Data is a critical component of modern decision-making systems; system malfunctions (e.g., performance degradation and module failure) can often be traced back to a mismatch between the properties of the data and the assumptions of the system modules that process the data. For example, with the increasing use of open-source libraries to develop data science pipelines, common causes of system malfunctions include inappropriately configured data processing libraries for data cleaning tasks such as entity resolution or missing value imputation. Our objective is to resolve malfunctioning pipelines and improve their utility; we introduce PipeLens, a framework that leverages successful and unsuccessful runs of past pipelines for fixing pipeline malfunctions. PipeLens uses an acyclic graph representation of the pipeline and performs causal reasoning through interventions: when a system malfunctions with a given dataset, PipeLens modifies the pipeline (by changing its structure or the parameters of its modules) and observes the impact of this intervention on system behavior. To focus on useful interventions, we learn a proxy function that approximates the pipeline's utility over a dataset and guides the search for the best intervention. Unlike traditional observational analysis that reports correlations between system parameters and their behavior, we provide causally verified root causes and suggest pipeline modifications that rectify malfunctions. Empirical evaluation on four data science tasks over four real-world datasets demonstrates that PipeLens consistently outperforms baselines in terms of interventions performed to repair malfunctions while maintaining practical running times.
Jahid Hasan, Stanley Jiang, T. Singh et al.· Proceedings of the VLDB Endo...· 0 citations
Answering an unbounded number of multi-dimensional range queries while preserving privacy is a significant problem that has been the focus of recent studies since range queries serve as a core component of many data analysis tasks. Existing techniques that use local differential privacy (LDP), which adds noise to users’ data locally, make assumptions that significantly affect utility. In particular, they assume that all attributes in a dataset are correlated and materialize all pair-wise grids/trees. Moreover, they assume that each pair-wise correlation is equally relevant when answering queries. These assumptions result in a low signal/noise ratio, especially for datasets with a large number of attributes. To address the deficiency of existing techniques, we propose a new approach to answer multi-dimensional range queries that leverages multi-dimensional correlations and workload characteristics to selectively choose the data that is collected under the LDP model. Our approach first leverages the availability of a small number of DP users to build a correlation model to determine how attributes are correlated. Then, we use the notion of multi-dimensional grids to capture multi-dimensional correlations. That allows us to materialize fewer grids, boosting the signal/noise ratio. Finally, we propose a new optimization step that makes use of available workload characteristics to identify the user distribution that minimizes the total error when answering multi-dimensional queries among users during the grid assignment process. We conduct extensive experiments on real and synthetic datasets and demonstrate the superiority of our method over existing approaches.
José S. Costa, Felipe T. Brito, Victor A. E. Farias et al.· The VLDB journal· 0 citations
Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited support for trajectory data, lacking authenticated data structures (ADS) that enable efficient verification. For example, ADSs designed for queries on one-dimensional data are unsuitable for queries on multidimensional trajectory data, while ADSs tailored for discrete data may yield incomplete results when applied to continuous trajectory data. We propose the first framework for verifiable trajectory range queries in hybrid-storage blockchains, called VTRQ. It features two efficient ADSs: (i) a spatial ADS for road networks that leverages hierarchical organization to aggregate trajectory, edge, and node hashes, thus reducing redundant computations and improving spatial verification efficiency; and (ii) a temporal ADS based on interval trees, which indexes only the start and end times of trajectories, thereby enabling pruning and efficient temporal verification. By separating spatial and temporal indexing, the method reduces the need for data comparison, enhancing both query and verification efficiency. To aggregate spatial and temporal query results, VTRQ provides a spatio-temporal edge aggregation mechanism that combines temporal verification of spatial nodes, spatial intersection computation, and temporal intersection analysis to achieve spatio-temporal filtering.
Zhongming Yao, Junchang Xin, Yumeng Song et al.· 0 citations
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