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Data"],"published-print":{"date-parts":[[2026,4,2]]},"abstract":"\n We study Aggregation Queries over Nearest Neighbors (AQNN), which compute aggregates over the learned representations of the neighborhood of a designated query object. For example, a medical professional may be interested in\n the average heart rate of patients whose representations are similar to that of an insomnia patient.<\/jats:italic>\n Answering AQNNs accurately and efficiently is challenging due to the high cost of generating high-quality representations (e.g., via a deep learning model trained on human expert annotations) and the different sensitivities of different aggregation functions to neighbor selection errors. We address these challenges by combining high-quality and low-cost representations to approximate the aggregate. We characterize value- and count-sensitive AQNNs and propose the\n Sampler with Precision-Recall in Target<\/jats:italic>\n (\n SPRinT<\/jats:sc>\n ), a query answering framework that works in three steps: (1) sampling, (2) nearest neighbor selection, and (3) aggregation. We further establish theoretical bounds on sample sizes and aggregation errors. Extensive experiments on five datasets from three domains (medical, social media, and e-commerce) demonstrate that\n SPRinT<\/jats:sc>\n achieves the lowest aggregation error with minimal computation cost in most cases compared to existing solutions.\n SPRinT<\/jats:sc>\n 's performance remains stable as dataset size grows, confirming its scalability for large-scale applications requiring both accuracy and efficiency.\n <\/jats:p>","DOI":"10.1145\/3786672","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:54:13Z","timestamp":1775584453000},"page":"1-26","source":"Crossref","is-referenced-by-count":0,"title":["On Efficient Approximate Aggregate Nearest Neighbor Queries over Learned Representations"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7638-1448","authenticated-orcid":false,"given":"Carrie","family":"Wang","sequence":"first","affiliation":[{"name":"The University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6194-4502","authenticated-orcid":false,"given":"Sihem","family":"Amer-Yahia","sequence":"additional","affiliation":[{"name":"CNRS, Univ. 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ACM Manag. Data"],"published-print":{"date-parts":[[2025,2,10]]},"abstract":"Existing what-if analysis systems are predominantly tailored to operate on either only the application layer or only the database layer of software. This isolated approach limits their effectiveness in scenarios where intensive interaction between applications and database systems occurs. To address this gap, we introduce Ultraverse, a what-if analysis framework that seamlessly integrates both application and database layers. Ultraverse employs dynamic symbolic execution to effectively translate application code into compact SQL procedure representations, thereby synchronizing application semantics at both SQL and application levels during what-if replays. A novel aspect of Ultraverse is its use of advanced query dependency analysis, which serves two key purposes: (1) it eliminates the need to replay irrelevant transactions that do not influence the outcome, and (2) it facilitates parallel replay of mutually independent transactions, significantly enhancing the analysis efficiency. Ultraverse is applicable to existing unmodified database systems and legacy application codes. Our extensive evaluations of the framework have demonstrated remarkable improvements in what-if analysis speed, achieving performance gains ranging from 7.7x to 291x across diverse benchmarks.<\/jats:p>","DOI":"10.1145\/3709734","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T15:45:06Z","timestamp":1739288706000},"page":"1-27","source":"Crossref","is-referenced-by-count":1,"title":["Ultraverse: An Efficient What-if Analysis Framework for Software Applications Interacting with Database Systems"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1282-5208","authenticated-orcid":false,"given":"Ronny","family":"Ko","sequence":"first","affiliation":[{"name":"Osaka University, Osaka, Kansai, Japan, Ohio State University, Columbus, USA, and Sensor Tech Inc., Daejeon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7239-5134","authenticated-orcid":false,"given":"Chuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"Osaka University, Osaka, Japan and Nagoya University, Nagoya, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5559-8300","authenticated-orcid":false,"given":"Makoto","family":"Onizuka","sequence":"additional","affiliation":[{"name":"Osaka University, Osaka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6527-5994","authenticated-orcid":false,"given":"Zhiqiang","family":"Lin","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8717-8343","authenticated-orcid":false,"given":"Yihe","family":"Huang","sequence":"additional","affiliation":[{"name":"Databricks, San Francisco, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1179","volume-title":"VLDB","author":"Alexe B.","year":"2006","unstructured":"B. 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ACM Manag. Data"],"published-print":{"date-parts":[[2024,5,29]]},"abstract":"Extracting interesting patterns from data is the main objective of Data Mining. In this context, Frequent Itemset Mining has shown its usefulness in providing insights from transactional databases, which, in turn, can be used to gain insights about the structure of Knowledge Graphs. While there have been a lot of advances in the field, due to the NP-hard nature of the problem, the main approaches still struggle when they are faced with large databases with large and sparse vocabularies, such as the ones obtained from graph propositionalizations. There have been efforts to propose parallel algorithms, but, so far, the goal has not been to tackle this source of complexity (i.e., vocabulary size), thus, in this paper, we propose to parallelize frequent itemset mining algorithms by partitioning the database horizontally (i.e., transaction-wise) while not neglecting all the possible vertical information (i.e., item-wise). Instead of relying on pure item co-appearance metrics, we advocate for the adoption of a different approach: modeling databases as documents, where each transaction is a sentence, and each item a word. In this way, we can apply recent language modeling techniques (i.e., word embeddings) to obtain a continuous representation of the database, clusterize it in different partitions, and apply any mining algorithm to them. We show how our proposal leads to informed partitions with a reduced vocabulary size and a reduced entropy (i.e., disorder). 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ACM Manag. Data"],"published-print":{"date-parts":[[2023,5,26]]},"abstract":"Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data cleaning that asks for all tuples guaranteed to be returned by a given query on all (in most cases, exponentially many) repairs of the inconsistent data. In this paper, we identify a class of acyclic select-project-join (SPJ) queries for which CQA can be solved via SQL rewriting with a linear time guarantee. Our rewriting method can be viewed as a generalization of Yannakakis' algorithm for acyclic joins to the inconsistent setting. We present LinCQA, a system that takes as input any query in our class and outputs rewritings in both SQL and non-recursive Datalog with negation. 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ACM Manag. Data"],"published-print":{"date-parts":[[2025,6,17]]},"abstract":"\n The increasing collection and analysis of personal data driven by digital technologies has raised concerns about individual privacy. Local Differential Privacy (LDP) has emerged as a promising solution to provide rigorous privacy guarantee for users, without relying on a trusted data collector. In the context of LDP, range mean estimation over numerical values is an important yet challenging problem. Simply applying existing work may introduce overly large noise sensitivity, since all of them focus on statistical tasks (e.g., mean or distribution) across the entire domain. In this paper, we propose a novel framework for <u>Priv<\/u>ate <u>R<\/u>ange <u>M<\/u>ean (\n PrivRM<\/jats:italic>\n ) estimation under LDP. Two implementations of the framework, namely\n PrivRM<\/jats:italic>\n I<\/jats:sup>\n and\n PrivRM<\/jats:italic>\n *<\/jats:sup>\n , are developed, which are adaptable to all existing numerical value perturbation mechanisms. As an optimization of the framework, we also propose a distribution-aware Adaptive Adjustment (AA) strategy to dynamically confine the perturbation space for skewed data distributions. Extensive experimental results show that under the same privacy guarantee and query range, our framework\n PrivRM<\/jats:italic>\n significantly improve over existing solutions.\n <\/jats:p>","DOI":"10.1145\/3725414","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:23:29Z","timestamp":1750281809000},"page":"1-26","source":"Crossref","is-referenced-by-count":3,"title":["PrivRM<\/i>\n : A Framework for Range Mean Estimation under Local Differential Privacy"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7279-4352","authenticated-orcid":false,"given":"Liantong","family":"Yu","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1547-2847","authenticated-orcid":false,"given":"Qingqing","family":"Ye","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5478-896X","authenticated-orcid":false,"given":"Rong","family":"Du","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2019. 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ACM Manag. Data"],"published-print":{"date-parts":[[2024,5,29]]},"abstract":"B-trees are widely recognized as one of the most important index structures in database systems, providing efficient query processing capabilities. Over the past few decades, many techniques have been developed to enhance the efficiency of B-trees from various perspectives. Among them, B-tree compression is an important technique introduced as early as the 1970s to improve both space efficiency and query performance. Since then, several B-tree compression techniques have been developed. However, to our surprise, we have found that these B-tree compression techniques were never compared against each other in prior works. Consequently, many important questions remain unanswered, such as whether B-tree compression is truly effective or not. If it is effective, under what scenarios and which B-tree compression methods should be employed? In this paper, we conduct the first experimental evaluation of seven widely used B-tree compression techniques using both synthetic and real datasets. 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ACM Manag. Data"],"published-print":{"date-parts":[[2026,4,2]]},"abstract":"Continuous subgraph matching (CSM), which finds incremental matches of a query graph for each update in a dynamic graph, has gained significant research attention. Most CSM algorithms follow a common indexing-enumeration paradigm: they first use indexes to identify candidate vertices and edges for the query graph, and then enumerate matches based on these candidates. Although there have been several comprehensive experimental analyses of CSM algorithms, they tend to evaluate CSM algorithms holistically, obscuring the distinct contributions of the indexing and enumeration methods to overall performance. In this paper, we decouple the indexing method and enumeration method of existing CSM algorithms, and focus on the comparison of indexing methods. Our experimental results offer guidance on index selection across different scenarios, serving as a reference for future research and industrial applications. They further reveal the relative importance of different index components, informing strategies to discard less essential parts when memory is limited. 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ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"Audit logs serve as a fundamental data source for system security. However, extracting high-value threat information from massive log data poses significant data management challenges: traditional unsupervised models produce high false positive rates due to their inherent assumption of equating statistical anomalies with malicious activities; Emerging Large Language Model (LLM) based solutions, despite their powerful semantic understanding capabilities, are constrained by high computational costs and context window lengths, making it difficult to detect attacks within massive logs. Furthermore, the outputs of existing methods differ significantly from the practical attack reports required by security analysts.<\/jats:p>\n To overcome these limitations, this paper proposes ANTEATER, an innovative end-to-end attack investigation framework based on raw logs that features a cascading ''filter-then-scrutinize'' architecture. The ''Filter'' stage is a lightweight, flow-based anomaly detection model that efficiently filters massive logs and reduces the data scale for investigation. Subsequently, the ''Scrutinize'' stage is an attack investigation model with a three-agent LLM collaboration. It operates on a provenance graph constructed from the filtered anomalous logs. The agents collaboratively and autonomously explore and reconstruct the attack subgraph, then generate a structured natural-language report. ANTEATER not only effectively mitigates the LLM bottleneck from cost and context window, enabling it to tackle long-term, stealthy attacks, but also bridges the critical gap between raw data detection and the generation of readable attack reports.<\/jats:p>","DOI":"10.1145\/3802012","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["ANTEATER: A Filter-then-Scrutinize Architecture for End-to-End Attack Investigation"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0072-163X","authenticated-orcid":false,"given":"Yiming","family":"Ren","sequence":"first","affiliation":[{"name":"Institute of Information Engineering\uff0cChinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9516-7994","authenticated-orcid":false,"given":"Haoqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering\uff0cChinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6967-4128","authenticated-orcid":false,"given":"Linghao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering\uff0cChinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9034-7598","authenticated-orcid":false,"given":"Haoyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering\uff0cChinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2646-6100","authenticated-orcid":false,"given":"Chengxiang","family":"Si","sequence":"additional","affiliation":[{"name":"National Computer Network Emergency Response Technical Team\/Coordination Center of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-5848","authenticated-orcid":false,"given":"Zhou","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4815-3463","authenticated-orcid":false,"given":"Qingyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al., 2023. 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ACM Manag. Data"],"published-print":{"date-parts":[[2024,3,12]]},"abstract":"Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques it is adjacent to. One useful output of nucleus decomposition is to generate a hierarchy among dense subgraphs at different resolutions. However, existing parallel algorithms for nucleus decomposition do not generate this hierarchy, and only compute the coreness values. This paper presents a scalable parallel algorithm for hierarchy construction, with practical optimizations, such as interleaving the coreness computation with hierarchy construction and using a concurrent union-find data structure in an innovative way to generate the hierarchy. We also introduce a parallel approximation algorithm for nucleus decomposition, which achieves much lower span in theory and better performance in practice. 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ACM Manag. Data"],"published-print":{"date-parts":[[2024,5,29]]},"abstract":"The study of uncertain graphs is crucial in diverse fields, including but not limited to protein interaction analysis, viral marketing, and network reliability. Processing queries on uncertain graphs presents formidable challenges due to the vast probabilistic space they encapsulate. While existing systems employ batch processing to address these challenges, their performance is often compromised by the suboptimal selection of parallel graph traversal methods, the excessive costs in random number generation, and additional workloads intrinsic to batch processing. In this paper, we introduce uBlade, an efficient batch-processing framework for uncertain graph queries on multi-core CPUs. uBlade utilizes the work-efficient graph traversal, achieving superior parallelism in the batch processing model. Additionally, our Quasi-Sampling technique reduces the random number generation cost by a factor of B, with O(B) denoting the batch size. We further examine the extra workload resulting from batch processing and introduce an efficient strategy to reorder possible worlds, minimizing this associated overhead. 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ACM Manag. Data"],"published-print":{"date-parts":[[2024,3,12]]},"abstract":"Sparse operators, i.e., operators that take sparse tensors as input, are of great importance in deep learning models. Due to the diverse sparsity patterns in different sparse tensors, it is challenging to optimize sparse operators by seeking an optimal sparse format, i.e., leading to the lowest operator latency. Existing works propose to decompose a sparse tensor into several parts and search for a hybrid of sparse formats to handle diverse sparse patterns. However, they often make a trade-off between search space and search time: their search spaces are limited in some cases, resulting in limited operator running efficiency they can achieve. In this paper, we try to extend the search space in its breadth (by doing flexible sparse tensor transformations) and depth (by enabling multi-level decomposition). We formally define the multi-level sparse format decomposition problem, which is NP-hard, and we propose a framework STile for it. To search efficiently, a greedy algorithm is used, which is guided by a cost model about the latency of computing a sub-task of the original operator after decomposing the sparse tensor. Experiments of two common kinds of sparse operators, SpMM and SDDMM, are conducted on various sparsity patterns, and we achieve 2.1-18.0\u00d7 speedup against cuSPARSE on SpMMs and 1.5 - 6.9\u00d7 speedup against DGL on SDDMM. The search time is less than one hour for any tested sparse operator, which can be amortized.<\/jats:p>","DOI":"10.1145\/3639323","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T18:51:32Z","timestamp":1711479092000},"page":"1-26","source":"Crossref","is-referenced-by-count":6,"title":["STile: Searching Hybrid Sparse Formats for Sparse Deep Learning Operators Automatically"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6462-5825","authenticated-orcid":false,"given":"Jingzhi","family":"Fang","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8364-3674","authenticated-orcid":false,"given":"Yanyan","family":"Shen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8618-9806","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8257-5806","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology & The Hong Kong University of Science and Technology (Guangzhou), Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Boman","author":"Ahrens Peter","year":"2020","unstructured":"Peter Ahrens and Erik G. 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ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"Predictive maintenance (PdM) is a key application of the industrial IoT, using sensor-acquired time-series data to forecast equipment failures that cost billions of dollars annually. Time-series anomaly detection (TSAD) offers a promising route to PdM, yet prior work is constrained by: i) reliance on private datasets or case studies, requiring extensive domain-specific engineering; ii) evaluation of only a small number of algorithms, limiting comparative insight; iii) use of a single implementation strategy per algorithm, hindering potential improvements; iv) emphasis on supervised solutions, necessitating annotated data that are costly or unavailable in practice; and v) neglect of runtime and online applicability, raising questions about deployability. To address these limitations, we present an extensive experimental study with rigorous statistical analysis conducted within a common evaluation framework. Our objectives are to: i) provide insights into the accuracy, robustness, and behavior of TSAD techniques in an online PdM setting across four implementation strategies; ii) analyze effectiveness\u2013runtime trade-offs; and iii) rate dataset difficulty and characterize the forms exhibited by anomalies preceding machine breakdowns. Our findings indicate that most industrial cases benefit from an initial calibration phase for operational data collection. Well-established traditional TSAD methods deliver the best trade-off among effectiveness, runtime efficiency, and the ability to predict individual failure types; pre-trained LLMs are still statistically outperformed in this context, suggesting that there is still room for improvement in that direction. Our study serves as a significant step towards disseminating TSAD for PdM and we open source our work to support future research.<\/jats:p>","DOI":"10.1145\/3802119","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-33","source":"Crossref","is-referenced-by-count":3,"title":["The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3057-4157","authenticated-orcid":false,"given":"Anastasios","family":"Papadopoulos","sequence":"first","affiliation":[{"name":"School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5394-6923","authenticated-orcid":false,"given":"Apostolos","family":"Giannoulidis","sequence":"additional","affiliation":[{"name":"ETIS, UMR 8051, CYU, ENSEA, CNRS, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3976-7615","authenticated-orcid":false,"given":"Anastasios","family":"Gounaris","sequence":"additional","affiliation":[{"name":"School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7592-748X","authenticated-orcid":false,"given":"John","family":"Paparrizos","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, Ohio, USA and School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2026. 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Existing practical range filters have addressed this problem heuristically, resulting in high false positive rates and query times when dealing with adversarial inputs, such as in the common scenario where queries are correlated with the keys.<\/jats:p>\n We introduce Grafite, a novel range filter that solves these issues with a simple design and clear theoretical guarantees that hold regardless of the input data and query distribution: given a fixed space budget of B bits per key, the query time is O(1), and the false positive probability is upper bounded by l\/2B-2, where l is the query range size. Our experimental evaluation shows that Grafite is the only range filter to date to achieve robust and predictable false positive rates across all combinations of datasets, query workloads, and range sizes, while providing faster queries and construction times, and dominating all competitors in the case of correlated queries.<\/jats:p>\n As a further contribution, we introduce a very simple heuristic range filter whose performance on uncorrelated queries is very close to or better than the one achieved by the best heuristic range filters proposed in the literature so far.<\/jats:p>","DOI":"10.1145\/3639258","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T18:51:32Z","timestamp":1711479092000},"page":"1-23","source":"Crossref","is-referenced-by-count":11,"title":["Grafite: Taming Adversarial Queries with Optimal Range Filters"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8397-7580","authenticated-orcid":false,"given":"Marco","family":"Costa","sequence":"first","affiliation":[{"name":"University of Pisa, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1353-360X","authenticated-orcid":false,"given":"Paolo","family":"Ferragina","sequence":"additional","affiliation":[{"name":"University of Pisa, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0328-7791","authenticated-orcid":false,"given":"Giorgio","family":"Vinciguerra","sequence":"additional","affiliation":[{"name":"University of Pisa, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/2556549.2556556"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/380752.380842"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3505286"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15775-2_37"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/362686.362692"},{"key":"e_1_2_2_6_1","volume-title":"Network Applications of Bloom Filters: A Survey. 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Massachusetts Institute of Technology, Project MAC."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080789"},{"key":"e_1_2_2_18_1","volume-title":"Proc. 26th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). 769--775","author":"Goswami Mayank","year":"2014","unstructured":"Mayank Goswami, Allan Gr\u00f8nlund, Kasper Green Larsen, and Rasmus Pagh. 2014. Approximate Range Emptiness in Constant Time and Optimal Space. 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ACM Manag. Data"],"published-print":{"date-parts":[[2024,12,18]]},"abstract":"\n Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose\n Camel<\/jats:italic>\n , a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable.\n Camel<\/jats:italic>\n compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value,\n Camel<\/jats:italic>\n identifies values that enable higher compression ratios.\n Camel<\/jats:italic>\n also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of\n Camel<\/jats:italic>\n and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that\n Camel<\/jats:italic>\n is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.\n <\/jats:p>","DOI":"10.1145\/3698802","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T16:40:35Z","timestamp":1734712835000},"page":"1-26","source":"Crossref","is-referenced-by-count":4,"title":["Camel:<\/i>\n Efficient Compression of Floating-Point Time Series"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2359-9252","authenticated-orcid":false,"given":"Yuanyuan","family":"Yao","sequence":"first","affiliation":[{"name":"College of Computer Science, Zhejiang University, Hangzhou, CN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5685-7017","authenticated-orcid":false,"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University, Hangzhou, CN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2034-5501","authenticated-orcid":false,"given":"Ziquan","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University, Hangzhou, CN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-8450","authenticated-orcid":false,"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University, Hangzhou, CN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-7670","authenticated-orcid":false,"given":"Christian S.","family":"Jensen","sequence":"additional","affiliation":[{"name":"College of Computer Science, Aalborg University, Aalborg, DK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5424-6442","authenticated-orcid":false,"given":"Tianyi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, Aalborg University, Aalborg, DK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2016. 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