arXiv subjects

Nima Shahbazi

Publications and source records attributed to Nima Shahbazi.

13 recordsLinked to original sources

arXiv2026

From Textual Columns to Query Plans: A Unified Relational-Semantic Execution Framework for Hybrid Query Processing

Real-world table question answering often involves hybrid schemas in which some query-relevant information is explicit in relational columns, while other attributes, predicates, or join conditions are only implicit in free-form text. Existing systems struggle with this setting: Text-to-SQL methods scale to large and multi-table databases but require fully structured schemas, whereas direct LLM-based methods can interpret textual content but are costly and unreliable when applied to large databases. We present OmniTQA, a unified framework for semi-structured table question answering that treats semantic reasoning as a first-class operation within relational query execution. OmniTQA compiles natural-language questions into directed acyclic graphs of relational and LLM-based semantic operators. This enables ambiguity-aware plan diversification, cost-aware optimization, and dual-engine execution over structured and textual data. Across structured and semi-structured benchmarks, OmniTQA consistently improves performance in hybrid settings, outperforming the strongest baselines by 14 accuracy points on average and by 27 points on the most challenging subset, while maintaining competitive accuracy on fully structured datasets.

cs.DB↗

arXiv2026

GraphMind: From Operational Traces to Self-Evolving Workflow Automation

Complex operational workflows coordinating personnel, tools, and information are central to system operations, yet end-to-end automation remains challenging due to extensive human input requirements and limited ability to adapt over time. We present GraphMind, a system that constructs, executes, and evolves action-centric workflow graphs with minimal human effort. The system operates in three phases. First, a scalable offline pipeline extracts structured workflow graphs from large volumes of human resolution traces, capturing problems, actions, and their causal relationships. Second, an online multi-agent traversal engine navigates the graph to dynamically construct and execute workflows, combining graph-guided retrieval with LLM-driven reasoning at each step. Third, Adaptive Traversal Reinforcement (ATR) reinforces successful traversal paths, enabling execution-informed graph adaptation. GraphMind has been deployed across four production cloud database services for incident investigation. Evaluated on 93 held-out incidents and validated via blind expert review, the system outperforms an Agentic Summary-RAG baseline in mitigation reach, hallucination rate, and diagnostic throughput while requiring 8x less retrieval context. The ATR layer reduces hallucination rate by 26%, demonstrating that workflow graphs can learn from execution feedback. A 12-week field study confirms practical value: 97% of scored conversations yield actionable results within interactive latency.

cs.AI↗

arXiv2026

Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications

NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users express queries iteratively (2) questions often span multiple data sources beyond the closed-world assumption of a single database, and (3) queries frequently rely on commonsense or external knowledge. Consequently, satisfying realistic data needs require integrating heterogeneous sources, modalities, and contextual data. In this paper, we present Blue's Data Intelligence Layer (DIL) designed to support multi-source, multi-modal, and data-centric applications. Blue is a compound AI system that orchestrates agents and data for enterprise settings. DIL serves as the data intelligence layer for agentic data processing, to bridge the semantic gap between user intent and available information by unifying structured enterprise data, world knowledge accessible through LLMs, and personal context obtained through interaction. At the core of DIL is a data registry that stores metadata for diverse data sources and modalities to enable both native and natural language queries. DIL treats LLMs, the Web, and the User as source 'databases', each with their own query interface, elevating them to first-class data sources. DIL relies on data planners to transform user queries into executable query plans. These plans are declarative abstractions that unify relational operators with other operators spanning multiple modalities. DIL planners support decomposition of complex requests into subqueries, retrieval from diverse sources, and finally reasoning and integration to produce final results. We demonstrate DIL through two interactive scenarios in which user queries dynamically trigger multi-source retrieval, cross-modal reasoning, and result synthesis, illustrating how compound AI systems can move beyond single database NL2SQL.

cs.AI↗

arXiv2026

Weighted Set Multi-Cover on Bounded Universe and Applications in Package Recommendation

The weighted set multi-cover problem is a fundamental generalization of set cover that arises in data-driven applications where one must select a small, low-cost subset from a large collection of candidates under coverage constraints. In data management settings, such problems arise naturally either as expressive database queries or as post-processing steps over query results, for example, when selecting representative or diverse subsets from large relations returned by database queries for decision support, recommendation, fairness-aware data selection, or crowd-sourcing. While the general weighted set multi-cover problem is NP-complete, many practical workloads involve a \emph{bounded universe} of items that must be covered, leading to the Weighted Set Multi-Cover with Bounded Universe (WSMC-BU) problem, where the universe size is constant. In this paper, we develop exact and approximation algorithms for WSMC-BU. We first discuss a dynamic programming algorithm that solves WSMC-BU exactly in $O(n^{\ell+1})$ time, where $n$ is the number of input sets and $\ell=O(1)$ is the universe size. We then present a $2$-approximation algorithm based on linear programming and rounding, running in $O(\mathcal{L}(n))$ time, where $\mathcal{L}(n)$ denotes the complexity of solving a linear program with $O(n)$ variables. To further improve efficiency for large datasets, we propose a faster $(2+\varepsilon)$-approximation algorithm with running time $O(n \log n + \mathcal{L}(\log W))$, where $W$ is the ratio of the total weight to the minimum weight, and $\varepsilon$ is an arbitrary constant specified by the user. Extensive experiments on real and synthetic datasets demonstrate that our methods consistently outperform greedy and standard LP-rounding baselines in both solution quality and runtime, making them suitable for data-intensive selection tasks over large query outputs.

cs.DB↗

arXiv2025

Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor

Frequency estimation in streaming data often relies on sketches like Count-Min (CM) to provide approximate answers with sublinear space. However, CM sketches introduce additive errors that disproportionately impact low-frequency elements, creating fairness concerns across different groups of elements. We introduce Fair-Count-Min, a frequency estimation sketch that guarantees equal expected approximation factors across element groups, thus addressing the unfairness issue. We propose a column partitioning approach with group-aware semi-uniform hashing to eliminate collisions between elements from different groups. We provide theoretical guarantees for fairness, analyze the price of fairness, and validate our theoretical findings through extensive experiments on real-world and synthetic datasets. Our experimental results show that Fair-Count-Min achieves fairness with minimal additional error and maintains competitive efficiency compared to standard CM sketches.

cs.DS↗

arXiv2024

FairEM360: A Suite for Responsible Entity Matching

Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mitigating the biases that exist in the data or are introduced by the matcher at this stage can contribute to promoting fairness in downstream tasks. This demonstration showcases FairEM360, a framework for 1) auditing the output of entity matchers across a wide range of fairness measures and paradigms, 2) providing potential explanations for the underlying reasons for unfairness, and 3) providing resolutions for the unfairness issues through an exploratory process with human-in-the-loop feedback, utilizing an ensemble of matchers. We aspire for FairEM360 to contribute to the prioritization of fairness as a key consideration in the evaluation of EM pipelines.

cs.DB↗

arXiv2024

REQUAL-LM: Reliability and Equity through Aggregation in Large Language Models

The extensive scope of large language models (LLMs) across various domains underscores the critical importance of responsibility in their application, beyond natural language processing. In particular, the randomized nature of LLMs, coupled with inherent biases and historical stereotypes in data, raises critical concerns regarding reliability and equity. Addressing these challenges are necessary before using LLMs for applications with societal impact. Towards addressing this gap, we introduce REQUAL-LM, a novel method for finding reliable and equitable LLM outputs through aggregation. Specifically, we develop a Monte Carlo method based on repeated sampling to find a reliable output close to the mean of the underlying distribution of possible outputs. We formally define the terms such as reliability and bias, and design an equity-aware aggregation to minimize harmful bias while finding a highly reliable output. REQUAL-LM does not require specialized hardware, does not impose a significant computing load, and uses LLMs as a blackbox. This design choice enables seamless scalability alongside the rapid advancement of LLM technologies. Our system does not require retraining the LLMs, which makes it deployment ready and easy to adapt. Our comprehensive experiments using various tasks and datasets demonstrate that REQUAL- LM effectively mitigates bias and selects a more equitable response, specifically the outputs that properly represents minority groups.

cs.CL↗

arXiv2024

A Fair and Memory/Time-efficient Hashmap

Hashmap is a fundamental data structure in computer science. There has been extensive research on constructing hashmaps that minimize the number of collisions leading to efficient lookup query time. Recently, the data-dependant approaches, construct hashmaps tailored for a target data distribution that guarantee to uniformly distribute data across different buckets and hence minimize the collisions. Still, to the best of our knowledge, none of the existing technique guarantees group fairness among different groups of items stored in the hashmap. Therefore, in this paper, we introduce FairHash, a data-dependant hashmap that guarantees uniform distribution at the group-level across hash buckets, and hence, satisfies the statistical parity notion of group fairness. We formally define, three notions of fairness and, unlike existing work, FairHash satisfies all three of them simultaneously. We propose three families of algorithms to design fair hashmaps, suitable for different settings. Our ranking-based algorithms reduce the unfairness of data-dependant hashmaps without any memory-overhead. The cut-based algorithms guarantee zero-unfairness in all cases, irrespective of how the data is distributed, but those introduce an extra memory-overhead. Last but not least, the discrepancy-based algorithms enable trading off between various fairness notions. In addition to the theoretical analysis, we perform extensive experiments to evaluate the efficiency and efficacy of our algorithms on real datasets. Our results verify the superiority of FairHash compared to the other baselines on fairness at almost no performance cost.

cs.DS↗

arXiv2024

Reliability Evaluation of Individual Predictions: A Data-centric Approach

Machine learning models only provide probabilistic guarantees on the expected loss of random samples from the distribution represented by their training data. As a result, a model with high accuracy, may or may not be reliable for predicting an individual query point. To address this issue, XAI aims to provide explanations of individual predictions, while approaches such as conformal predictions, probabilistic predictions, and prediction intervals count on the model's certainty in its prediction to identify unreliable cases. Conversely, instead of relying on the model itself, we look for insights in the training data. That is, following the fact a model's performance is limited to the data it has been trained on, we ask "is a model trained on a given data set, fit for making a specific prediction?". Specifically, we argue that a model's prediction is not reliable if (i) there were not enough similar instances in the training set to the query point, and (ii) if there is a high fluctuation (uncertainty) in the vicinity of the query point in the training set. Using these two observations, we propose data-centric reliability measures for individual predictions and develop novel algorithms for efficient and effective computation of the reliability measures during inference time. The proposed algorithms learn the necessary components of the measures from the data itself and are sublinear, which makes them scalable to very large and multi-dimensional settings. Furthermore, an estimator is designed to enable no-data access during the inference time. We conduct extensive experiments using multiple real and synthetic data sets and different tasks, which reflect a consistent correlation between distrust values and model performance.

cs.DB↗

arXiv2023

Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching

Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias and its societal impacts. Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching. Towards addressing this gap, we perform an extensive experimental evaluation of a variety of EM techniques in this paper. We generated two social datasets from publicly available datasets for the purpose of auditing EM through the lens of fairness. Our findings underscore potential unfairness under two common conditions in real-world societies: (i) when some demographic groups are overrepresented, and (ii) when names are more similar in some groups compared to others. Among our many findings, it is noteworthy to mention that while various fairness definitions are valuable for different settings, due to EM's class imbalance nature, measures such as positive predictive value parity and true positive rate parity are, in general, more capable of revealing EM unfairness.

cs.DB↗

arXiv2023

Data Coverage for Detecting Representation Bias in Image Datasets: A Crowdsourcing Approach

Existing machine learning models have proven to fail when it comes to their performance for minority groups, mainly due to biases in data. In particular, datasets, especially social data, are often not representative of minorities. In this paper, we consider the problem of representation bias identification on image datasets without explicit attribute values. Using the notion of data coverage for detecting a lack of representation, we develop multiple crowdsourcing approaches. Our core approach, at a high level, is a divide and conquer algorithm that applies a search space pruning strategy to efficiently identify if a dataset misses proper coverage for a given group. We provide a different theoretical analysis of our algorithm, including a tight upper bound on its performance which guarantees its near-optimality. Using this algorithm as the core, we propose multiple heuristics to reduce the coverage detection cost across different cases with multiple intersectional/non-intersectional groups. We demonstrate how the pre-trained predictors are not reliable and hence not sufficient for detecting representation bias in the data. Finally, we adjust our core algorithm to utilize existing models for predicting image group(s) to minimize the coverage identification cost. We conduct extensive experiments, including live experiments on Amazon Mechanical Turk to validate our problem and evaluate our algorithms' performance.

cs.DB↗

arXiv2023

PopSim: An Individual-level Population Simulator for Equitable Allocation of City Resources

Historical systematic exclusionary tactics based on race have forced people of certain demographic groups to congregate in specific urban areas. Aside from the ethical aspects of such segregation, these policies have implications for the allocation of urban resources including public transportation, healthcare, and education within the cities. The initial step towards addressing these issues involves conducting an audit to assess the status of equitable resource allocation. However, due to privacy and confidentiality concerns, individual-level data containing demographic information cannot be made publicly available. By leveraging publicly available aggregated demographic statistics data, we introduce PopSim, a system for generating semi-synthetic individual-level population data with demographic information. We use PopSim to generate multiple benchmark datasets for the city of Chicago and conduct extensive statistical evaluations to validate those. We further use our datasets for several case studies that showcase the application of our system for auditing equitable allocation of city resources.

cs.CY↗

arXiv2023

Representation Bias in Data: A Survey on Identification and Resolution Techniques

Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. Given that "bias in, bias out", one cannot expect AI-based solutions to have equitable outcomes for societal applications, without addressing issues such as representation bias. While there has been extensive study of fairness in machine learning models, including several review papers, bias in the data has been less studied. This paper reviews the literature on identifying and resolving representation bias as a feature of a data set, independent of how consumed later. The scope of this survey is bounded to structured (tabular) and unstructured (e.g., image, text, graph) data. It presents taxonomies to categorize the studied techniques based on multiple design dimensions and provides a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.

cs.DB↗