bluuebunny/crossref_metadata_2025 huggingface dataset:
ANALISIS PEMANFAATAN LAYANAN TELEMEDICINE PASCA PANDEMI COVID-19 DI JAWA TENGAH
Authors: Santoso Bagus Satrio , Budiyanti Rani Tiyas , Nandini Nurhasmadiar
Published: August 2024

Utilization of telemedicine services decreased after the pandemic conditions improved. Telemedicine utilization is closely related to the internet network. Central Java is the province with the lowest internet penetration rate in Java. The purpose of this study was to determine the factors related to the utilization of telemedicine services after the COVID-19 pandemic in Central Java. This type of research was quantitative research with a cross-sectional study design. This research was conducted in December 2023-March 2024. The sample in this study was 300 people in Central Java. The sampling technique used accidental sampling method. The analysis used was univariate analysis and bivariate analysis using the Chi Square test. This study showed that 50.3% of respondents utilized telemedicine services after the pandemic. Utilized telemedicine services after the pandemic related to education (p-value <0.001), knowledge (p-value <0.001), trust (p-value <0.001), family support (p-value <0.001), accessibility (p-value <0.001), and need (p-value <0.001). All independent variables in this study (education, knowledge, trust, family support, accessibility, needs) are associated with the utilization of telemedicine services after the COVID-19 pandemic.
Towards Optimal Transaction Scheduling
Authors: Cheng Audrey , Kabcenell Aaron , Chan Jason , Shi Xiao , Bailis Peter , Crooks Natacha , Stoica Ion
Published: August 2024

Maximizing transaction throughput is key to high-performance database systems, which focus on minimizing data access conflicts to improve performance. However, finding efficient schedules that reduce conflicts remains an open problem. For efficiency, previous scheduling techniques consider only a small subset of possible schedules. In this work, we propose systematically exploring the entire schedule space, proactively identifying efficient schedules, and executing them precisely during execution to improve throughput. We introduce a greedy scheduling policy, SMF, that efficiently finds fast schedules and outperforms state-of-the-art search techniques. To realize the benefits of these schedules in practice, we develop a schedule-first concurrency control protocol, MVSchedO, that enforces fine-grained operation orders. We implement both in our system R-SMF, a modified version of RocksDB, to achieve up to a 3.9× increase in throughput and 3.2× reduction in tail latency on a range of benchmarks and real-world workloads.
Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL
Authors: Fan Ju , Gu Zihui , Zhang Songyue , Zhang Yuxin , Chen Zui , Cao Lei , Li Guoliang , Madden Samuel , Du Xiaoyong , Tang Nan
Published: August 2024

Zero-shot natural language to SQL (NL2SQL) aims to generalize pretrained NL2SQL models to new environments (
e.g.
, new databases and new linguistic phenomena) without any annotated NL2SQL samples from these environments. Existing approaches either use small language models (SLMs) like BART and T5, or prompt large language models (LLMs). However, SLMs may struggle with complex natural language reasoning, and LLMs may not precisely align schemas to identify the correct columns or tables. In this paper, we propose a ZeroNL2SQL framework, which divides NL2SQL into smaller sub-tasks and utilizes both SLMs and LLMs. ZeroNL2SQL first fine-tunes SLMs for better generalizability in SQL structure identification and schema alignment, producing an
SQL sketch.
It then uses LLMs's language reasoning capability to fill in the missing information in the SQL sketch. To support ZeroNL2SQL, we propose novel database serialization and question-aware alignment methods for effective sketch generation using SLMs. Additionally, we devise a multi-level matching strategy to recommend the most relevant values to LLMs, and select the optimal SQL query via an execution-based strategy. Comprehensive experiments show that ZeroNL2SQL achieves the best zero-shot NL2SQL performance on benchmarks,
i.e.
, outperforming the state-of-the-art SLM-based methods by 5.5% to 16.4% and exceeding LLM-based methods by 10% to 20% on execution accuracy.
Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy
Authors: Kato Fumiyuki , Xiong Li , Takagi Shun , Cao Yang , Yoshikawa Masatoshi
Published: August 2024

Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP at the record-level within each silo for cross-silo FL. However, a single user's data may extend across multiple silos, and the desired user-level DP guarantee for such a setting remains unknown. In this study, we present Uldp-FL, a novel FL framework designed to guarantee user-level DP in cross-silo FL where a single user's data may belong to multiple silos. Our proposed algorithm directly ensures user-level DP through per-user weighted clipping, departing from group-privacy approaches. We provide a theoretical analysis of the algorithm's privacy and utility. Additionally, we improve the utility of the proposed algorithm with an enhanced weighting strategy based on user record distribution and design a novel private protocol that ensures no additional information is revealed to the silos and the server. Experiments on real-world datasets show substantial improvements in our methods in privacy-utility trade-offs under user-level DP compared to baseline methods. To the best of our knowledge, our work is the first FL framework that effectively provides user-level DP in the general cross-silo FL setting.
Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information Networks
Authors: Zhou Yingli , Fang Yixiang , Ma Chenhao , Hou Tianci , Huang Xin
Published: August 2024

In the heterogeneous information network (HIN), a motif-clique is a "complete graph" for a given motif (or a small connected graph) that could capture the desired relationship in the motif. The maximal motif-cliques of HINs have found various applications in community discovery, recommendation, and biological network analysis. The state-of-the-art algorithm for enumerating maximal motif-cliques may have to explore all possible subgraphs of a maximal motif-clique and check whether a maximal motif-clique has been enumerated at each recursive step, which is very time-consuming. To improve the efficiency of enumeration, in this paper, we develop efficient algorithms for maximal motif-clique enumeration over large HINs. We first introduce an order-based framework to avoid duplicated enumeration, which results in lower time complexity compared to the existing algorithm. We then propose a pivot-based pruning strategy, which significantly reduces the search space. We further optimize the process of identifying the candidate sets and locating the subgraphs containing the maximal motif-cliques. Extensive experiments on five real-world HINs demonstrate that our proposed algorithm achieves high efficiency and is up to three orders of magnitude faster than the state-of-the-art algorithm.
OUTRE: An OUT-of-Core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine
Authors: Sheng Zeang , Zhang Wentao , Tao Yangyu , Cui Bin
Published: August 2024

Sampling-based Graph Neural Networks (GNNs) have become the de facto standard for handling various graph learning tasks on large-scale graphs. As the graph size grows larger and even exceeds the standard host memory size of a single machine, out-of-core sampling-based GNN training has gained attention from the community. For out-of-core sampling-based GNN training, the performance bottleneck is the data preparation process that includes sampling neighbor lists and gathering node features from external storage. Based on this observation, existing out-of-core GNN training frameworks try to accomplish larger percentages of data requests without inquiring the external storage by designing better in-memory caches. However, the enormous overall requested data volume is unchanged under this approach. In this paper, we present a new perspective on
reducing the overall requested data volume.
Through a quantitative analysis, we find that
Neighborhood Redundancy
and
Temporal Redundancy
exist in out-of-core sampling-based GNN training. To reduce these two kinds of data redundancies, we propose OUTRE, an OUT-of-core de-REdundancy GNN training framework. OUTRE incorporates two new designs,
partition-based batch construction
and
historical embedding cache
, to reduce the corresponding data redundancies. Moreover, we propose
automatic cache space management
to automatically organize available memory for different caches. Evaluation results on four public large-scale graph datasets show that OUTRE achieves 1.52× to 3.51× speedup against the SOTA framework.
On Reducing Space Amplification with Multi-Column Compaction in Apache IoTDB
Authors: Fang Chenguang , Chen Zijie , Song Shaoxu , Huang Xiangdong , Wang Chen , Wang Jianmin
Published: August 2024

Log-structured merge trees (LSM-trees) are commonly employed as the storage engines for write-intensive workloads in modern time series databases including Apache IoTDB. Following append-only principle, LSM-trees can handle intensive writes and updates, but consequently suffer high space amplification (SA). To reduce SA in LSM-tree, compaction is triggered periodically to reorganize a large number of immutable files on disk to eliminate redundancy. This issue is further complicated in the Internet of Things (IoT) scenarios, where frequent out-of-order data insertions and data updates introduce duplicated keys, obsolete values and overlapping bitmaps in multi-column data, thereby exacerbating SA concerns.

To mitigate SA in such contexts, this paper presents a Multi-Column Compaction (MCC) strategy in Apache IoTDB, an open-source time series database utilizing LSM-tree architecture and supporting multi-column storage. We take into consideration both the separate insertions (out-of-order data) and updates of multi-column data, and analyze the hardness of selecting proper files with the maximum space reduction in compaction. We then propose a heuristic method designed to improve the file selection, thus reducing SA. To enhance the efficiency of this approach, we further devise File Prefetcher and Compaction Cache. The proposed MCC has been implemented in Apache IoTDB. Experimental results demonstrate that our proposed MCC achieves better performance in reducing space amplification.
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data
Authors: Schmidl Sebastian , Naumann Felix , Papenbrock Thorsten
Published: August 2024

Detecting anomalous subsequences in time series data is one of the key tasks in time series analytics, having applications in environmental monitoring, preventive healthcare, predictive maintenance, and many further areas. Data scientists have developed various anomaly detection algorithms with individual strengths, such as the ability to detect repeating anomalies, anomalies in non-periodic time series, or anomalies with varying lengths. For a given dataset and task, the best algorithm with a suitable parameterization and, in some cases, sufficient training data, usually solves the anomaly detection problem well. However, given the high number of existing algorithms, their numerous parameters, and a pervasive lack of training data and domain knowledge, effective anomaly detection is still a complex task that heavily relies on manual experimentation.

We propose the unsupervised AutoTSAD system, which parameterizes, executes, and ensembles various highly effective anomaly detection algorithms. The ensembling system automatically presents an aggregated anomaly scoring for an arbitrary time series without a need for training data or parameter expertise. Our experiments show that AutoTSAD offers an anomaly detection accuracy comparable to the best manually optimized anomaly detection algorithms, and can significantly outperform existing method selection and ensembling approaches for time series anomaly detection.
When Amnesia Strikes: Understanding and Reproducing Data Loss Bugs with Fault Injection
Authors: Ramos Maria , Azevedo João , Kingsbury Kyle , Pereira José , Esteves Tânia , Macedo Ricardo , Paulo João
Published: August 2024

We present LazyFS, a new fault injection tool that simplifies the debugging and reproduction of complex data
durability
bugs experienced by databases, key-value stores, and other data-centric systems in crashes. Our tool simulates persistence properties of POSIX file systems (e.g., operations
ordering
and
atomicity)
and enables users to inject lost and torn write faults with a precise and controlled approach. Further, it provides profiling information about the system's operations flow and persisted data, enabling users to better understand the root cause of errors.

We use LazyFS to study seven important systems: PostgreSQL, etcd, Zookeeper, Redis, LevelDB, PebblesDB, and Lightning Network. Our fault injection campaign shows that LazyFS automates and facilitates the reproduction of five known bug reports containing manual and complex reproducibility steps. Further, it aids in understanding and reproducing seven ambiguous bugs reported by users. Finally, LazyFS is used to find eight new bugs, which lead to data loss, corruption, and unavailability.
PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy
Authors: Wang Leixia , Ye Qingqing , Hu Haibo , Meng Xiaofeng
Published: August 2024

Answering range queries in the context of Local Differential Privacy (LDP) is a widely studied problem in Online Analytical Processing (OLAP). Existing LDP solutions all assume a uniform data distribution within each domain partition, which may not align with real-world scenarios where data distribution is varied, resulting in inaccurate estimates. To address this problem, we introduce PriPL-Tree, a novel data structure that combines hierarchical tree structures with piecewise linear (PL) functions to answer range queries for arbitrary distributions. PriPL-Tree precisely models the underlying data distribution with a few line segments, leading to more accurate results for range queries. Furthermore, we extend it to multi-dimensional cases with novel data-aware adaptive grids. These grids leverage the insights from marginal distributions obtained through PriPL-Trees to partition the grids adaptively, adapting the density of underlying distributions. Our extensive experiments on both real and synthetic datasets demonstrate the effectiveness and superiority of PriPL-Tree over state-of-the-art solutions in answering range queries across arbitrary data distributions.
HAIChart: Human and AI Paired Visualization System
Authors: Xie Yupeng , Luo Yuyu , Li Guoliang , Tang Nan
Published: August 2024

The growing importance of data visualization in business intelligence and data science emphasizes the need for tools that can efficiently generate meaningful visualizations from large datasets. Existing tools fall into two main categories: human-powered tools (
e.g.
, Tableau and PowerBI), which require intensive expert involvement, and AI-powered automated tools (
e.g.
, Draco and Table2Charts), which often fall short of
guessing
specific user needs.

In this paper, we aim to achieve the best of both worlds. Our key idea is to initially auto-generate a set of high-quality visualizations to minimize manual effort, then refine this process iteratively with user feedback to more closely align with their needs. To this end, we present HAIChart, a reinforcement learning-based framework designed to iteratively recommend good visualizations for a given dataset by incorporating user feedback. Specifically, we propose a Monte Carlo Graph Search-based visualization generation algorithm paired with a composite reward function to efficiently explore the visualization space and automatically generate good visualizations. We devise a visualization hints mechanism to actively incorporate user feedback, thus progressively refining the visualization generation module. We further prove that the top-
k
visualization hints selection problem is NP-hard and design an efficient algorithm. We conduct both quantitative evaluations and user studies, showing that HAIChart significantly outperforms state-of-the-art human-powered tools (21% better at Recall and 1.8× faster) and AI-powered automatic tools (25.1% and 14.9% better in terms of Hit@3 and R10@30, respectively).
A Sampling-Based Framework for Hypothesis Testing on Large Attributed Graphs
Authors: Wang Yun , Kosyfaki Chrysanthi , Amer-Yahia Sihem , Cheng Reynold
Published: August 2024

Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis testing on graphs is gaining importance. In this work, we formalize node, edge, and path hypotheses on attributed graphs. We develop a sampling-based hypothesis testing framework, which can accommodate existing hypothesis-agnostic graph sampling methods. To achieve accurate and time-efficient sampling, we then propose a Path-Hypothesis-Aware SamplEr, PHASE, an
m
-dimensional random walk that accounts for the paths specified in the hypothesis. We further optimize its time efficiency and propose PHASE
opt
. Experiments on three real datasets demonstrate the ability of our framework to leverage common graph sampling methods for hypothesis testing, and the superiority of hypothesis-aware sampling methods in terms of accuracy and time efficiency.
Fainder: A Fast and Accurate Index for Distribution-Aware Dataset Search
Authors: Behme Lennart , Galhotra Sainyam , Beedkar Kaustubh , Markl Volker
Published: August 2024

Efficient data discovery is crucial in the era of data-driven decisionmaking. However, current practices face significant challenges due to the intricacies of identifying datasets with specific distributional characteristics, such as percentiles, when data repositories are decentralized. Traditional keyword-based search methods are insufficient for these complex requirements, often resulting in suboptimal dataset search results. To address these challenges, this paper presents Fainder, a fast and accurate index for "percentile predicates" on histogram-based data summaries, which streamlines the search process for datasets with specific distributional requirements. Fainder can be constructed on heterogeneous histogram collections and employs binary search in conjunction with multi-step pruning techniques to efficiently identify search results for percentile predicates. Thereby, it simplifies data provisioning and improves the effectiveness of dataset discovery. Empirical evaluation of our solution on three large-scale data repositories shows that Fainder is effective for distribution-aware dataset search and provides order-of-magnitude efficiency gains over baselines.
The Dawn of Natural Language to SQL: Are We Fully Ready?
Authors: Li Boyan , Luo Yuyu , Chai Chengliang , Li Guoliang , Tang Nan
Published: August 2024

Translating users' natural language questions into SQL queries (
i.e.
, nl2sql) significantly lowers the barriers to accessing relational databases. The emergence of Large Language Models has introduced a novel paradigm in nl2sql tasks, enhancing capabilities dramatically. However, this raises a critical question:
Are we fully prepared to deploy
nl2sql
models in production?

To address the posed questions, we present a multi-angle nl2sql evaluation framework,
NL2SQL360
, to facilitate the design and test of new nl2sql methods for researchers. Through
NL2SQL360
, we conduct a detailed comparison of leading nl2sql methods across a range of application scenarios, such as different data domains and sql characteristics, offering valuable insights for selecting the most appropriate nl2sql methods for specific needs. Moreover, we explore the nl2sql design space, leveraging NL2SQL360 to automate the identification of an optimal nl2sql solution tailored to user-specific needs. Specifically,
NL2SQL360
identifies an effective nl2sql method,
SuperSQL
, distinguished under the Spider dataset using the execution accuracy metric. Remarkably,
SuperSQL
achieves competitive performance with execution accuracy of
87
% and
62.66
% on the Spider and BIRD test sets, respectively.
Counterfactual Explanation of Shapley Value in Data Coalitions
Authors: Si Michelle , Pei Jian
Published: August 2024

The Shapley value is widely used for data valuation in data markets. However, explaining the Shapley value of an owner in a data coalition is an unexplored and challenging task. To tackle this, we formulate the problem of finding the counterfactual explanation of Shapley value in data coalitions. Essentially, given two data owners
A
and
B
such that
A
has a higher Shapley value than
B
, a counter-factual explanation is a smallest subset of data entries in
A
such that transferring the subset from
A
to
B
makes the Shapley value of
A
less than that of
B.
We show that counterfactual explanations always exist, but finding an exact counterfactual explanation is NP-hard. Using Monte Carlo estimation to approximate counterfactual explanations directly according to the definition is still very costly, since we have to estimate the Shapley values of owners
A
and
B
after each possible subset shift. We develop a series of heuristic techniques to speed up computation by estimating differential Shapley values, computing the power of singular data entries, and shifting subsets greedily, culminating in the SV-Exp algorithm. Our experimental results on real datasets clearly demonstrate the efficiency of our method and the effectiveness of counterfactuals in interpreting the Shapley value of an owner.
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions
Authors: Zhang William , Lim Wan Shen , Butrovich Matthew , Pavlo Andrew
Published: August 2024

Existing machine learning (ML) approaches to automatically optimize database management systems (DBMSs) only target a single configuration space at a time (e.g., knobs, query hints, indexes). Simultaneously tuning multiple configuration spaces is challenging due to the combined space's complexity. Previous tuning methods work around this by sequentially tuning individual spaces with a pool of tuners. However, these approaches struggle to coordinate their tuners and get stuck in local optima.

This paper presents the Proto-X framework that holistically tunes multiple configuration spaces. The key idea of Proto-X is to identify similarities across multiple spaces, encode them in a high-dimensional model, and then synthesize "proto-actions" to navigate the organized space for promising configurations. We evaluate Proto-X against state-of-the-art DBMS tuning frameworks on tuning PostgreSQL for analytical and transactional workloads. By reasoning about configuration spaces that are orders of magnitude more complex than other frameworks (both in terms of quantity and variety), Proto-X discovers configurations that improve PostgreSQL's performance by up to 53% over the next best approach.
LITS: An Optimized Learned Index for Strings
Authors: Yang Yifan , Chen Shimin
Published: August 2024

Index is an important component in database systems. Learned indexes have been shown to outperform traditional tree-based index structures for fixed-sized integer or floating point keys. However, the application of the learned solution to variable-length string keys is under-researched. Our experiments show that existing learned indexes for strings fail to outperform traditional string indexes, such as HOT and ART. String keys are long and variable sized, and often contain skewed prefixes, which make the last-mile search expensive, and adversely impact the capability of learned models to capture the skewed distribution of string keys.

In this paper, we propose a novel learned index for string keys, LITS (Learned Index with Hash-enhanced Prefix Table and Subtries). We propose an optimized learned model, combining a global Hash-enhanced Prefix Table (HPT) and a per-node local linear model to better distinguish string keys. Moreover, LITS exploits compact leaf nodes and hybrid structures with a PMSS model for efficient point and range operations. Our experimental results using eleven string data sets show that LITS achieves up to 2.43x and 2.27x improvement over HOT and ART for point operations, and attains comparable scan performance.
Partition, Don't Sort! Compression Boosters for Cloud Data Ingestion Pipelines
Authors: Hansert Patrick , Michel Sebastian
Published: August 2024

Data Lakes deployed in the cloud are a go-to solution for enterprise data storage. While the pay-as-you-go cost model allows flexible resource allocation and billing, it mandates an efficient use of resources like CPU hours, network traffic, and used storage. The distributed nature of cloud environments necessitates partitioning the data and processing these partitions separately. In this work, we put forward a practical solution to improve the efficiency of compression algorithms on Dremel-encoded data by clustering similarly structured nested data at ingestion time, such that compressible partitions can be created. We propose a clustering approach inspired by decision trees that outpaces even the naive partition-then-sort approach by up to factor 17.44 while also boosting the compression by up to factor 2. We further show that when sorting the individual buckets, a compression boost that is competitive with the well-established increasing-cardinality heuristic can be achieved, but at a lower ingestion time.
DAFDiscover: Robust Mining Algorithm for Dynamic Approximate Functional Dependencies on Dirty Data
Authors: Ding Xiaoou , Lu Yixing , Wang Hongzhi , Wang Chen , Liu Yida , Wang Jianmin
Published: August 2024

Data dependency mining plays a crucial role in understanding data relationships. To address the increasing complexities of real-world data, Approximate Functional Dependencies (AFDs) have been introduced, building upon traditional FD. However, existing AFD approaches use static relaxation coefficients, limiting their effectiveness in capturing dependencies in noisy data. We propose a dynamic AFD variant, DAFD, which incorporates attribute error rates. We establish a bijection between DAFD and FD, develop its inference system, and introduce DAFDiscover, an algorithm for mining dependencies directly on noisy data. DAFDiscover matches the time and space complexity of SOTA AFD mining methods while offering superior performance. We theoretically prove its correctness, provide a method for calculating DAFD probabilities (DAFD-
prob
), and derive a lower bound for DAFD's validity on dirty data. Experimental results on multiple public datasets demonstrate the semantic superiority of DAFD and the effectiveness of DAFDiscover compared to existing SOTA AFD mining techniques.
Hardware-Efficient Data Imputation through DBMS Extensibility
Authors: Mohr-Daurat Hubert , Theodorakis Georgios , Pirk Holger
Published: August 2024

The separation of data and code/queries has served Data Management Systems (DBMSs) well for decades. However, while the resulting soundness and rigidity are the basis for many performance-oriented optimizations, it lacks the flexibility to efficiently support modern data science applications: data cleansing, data ingestion/augmentation or generative models. To support such applications without sacrificing performance, we propose a new logical data model called
Homoiconic Collection Processing (HCP).
HCP is based on a well-known Meta-Programming concept called
Homoiconicity
(a unified representation for code and data).

In a DBMS, HCP supports the storage of "classic" relational data but also allows the storage and evaluation of code fragments we refer to as "Homoiconic Expressions". Homoiconic Expressions enable applications such as data imputation
directly in the database kernel.
Implemented naïvely, such flexibility would come at a prohibitive cost in terms of performance. To make HCP performance-competitive with highly-tuned in-memory DBMSs, we develop a novel storage and processing model called
Shape-Wise Microbatching (SWM)
and implement it in a system called BOSS. BOSS is performance-competitive with high-performance DBMSs while offering unprecedented extensibility. To demonstrate the extensibility, we implement an extension for impute-and-query workloads: BOSS outperforms state-of-the-art homoiconic runtimes and data imputation systems by two to five orders of magnitude.
ReCG: Bottom-up JSON Schema Discovery Using a Repetitive Cluster-and-Generalize Framework
Authors: Yun Joohyung , Tak Byungchul , Han Wook-Shin
Published: August 2024

The schemalessness, one of the major advantages of JSON representation format, comes with high penalties in querying and operations by denying various critical functions such as query optimizations, indexing, or data verification. There have been continuous efforts to develop an accurate JSON schema discovery algorithm from a bag of JSON documents. Unfortunately, existing schema discovery techniques, being top-down algorithms, face challenges from the lack of visibility into children nodes of JSON tree. With absence of the information about lower-level JSON elements, top-down algorithms need to employ assumptions and heuristics to decide the schema type of nodes. However, such static decisions are often violated in datasets which causes top-down algorithms to perform poorly. To overcome this, we propose an algorithm, called ReCG, that processes JSON documents in a bottom-up manner. It builds up schemas from leaf elements upward in the JSON document tree and, thus, can make more informed decisions of the schema node types. In addition, we adopt MDL (Minimum Description Length) principles systematically while building up the schemas to choose among candidate schemas the most concise yet accurate one with well-balanced generality. Evaluations show that our technique improves the recall and precision of found schemas by as high as 47%, resulting in 46% better F1 score while also performing 2.11× faster on average against the state-of-the-art.
Efficient Validation of SHACL Shapes with Reasoning
Authors: Ke Jin , Zacouris Zenon , Acosta Maribel
Published: August 2024

As the usage of knowledge graphs (KGs) becomes more pervasive in practical applications, there is a burgeoning need for high-quality data. The SHApes Constraint Language (SHACL) allows for expressing certain types of quality constraints that define sub-structures and correct values in KGs modelled with RDF. Nevertheless, performing SHACL validation without entailment often yields onesided outcomes, as it falls short of validating crucial implicit data encoded in the KG ontology. Current solutions that incorporate entailment into SHACL validation are inefficient, due to the time-intensive process of applying inference rules to the entire dataset. Moreover, applying entailment for SHACL validation can generate large amounts of redundant triples, exacerbating the validation workload and resulting in erroneous or redundant validation results. In light of these challenges, we propose Re-SHACL, an approach that combines targeted reasoning and entity merging techniques to generate a concise, consolidated RDF graph devoid of redundancy. Re-SHACL significantly reduces execution time and improves the accuracy of the validation reports. Our experiments demonstrate that Re-SHACL can be combined with state-of-the-art validators to deliver accurate validation reports efficiently.
QED: A Powerful Query Equivalence Decider for SQL
Authors: Wang Shuxian , Pan Sicheng , Cheung Alvin
Published: August 2024

Checking query equivalence is of great significance in database systems. Prior work in automated query equivalence checking sets the first steps in formally modeling and reasoning about query optimization rules, but only supports a limited number of query features. In this paper, we present Qed, a new framework for query equivalence checking based on bag semantics. Qed uses a new formalism called Q-expressions that models queries using different normal forms for efficient equivalence checking, and models features such as integrity constraints and NULLs in a principled way unlike prior work. Our formalism also allows us to define a new query fragment that encompasses many real-world queries with a complete equivalence checking algorithm, assuming a complete first-order theory solver. Empirically, Qed can verify 299 out of 444 query pairs extracted from the Calcite framework and 979 out of 1287 query pairs extracted from CockroachDB, which is more than 2× the number of cases proven by prior state-of-the-art solver.
RUSH: Real-Time Burst Subgraph Detection in Dynamic Graphs
Authors: Chen Yuhang , Jiang Jiaxin , Sun Shixuan , He Bingsheng , Chen Min
Published: August 2024

Graph analytics have been effective in the data science pipeline of fraud detections. In the ever-evolving landscape of e-commerce platforms like Grab or transaction networks such as cryptos, we have witnessed the phenomenon of 'burst subgraphs,' characterized by rapid increases in subgraph density within short timeframes---as a common pattern for fraud detections on dynamic graphs. However, existing graph processing frameworks struggle to efficiently manage these due to their inability to handle sudden surges in data. In this paper, we propose RUSH (
R
eal-time b
U
rst
S
ubgrap
H
detection framework), a pioneering framework tailored for real-time fraud detection within dynamic graphs. By focusing on both the density and the rate of change of subgraphs, RUSH identifies crucial indicators of fraud. Utilizing a sophisticated incremental update mechanism, RUSH processes burst subgraphs on large-scale graphs with high efficiency. Furthermore, RUSH is designed with user-friendly APIs that simplify the customization and integration of specific fraud detection metrics. In the deployment within Grab's operations, detecting burst subgraphs can be achieved with approximately ten lines of code. Through extensive evaluations on real-world datasets, we show RUSH's effectiveness in fraud detection and its robust scalability across various data sizes. In case studies, we illustrate how RUSH can detect fraud communities within various Grab business scenarios, such as customer-merchant collusion and promotion abuse, and identify wash trading in crypto networks.
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems
Authors: Lim Wan Shen , Ma Lin , Zhang William , Butrovich Matthew , Arch Samuel , Pavlo Andrew
Published: August 2024

Autonomous database management systems (DBMSs) aim to optimize themselves automatically without human guidance. They rely on machine learning (ML) models that predict their run-time behavior to evaluate whether a candidate configuration is beneficial without the expensive execution of queries. However, the high cost of collecting the training data to build these models makes them impractical for real-world deployments. Furthermore, these models are instance-specific and thus require retraining whenever the DBMS's environment changes. State-of-the-art methods spend over 93% of their time running queries for training versus tuning.

To mitigate this problem, we present the Boot framework for automatically accelerating training data collection in DBMSs. Boot utilizes macro- and micro-acceleration (MMA) techniques that modify query execution semantics with approximate run-time telemetry and skip repetitive parts of the training process. To evaluate Boot, we integrated it into a database gym for PostgreSQL. Our experimental evaluation shows that Boot reduces training collection times by up to 268× with modest degradation in model accuracy. These results also indicate that our MMA-based approach scales with dataset size and workload complexity.
Why TPC is Not Enough: An Analysis of the Amazon Redshift Fleet
Authors: van Renen Alexander , Horn Dominik , Pfeil Pascal , Vaidya Kapil , Dong Wenjian , Narayanaswamy Murali , Liu Zhengchun , Saxena Gaurav , Kipf Andreas , Kraska Tim
Published: August 2024

Database research and development is heavily influenced by benchmarks, such as the industry-standard TPC-H and TPC-DS for analytical systems. However, these twenty-year-old benchmarks neither capture how databases are deployed nor what workloads modern cloud data warehouse systems face these days.

In this paper, we summarize well-known, confirm suspected, and unearth novel discrepancies between TPC-H/DS and actual workloads using empirical data. We base our analysis on telemetrics from Amazon Redshift - one of the largest cloud data warehouse deployments. Among others, we show how write-heavy data pipelines are prominent, workloads vary over time (in both load and type), queries are repetitive, and how most properties of queries or workloads experience very long tailed distributions. We conclude that data warehouse benchmarks, just like database systems, need to become more holistic and stop focusing solely on query engine performance. Finally, we publish a dataset containing query statistics of 200 randomly selected Redshift serverless and provisioned instances (each) over a three-month period, as a basis for building more realistic benchmarks.
Medication improves velocity, reaction time, and movement time but not amplitude or error during memory‐guided reaching in Parkinson's disease
Authors: Trevarrow Michael P. , Munoz Miranda J. , Rivera Yessenia M. , Arora Rishabh , Drane Quentin H. , Pal Gian D. , Verhagen Metman Leonard , Goelz Lisa C. , Corcos Daniel M. , David Fabian J.
Published: August 2024

The motor impairments experienced by people with Parkinson's disease (PD) are exacerbated during memory‐guided movements. Despite this, the effect of antiparkinson medication on memory‐guided movements has not been elucidated. We evaluated the effect of antiparkinson medication on motor control during a memory‐guided reaching task with short and long retention delays in participants with PD and compared performance to age‐matched healthy control (HC) participants. Thirty‐two participants with PD completed the motor section of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS‐UPDRS III) and performed a memory‐guided reaching task with two retention delays (0.5 s and 5 s) while on and off medication. Thirteen HC participants completed the MDS‐UPDRS III and performed the memory‐guided reaching task. In the task, medication increased movement velocity, decreased movement time, and decreased reaction time toward what was seen in the HC. However, movement amplitude and reaching error were unaffected by medication. Shorter retention delays increased movement velocity and amplitude, decreased movement time, and decreased error, but increased reaction times in the participants with PD and HC. Together, these results imply that antiparkinson medication is more effective at altering the neurophysiological mechanisms controlling movement velocity and reaction time compared with other aspects of movement control.
The analgesic effect of paeoniflorin: A focused review
Authors: Li Mingzhu , Zhu Xudong , Zhang Mingxue , Yu Jun , Jin Shengbo , Hu Xiaoli , Piao Haozhe
Published: August 2024

Pain has been a prominent medical concern since ancient times. Despite significant advances in the diagnosis and treatment of pain in contemporary medicine, there is no a therapeutic cure for chronic pain. Chinese herbaceous peony, a traditional Chinese analgesic herb has been in clinical use for millennia, with widespread application and substantial efficacy. Paeoniflorin (PF), the main active ingredient of Chinese herbaceous peony, has antioxidant, anti-inflammatory, anticancer, analgesic, and antispasmodic properties, among others. The analgesic effect of PF, involving multiple critical targets and pain regulatory pathways, has been a hot spot for current research. This article reviews the literature related to the analgesic effect of PF in the past decade and discusses the molecular mechanism of the analgesic effect of PF, including the protective effects of nerve cells, inhibition of inflammatory reactions, antioxidant effects, reduction of excitability in nociceptor, inhibition of the nociceptive excitatory neuroreceptor system, activation of the nociceptive inhibitory neuroreceptor system and regulation of other receptors involved in nociceptive sensitization. Thus, providing a theoretical basis for pain prevention and treatment research. Furthermore, the prospect of PF-based drug development is presented to propose new ideas for clinical analgesic therapy.
Assessment of Se, As, Cd, Cr, Hg, and Pb content status in Ankang tea plantations of China
Authors: Ji Huawei , Lin Pingxuan , Wang Hui , Chen Jiping , Ren Rui , Li Aorui
Published: August 2024

Selenium (Se) is an essential trace element for humans. The Ankang tea plantation area in China is the best place for producing high-quality and Se-enriched organic tea. However, accumulation of potentially toxic elements (PTEs) in soil and tea leaves has attracted a lot of attention. Therefore, in this research, the content status of Se and five PTEs (As, Cd, Cr, Hg, and Pb) in soil and tea leaf samples collected from 88 Ankang tea plantations was studied. Results showed that the average Se content was 0.17 mg kg
−1 and the Se-enrichment rate was 25% for the 88 tea leaf samples, and As, Cd, Cr, Hg, and Pb contents in all tea leaf samples met the Chinese standard for pollution-free tea except for 11.2% excess Cr in only one sample. Cd concentrations in 56.82% soil samples exceeded the Chinese risk screening value for soil contamination of the agricultural land, and therefore the pollution degree of Cd is high in these soil samples. Moreover, the Hakanson potential ecological risk assessment of soil As, Cd, Cr, Hg, and Pb indicated that the Ankang tea plantation area was at a medium potential ecological risk level. These results will provide theoretical support for ecological risk management and safe production of Se-enriched tea in Ankang Se-enriched tea plantations.
Artificial neural network infused quasi oppositional learning partial reinforcement algorithm for structural design optimization of vehicle suspension components
Authors: Sait Sadiq M. , Mehta Pranav , Pholdee Nantiwat , Yıldız Betül Sultan , Yıldız Ali Rıza
Published: August 2024

This paper introduces and investigates an enhanced Partial Reinforcement Optimization Algorithm (E-PROA), a novel evolutionary algorithm inspired by partial reinforcement theory to efficiently solve complex engineering optimization problems. The proposed algorithm combines the Partial Reinforcement Optimization Algorithm (PROA) with a quasi-oppositional learning approach to improve the performance of the pure PROA. The E-PROA was applied to five distinct engineering design components: speed reducer design, step-cone pulley weight optimization, economic optimization of cantilever beams, coupling with bolted rim optimization, and vehicle suspension arm optimization problems. An artificial neural network as a metamodeling approach is used to obtain equations for shape optimization. Comparative analyses with other benchmark algorithms, such as the ship rescue optimization algorithm, mountain gazelle optimizer, and cheetah optimization algorithm, demonstrated the superior performance of E-PROA in terms of convergence rate, solution quality, and computational efficiency. The results indicate that E-PROA holds excellent promise as a technique for addressing complex engineering optimization problems.
Policy Actors’ Struggle for Attention: The Role of Peer Networks in the Migration Discourse on Twitter (X)
Authors: Hanke Sara , Sicakkan Hakan G. , Van Wolleghem Pierre Georges , Heiberger Raphael Heiko
Published: August 2024

Policy actors (PAs) like nongovernmental organizations, political parties or governmental institutions strategically communicate on social media to gain attention and thus influence the public agenda. We argue that networks of PAs engaged in the same issues (i.e., a PA’s peer network) are crucial to attracting the interest of a broad audience. Drawing on network theory, we posit that (i) ideological homophily, and (ii) the centrality and embeddedness in a PA’s peer network increase the attention received from all Twitter (now X) users. We investigate these premises by analyzing the European migration discourse on Twitter (2014–2020). The results of our study reveal that the centrality of PAs in their peer networks and ideologically similar relations considerably increase attention from the whole Twittersphere. These findings provide strong evidence that a PA’s role in its organizational peer network on social media governs the attention received in the overall discourse.
Influence mechanism of paper mechanical properties: numerical simulation and experimental verification based on a fiber network
Authors: Sun Hao , Wang Yaling , Li Haojin , Wei Lingjun , Zhu Yunfeng , Zhang Wanlu , Wang Wei
Published: August 2024

Paper is a kind of renewable material that exists widely and has important application prospects. However, previous studies have mostly focused on the macromechanical properties of paper but lack micro theory based on paper fiber networks. We present a comprehensive experimental and computational study on the mechanical properties of fibers and fiber networks under the influence of microstructure. A beam-spring model was established based on a beam-fiber network to simulate the behavior of fiber networks. Simulations were performed to demonstrate the influence of fiber microstructural parameters such as fiber bond strength, stiffness, failure strength, size, and network density on mechanical features. Mechanical experiments verified that the fiber bond strength had a greater influence on the paper properties than did the fiber strength. This result is highly consistent with that of the model. All the simulations were validated by experimental measurements. Finally, we provided computational insights into the interfiber bond damage pattern with respect to different fiber microlevels and demonstrated that the proposed beam-spring model can be used to predict the response of fiber networks of paper materials. The above research can be used to optimize the formulation, process, and treatment of paper to meet specific application needs.
Spilled Wine, Spilled Blood: Spilling the Secrets of the Covered Cup from the Chungul Kurgan
Authors: Woodfin Warren T.
Published: August 2024

The covered cup, discovered in 1981 during the salvage excavation of a burial mound in southern Ukraine, is an impressive example of secular metalwork from around 1200. The cup’s interior contains a cast silver-gilt lion and a hitherto undetected siphon mechanism, making it one of the first preserved automata from Europe and the earliest known Western medieval example of this type of trick vessel. The cup from the Chungul Kurgan helps to clarify the probable operation of the chantepleure illustrated in the sketchbook of Villard de Honnecourt and sheds light on possible other medieval automata. The cup’s presence in the same burial with other works from the same approximate date and region suggests historical circumstances that might have resulted in its burial with a nomadic leader in the steppe.