Differential Testing of SQL Aggregates
Madhulatha Mandarapu, Sandeep Kunkunuru
Differential database testing compares results across engines and calls a discrepancy a bug. For floating-point aggregates this is unsound: engines legitimately disagree because fl…
Removable and Irreducible: A Token-Cost Ledger for the Multilingual Tokenization Tax
Madhulatha Mandarapu, Sandeep Kunkunuru
Large language models pay a well-documented tax on non-English text: the same content costs several times more tokens, and because attention is quadratic in sequence length, far mo…
The Decode-Work Law: Margin-Governed, Provably-Exact Spatial Joins over Compressed Geometry
Madhulatha Mandarapu, Sandeep Kunkunuru
Filter-and-refine spatial joins have always avoided touching exact geometry for certified candidate pairs, but the field never modeled the decompression cost of the pairs that surv…
When to Repair a Graph ANN Index: A Matched-Budget Negative Result, and the Interpolated-Baseline Trap That Hid It
Madhulatha Mandarapu, Sandeep Kunkunuru
Graph approximate-nearest-neighbor (ANN) indexes (HNSW, DiskANN/Vamana) lose recall under insert/delete churn, because deletions orphan the greedy-search paths that route through r…
Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations
Madhulatha Mandarapu, Sandeep Kunkunuru
LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes that GPT-4 agents achieve 65%…
One Index for Subsumption and Roll-up across Time, Geography, and Ontology
Madhulatha Mandarapu, Sandeep Kunkunuru
Time-series, geospatial, and ontology systems each maintain a hierarchy -- day <= month <= year, zip <= city, is-a / part-of -- and each indexes it in a separate silo. We observe t…
Knowledge-Graph Grounding Helps LLMs Only for Out-of-Training Knowledge: A Controlled Study on Clinical Question Answering
Madhulatha Mandarapu, Sandeep Kunkunuru
A recent Nature Medicine study reports that general-purpose frontier LLMs outperform specialized retrieval-augmented clinical tools on medical benchmarks, and that retrieval can hu…
When Is a Columnar Scan Bandwidth-Bound? A Decode-Throughput Law and Its Cross-Hardware Validation
Madhulatha Mandarapu, Sandeep Kunkunuru
A columnar scan that decompresses, filters, and aggregates should be limited only by memory bandwidth (the roofline floor T >= BytesRead/beta), yet real kernels are often compute-b…
Caching for Dollars, Not Hits: An Exact Offline Reference for Cloud-Egress Caching and the Crossover That Decides When It Pays
Madhulatha Mandarapu, Sandeep Kunkunuru
When a cache miss fetches from cloud object storage, the bill is per GET request and per byte of egress, not latency. Classic caching minimizes the miss rate, the wrong objective:…
Group Commit Self-Clocks: Why Tuning Is Unnecessary Above a Device-Set Load Threshold
Madhulatha Mandarapu, Sandeep Kunkunuru
Group commit amortizes the fixed cost of a durable log flush across many committing transactions; the release rule - a timer, a batch size, or an adaptive policy - is a classic tun…
The Value of Adaptivity in LSM Bloom-Filter Tuning: A Log-Law and a Two-Clock Frontier
Madhulatha Mandarapu, Sandeep Kunkunuru
Log-structured merge (LSM) trees attach an approximate-membership filter to every run and must split a fixed memory budget across them. The static optimum is known (Monkey); a larg…
Filtered ANN as a Phase Transition: When Selectivity-Estimation Error Causes Plan Regret
Madhulatha Mandarapu, Sandeep Kunkunuru
A filtered approximate-nearest-neighbor (ANN) query returns the k nearest vectors among those satisfying an attribute predicate P of selectivity s. The best execution strategy -- p…
When Does q-error Predict Plan Regret? Three Regimes of Cardinality-Estimation Error
Madhulatha Mandarapu, Sandeep Kunkunuru
Cardinality-estimation (CE) research ranks estimators by q-error, yet it is well known that q-error is an imperfect proxy for query-plan quality. We give a measurement-driven accou…
Graph-Grounded Optimization: Rao-Family Metaheuristics, Classical OR, and SLM-Driven Formulation over Knowledge Graphs
Madhulatha Mandarapu, Sandeep Kunkunuru
We propose graph-grounded optimization: a paradigm in which the decision variables, constraints, and objective coefficients of a real-world optimization problem are sourced from a…
Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
Madhulatha Mandarapu, Sandeep Kunkunuru
Biomedical knowledge is fragmented across siloed databases -- Reactome for pathways, STRING for protein interactions, ClinicalTrials.gov for study registries, DrugBank for drug voc…
Samyama: A Unified Graph-Vector Database with In-Database Optimization, Agentic Enrichment, and Hardware Acceleration
Madhulatha Mandarapu, Sandeep Kunkunuru
Modern data architectures fragment across graph databases, vector stores, analytics engines and optimization solvers, forcing ETL between them. We present Samyama, a graph-vector d…