bluuebunny/crossref_metadata_2025 huggingface dataset:
Half-metallicity feature within Ag-doped zincblende WGe alloys: DFT+U insights
Authors: Nasri Djazia , Riane Houaria , Monir Mohammed El Amine , Al-Maaitah Ibtisam F. , Al-Maaitah A. F. , Laref Amel , Baltach Hadj , Reguig Abdelkarim Bendoukha
Published: May 2023

By employing the full-potential linearized augmented plane wave (FP-L/APW[Formula: see text]lo) technique based on density functional theory (DFT), the structural, electronic and magnetic properties of zincblende W[Formula: see text]AgxGe alloys ([Formula: see text] and 0.25) are scrutinized thoroughly. Based on the generalized gradient approximation (GGA), the exchange-correlation energy functional is included in the current simulation. For computing the structural features, the GGA approximation is applied, whereas GGA, [Formula: see text] and the modified Burke–Johnson of [Formula: see text] (TB-mBJ-[Formula: see text] approximations are incorporated to perform the calculations of electronic and magnetic behaviors of these alloys. The structural analysis of the alloys indicated that the total energy of the W[Formula: see text]Ag[Formula: see text]Ge alloy was favorable in the ferromagnetic ground state. The spin-polarized electronic structure shows the half-metallic behavior of the W[Formula: see text]Ag[Formula: see text]Ge alloy, while the WGe compound is identified as a metal. The magnetic results obtained from the half-metallic W[Formula: see text]Ag[Formula: see text]Ge alloy increasingly support the full half-metallicity of this compound because an integer value is acquired for the total magnetic moment. The strong hybridization between 4p-Ge and 3d-W states brings forth weak local magnetic moments at the nonmagnetic Ge atomic sites and reduces the local magnetic moment of the W atomic sites from its free space charge.
Circinus: Fast Redundancy-Reduced Subgraph Matching
Authors: Jin Tatiana , Li Boyang , Li Yichao , Zhou Qihui , Ma Qianli , Zhao Yunjian , Chen Hongzhi , Cheng James
Published: May 2023

Subgraph matching is one of the most important problems in graph analytics. Many algorithms and systems have been proposed for subgraph matching. Most of these works follow Ullmann's backtracking approach as it is memory-efficient in handling an explosive number of intermediate matching results. However, they have largely overlooked an intrinsic problem of backtracking, namely repeated computation, which contributes to a large portion of the heavy computation in subgraph matching. This paper proposes a subgraph matching system, Circinus, which enables effective computation sharing by a new compression-based backtracking method. Our extensive experiments show that Circinus significantly reduces repeated computation, which transfers to up to several orders of magnitude performance improvement.
Efficient Estimation of Pairwise Effective Resistance
Authors: Yang Renchi , Tang Jing
Published: May 2023

Given an undirected graph G, the effective resistance r(s,t) measures the dissimilarity of node pair s,t in G, which finds numerous applications in real-world problems, such as recommender systems, combinatorial optimization, molecular chemistry, and electric power networks. Existing techniques towards pairwise effective resistance estimation either trade approximation guarantees for practical efficiency, or vice versa. In particular, the state-of-the-art solution is based on a multitude of Monte Carlo random walks, rendering it rather inefficient in practice, especially on large graphs.

Motivated by this, this paper first presents an improved Monte Carlo approach, AMC, which reduces both the length and amount of random walks required without degrading the theoretical accuracy guarantee, through careful theoretical analysis and an adaptive sampling scheme. Further, we develop a greedy approach, GEER, which combines AMC with sparse matrix-vector multiplications in an optimized and non-trivial way. GEER offers significantly improved practical efficiency over AMC without compromising its asymptotic performance and accuracy guarantees. Extensive experiments on multiple benchmark datasets reveal that GEER is orders of magnitude faster than the state of the art in terms of computational time when achieving the same accuracy.
Transaction Scheduling: From Conflicts to Runtime Conflicts
Authors: Cao Yang , Fan Wenfei , Ou Weijie , Xie Rui , Zhao Wenyue
Published: May 2023

This paper studies how to improve the performance of main memory multicore OLTP systems for executing transactions with conflicts. A promising approach is to partition transaction workloads into mutually conflict-free clusters, and distribute the clusters to different cores for concurrent execution. We show that if transactions in each cluster are properly scheduled, transactions that are traditionally considered conflicting can be executed without conflicts at runtime. In light of this, we propose to schedule transactions and reduce runtime conflicts, instead of partitioning based on the conventional notion of conflicts. We formulate the transaction scheduling problem to minimize runtime conflicts, and show that the problem is NP-complete. This said, we develop an efficient scheduling algorithm to improve parallelism. Moreover, for transactions that are not packed in batches, we show that runtime conflict analysis also helps reduce conflict penalties, by proposing a proactive deferring method. Using standard and enhanced benchmarks, we show that on average our scheduling and proactive deferring methods improve the throughput of existing partitioners and concurrency control protocols by 131% and 109%, respectively, up to 294% and 152%.
Ground Truth Inference for Weakly Supervised Entity Matching
Authors: Wu Renzhi , Bendeck Alexander , Chu Xu , He Yeye
Published: May 2023

Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine learning (ML) models currently achieve state-of-the-art matching performance; however, they require a large number of labeled examples, which are often expensive or infeasible to obtain. This has inspired us to approach data labeling for EM using weak supervision. In particular, we use the labeling function abstraction popularized by Snorkel, where each labeling function (LF) is a user-provided program that can generate many noisy match/non-match labels quickly and cheaply. Given a set of user-written LFs, the quality of data labeling depends on a labeling model to accurately infer the ground-truth labels. In this work, we first propose a simple but powerful labeling model for general weak supervision tasks. Then, we tailor the labeling model specifically to the task of entity matching by considering the EM-specific transitivity property.

The general form of our labeling model is simple while substantially outperforming the best existing method across ten general weak supervision datasets. To tailor the labeling model for EM, we formulate an approach to ensure that the final predictions of the labeling model satisfy the transitivity property required in EM, utilizing an exact solution where possible and an ML-based approximation in remaining cases. On two single-table and nine two-table real-world EM datasets, we show that our labeling model results in a 9% higher F1 score on average than the best existing method. We also show that a deep learning EM end model (DeepMatcher) trained on labels generated from our weak supervision approach is comparable to an end model trained using tens of thousands of ground-truth labels, demonstrating that our approach can significantly reduce the labeling efforts required in EM.
Raster Intervals: An Approximation Technique for Polygon Intersection Joins
Authors: Georgiadis Thanasis , Mamoulis Nikos
Published: May 2023

Many data science applications, most notably Geographic Information Systems, require the computation of spatial joins between large object collections. The objective is to find pairs of objects that intersect, i.e., share at least one common point. The intersection test is very expensive especially for polygonal objects. Therefore, the objects are typically approximated by their minimum bounding rectangles (MBRs) and the join is performed in two steps. In the filter step, all pairs of objects whose MBRs intersect are identified as candidates; in the refinement step, each of the candidate pairs is verified for intersection. The refinement step has been shown notoriously expensive, especially for polygon-polygon joins, constituting the bottleneck of the entire process. We propose a novel approximation technique for polygons, which (i) rasterizes them using a fine grid, (ii) models groups of nearby cells that intersect a polygon as an interval, and (iii) encodes each interval by a bitstring that captures the overlap of each cell in it with the polygon. We also propose an efficient intermediate filter, which is applied on the object approximations before the refinement step, to avoid it for numerous object pairs. Via experimentation with real data, we show that the end-to-end spatial join cost can be reduced by up to one order of magnitude with the help of our filter and by at least three times compared to using alternative intermediate filters.
FactorJoin: A New Cardinality Estimation Framework for Join Queries
Authors: Wu Ziniu , Negi Parimarjan , Alizadeh Mohammad , Kraska Tim , Madden Samuel
Published: May 2023

Cardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries. They either rely on simplified assumptions leading to ineffective cardinality estimates or build large models to understand the complicated data distributions, leading to long planning times and a lack of generalizability across queries.

In this paper, we propose a new framework FactorJoin for estimating join queries. FactorJoin combines the idea behind the classical join-histogram method to efficiently handle joins with the learning-based methods to accurately capture attribute correlation Specifically, FactorJoin scans every table in a DB and builds single-table conditional distributions during an offline preparation phase. When a join query comes, FactorJoin translates it into a factor graph model over the learned distributions to effectively and efficiently estimate its cardinality.

Unlike existing learning-based methods, FactorJoin does not need to de-normalize joins upfront or require executed query workloads to train the model. Since it only relies on single-table statistics, FactorJoin has a small space overhead and is extremely easy to train and maintain. In our evaluation, FactorJoin can produce more effective estimates than the previous state-of-the-art learning-based methods, with 40x less estimation latency, 100x smaller model size, and 100x faster training speed at comparable or better accuracy. In addition, FactorJoin can estimate 10,000 sub-plan queries within one second to optimize the query plan, which is very close to the traditional cardinality estimators in commercial DBMS.
MRVs: Enforcing Numeric Invariants in Parallel Updates to Hotspots with Randomized Splitting
Authors: Faria Nuno , Pereira José
Published: May 2023

Performance of transactional systems is degraded by update hotspots as conflicts lead to waiting and wasted work. This is particularly challenging in emerging large-scale database systems, as latency increases the probability of conflicts, state-of-the-art lock-based mitigations are not available, and most alternatives provide only weak consistency and cannot enforce lower bound invariants. We address this challenge with Multi-Record Values (MRVs), a technique that can be layered on existing database systems and that uses randomization to split and access numeric values in multiple records such that the probability of conflict can be made arbitrarily small. The only coordination needed is the underlying transactional system, meaning it retains existing isolation guarantees. The proposal is tested on five different systems ranging from DBx1000 (scale-up) to MySQL GR and a cloud-native NewSQL system (scale-out). The experiments explore design and configuration trade-offs and, with the TPC-C and STAMP Vacation benchmarks, demonstrate improved throughput and reduced abort rates when compared to alternatives.
A Universal Question-Answering Platform for Knowledge Graphs
Authors: Omar Reham , Dhall Ishika , Kalnis Panos , Mansour Essam
Published: May 2023

Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well-formed SPARQL query requires information about the graph structure and the exact URIs of its components, which is impractical for the average user. Question answering (QA) systems assist by translating natural language questions to SPARQL. Existing QA systems are typically based on application-specific human-curated rules, or require prior information, expensive pre-processing and model adaptation for each targeted KG. Therefore, they are hard to generalize to a broad set of applications and KGs. In this paper, we propose KGQAn, a universal QA system that does not need to be tailored to each target KG. Instead of curated rules, KGQAn introduces a novel formalization of question understanding as a text generation problem to convert a question into an intermediate abstract representation via a neural sequence-to-sequence model. We also develop a just-in-time linker that maps at query time the abstract representation to a SPARQL query for a specific KG, using only the publicly accessible APIs and the existing indices of the RDF store, without requiring any pre-processing. Our experiments with several real KGs demonstrate that KGQAn is easily deployed and outperforms by a large margin the state-of-the-art in terms of quality of answers and processing time, especially for arbitrary KGs, unseen during the training.
Robust and Transferable Log-based Anomaly Detection
Authors: Jia Peng , Cai Shaofeng , Ooi Beng Chin , Wang Pinghui , Xiong Yiyuan
Published: May 2023

Log messages provide a valuable source of runtime information for ensuring the safety and consistency of systems. Recently, many machine learning and deep learning methods have been proposed to automatically detect anomalous log messages, obviating the need for manual detection by experts. However, we find that in practice, the effectiveness of existing learning-based methods is severely affected by incomplete information and distribution shift. Specifically, each log message can actually be parsed into a fixed number of key information fields, while existing methods analyze log messages using only the log event information and ignore other useful information fields that can be critical to anomaly detection. Further, the distribution of real-world log messages changes continuously due to the dynamic nature of the runtime environment and thus, a detection model conventionally trained based on the unrealistic i.i.d. assumption may not provide the expected and consistent performance.

In this paper, we present a robust and transferable anomaly detection framework RT-Log to address the above problems. To perform a comprehensive analysis of log messages, we introduce an adaptive relation modeling technique, which captures feature interactions among log information fields selectively and dynamically for effective and interpretable log representations. To establish its robustness and transferability, we propose a general environment generalization technique for learning the environment invariant representations that can generalize across different runtime environments. We evaluate the anomaly detection performance of RT-Log on large real-world datasets. Extensive experimental results demonstrate that RT-Log consistently outperforms state-of-the-art methods by a significant margin under different settings.
Discovering Top-k Rules using Subjective and Objective Criteria
Authors: Fan Wenfei , Han Ziyan , Wang Yaoshu , Xie Min
Published: May 2023

This paper studies two questions about rule discovery. Can we characterize the usefulness of rules using quantitative criteria? How can we discover rules using those criteria? As a testbed, we consider entity enhancing rules (REEs), which subsume common association rules and data quality rules as special cases. We characterize REEs using a bi-criteria model, with both objective measures such as support and confidence, and subjective measures for the user's needs; we learn the subjective measure and the weight vectors via active learning. Based on the bi-criteria model, we develop a top-k algorithm to discover top-ranked REEs, and an any-time algorithm for successive discovery via lazy evaluation. We parallelize these algorithms such that they guarantee to reduce runtime when more processors are used. Using real-life and synthetic datasets, we show that the algorithms are able to find top-ranked rules and speed up conventional rule-discovery methods by 134X on average.
Discovering Similarity Inclusion Dependencies
Authors: Kaminsky Youri , Pena Eduardo H. M. , Naumann Felix
Published: May 2023

Inclusion dependencies (INDs) are a well-known type of data dependency, specifying that the values of one column are contained in those of another column. INDs can be used for various purposes, such as foreign-key candidate selection or join partner discovery. The traditional notion of INDs is based on clean data, where the dependencies hold without exceptions. Unfortunately, data often contain errors, preventing otherwise valid INDs from being discovered. A typical response to this problem is to relax the dependency definition using a similarity measure to account for minor data errors, such as typos or different formatting. While this relaxation is known for functional dependencies, for inclusion dependencies no such relaxation has been defined.

We formally introduce similarity inclusion dependencies, which relax the inclusion by demanding the existence only of sufficiently similar values. Similarity inclusion dependencies can fulfill traditional IND use cases, such as foreign-key candidate discovery, even in the presence of dirty data. We present Sawfish, the first algorithm to discover all similarity inclusion dependencies in a given dataset efficiently. Our algorithm combines approaches for the discovery of traditional INDs and string similarity joins with a novel sliding-window approach and lazy candidate validation. Our experimental evaluation shows that Sawfish can outperform a baseline by a factor of up to 6.5.
Maximal Defective Clique Enumeration
Authors: Dai Qiangqiang , Li Rong-Hua , Liao Meihao , Wang Guoren
Published: May 2023

Maximal clique enumeration is a fundamental operator in graph analysis. The model of clique, however, is typically too restrictive for real-world applications as it requires an edge for every pair of vertices. To remedy this restriction, practical graph analysis applications often resort to find relaxed cliques as alternatives. In this work, we investigate a notable relaxed clique model, called s-defective clique, which allows at most s edges to be missing. Similar to the complexity of maximal clique enumeration, the problem of enumerating all maximal s-defective cliques is also NP-hard. To solve this problem, we first develop a new polynomial-delay algorithm based on a carefully-designed reverse search technique, which can output two consecutive results within polynomial time. To achieve better practical efficiency, we propose a branch-and-bound algorithm with a novel pivoting technique. We prove that the time complexity of this algorithm depends only on O(α_sn) or O(αsδ) when using a degeneracy ordering optimization, where αs is a positive real number strictly less than 2, and δ (δ 0). We also develop several new pruning techniques to further improve the efficiency of our branch-and-bound algorithm to enumerate all relatively-large maximal s-defective cliques. In addition, we further generalize our pivot-based branch-and-bound algorithm to enumerate all maximal subgraphs satisfying a hereditary property. Here we call a graph meeting the hereditary property if all its subgraphs have the same property as itself. Finally, extensive experiments on 11 datasets demonstrate the efficiency, effectiveness, and scalability of the proposed solutions.
Efficient Biclique Counting in Large Bipartite Graphs
Authors: Ye Xiaowei , Li Rong-Hua , Dai Qiangqiang , Qin Hongchao , Wang Guoren
Published: May 2023

A (p,q)-biclique is a complete subgraph (X,Y) that |X|=p, |Y|=q. Counting (p,q)-bicliques in bipartite graphs is an important operator for many bipartite graph analysis applications. However, getting the count of (p,q)-bicliques for large p and q (e.g., p,q ≥ 10) is extremely difficult, because the number of (p,q)-bicliques increases exponentially with respect to p and q. The state-of-the-art algorithm for this problem is based on the (p,q)-biclique enumeration technique which is often costly due to the exponential blowup in the enumeration space of (p,q)-bicliques. To overcome this problem, we first propose a novel exact algorithm, called EPivoter, based on a newly-developed edge-pivoting technique. The striking feature of EPivoter is that it can count (p,q)-bicliques for all pairs of (p,q) using a combinatorial technique, instead of exhaustively enumerating all (p,q)-bicliques. Second, we propose a novel dynamic programming (DP) based h-zigzag sampling technique to provably approximate the count of the (p,q)-bicliques for all pairs of (p,q), where an h-zigzag is an ordered simple path in G with length 2h-1 (h = min{p,q}). We show that our DP-based sampling technique is very efficient. Third, to further improve the efficiency, we also propose a hybrid framework that integrates both the exact EPivoter algorithm and sampling-based algorithms. Extensive experiments on 7 real-world graphs show that our algorithms are several orders of magnitude faster than the state-of-the-art algorithm.
Managing Conflicting Interests of Stakeholders in Influencer Marketing
Authors: Huang Shixun , Gan Junhao , Bao Zhifeng , Lin Wenqing
Published: May 2023

A successful campaign should be able to attract investment from the brand, and meanwhile manage the conflicting interests in the campaign cost between the brand and the influencers. As such, the agency between these two stakeholders plays a vital role. Motivated by the above, we stand in the agency's shoes to formulate an interesting yet practical problem, namely Profit Divergence Minimization in Investment-Persuasive Influencer Marketing Campaign (PDMIC). This problem aims to (i) minimize the divergence of the actual hiring prices from the asking prices of the influencers and meanwhile (ii) maintain the attractiveness of the pricing scheme for the influencers to the brand. We show that this problem is NP-hard. To mitigate the challenge of the extremely large searching space of the hiring prices of the influencers, we solve this problem by firstly considering a restrictive searching sub-space and then gradually expanding the searching sub-space to the whole space in the end (specifically, from binary price choices to a set of integer prices and then to any price in the feasible price range). We propose effective yet efficient approximate algorithms for solving the problem in each of these settings. Extensive experiments demonstrate the superiority of our methods.
Time Series Data Validity
Authors: Su Yunxiang , Gong Yikun , Song Shaoxu
Published: May 2023

As a key step of data preparation, it is always necessary to first assert the quality of data before conducting any data application. Given a set of constraints, the validity measure evaluates the degree of data meeting the constraints, e.g., whether the values are in the specified range or fluctuate drastically over time in a series. It is worth noting that simply counting all the data points in violation to the constraints may over claim the data validity issue. Following the minimum change criteria in data repairing, we propose to study the minimum number of data points that need to be changed in order to satisfy the constraints, or equivalently, the maximum rate of data that can be reserved without change, as the validity measure. To our best knowledge, this is the first study on defining and evaluating time series data validity. We devise algorithms for computing the validity measure in quadratic time and linear space. Remarkably, the validity measure has been deployed and included as a function in SQL statements, in Apache IoTDB, an open-source time series database. The algorithm fully adapts to the LSM-based storage of time series in multiple segments. Extensive experiments over 8 real-world datasets show up to 4 orders of magnitude improvement in time cost compared to the related method SCREEN.
ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale
Authors: Liu Kaiqi , Tong Panrong , Li Mo , Wu Yue , Huang Jianqiang
Published: May 2023

Data scientists and researchers utilize enormous spatio-temporal data and build machine learning models to solve practical problems in diverse domains including intelligent transportation, urban planning, epidemic prediction, and many more. Extracting application-specific features from big spatio-temporal data poses system requirements of heterogeneous data support, efficient and scalable computing over spatial and temporal dimensions, as well as a user-friendly programming interface. This paper presents ST4ML, a distributed spatio-temporal data processing system to support scalable machine-learning-oriented applications. We propose a three-stage pipelining computing framework, namely "selection-conversion-extraction" to abstract the distributed computing flow and implement it based on Apache Spark. To the best of our knowledge, ST4ML is the first of its kind to realize our design considerations. Extensive experiments with real-world datasets evidence that ST4ML outperforms straightforward extensions of existing ST data processing systems by up to an order of magnitude. ST4ML is open-sourced at https://github.com/Panrong/st4ml.
RLS Side Channels: Investigating Leakage of Row-Level Security Protected Data Through Query Execution Time
Authors: Dar Chen , Hershcovitch Moshik , Morrison Adam
Published: May 2023

Many modern use cases of relational databases involve multi-tenancy. To allow a tenant to only access its data, relational database systems (RDBMSs) introduced row-level security (RLS). RLS enables specifying per-row access controls, which the database enforces by rewriting tenant queries to add an RLS policy filter that filters out rows the tenant is not allowed to view. Unfortunately, while RLS blocks queries from returning unauthorized data, side-effects of query execution can form a side-channel that leaks information about such secret data.

This paper investigates how RLS query execution time can leak information about rows that the querying tenant is restricted from viewing. We show that in PostgreSQL and SQL Server, an attacker can craft index-using queries to learn whether a value they are not authorized to view exists in an RLS-protected table, and in some cases, how many times such a value exists in the table. Our attack succeeds in a realistic cloud setting: we successfully attack managed PostgreSQL and SQL Server database instances on AWS from virtual machines in the same and different data centers.

To block the RLS time side-channel, we design a data-oblivious query scheme for the case of unique keys. We also analyze the trade-offs created by the data-oblivious approach for non-unique keys.

To facilitate the evaluation of RLS attacks and defenses, we introduce a benchmark that supports multi-tenancy and RLS, which are not supported by established benchmarks such as YCSB. We implement our solution in PostgreSQL and show that it achieves security with minimal performance impact.
Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware
Authors: Schönberger Manuel , Scherzinger Stefanie , Mauerer Wolfgang
Published: May 2023

The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for quantum algorithms. We present the first quantum implementation of join ordering, one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes conforming to current quantum annealers. Yet, we also confirm that limits of early-stage technology are quickly reached.

Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future QPUs, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and problem size.

In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.
Grep: A Graph Learning Based Database Partitioning System
Authors: Zhou Xuanhe , Li Guoliang , Feng Jianhua , Liu Luyang , Guo Wei
Published: May 2023

Database partitioning is a fundamental but challenging task in distributed databases, which selects specific columns as a partitioning key for each table and uses the partitioning key to allocate the table data into different compute nodes in order to maximize the performance. However, this problem is NP-hard and existing distributed databases require users to manually specify the partitioning keys, which may cause potential performance degradation. Although reinforcement learning based methods have been proposed, they have several limitations. First, they do not capture the complex data distributions and query access patterns, and thus involve high computation cost across different compute nodes to answer a query. Second, they involve an expensive step to repetitively partition the data into different compute nodes in order to train a learned key-selection model, which is a waste of time and resources. To address these limitations, we propose a practical learned database partitioning system Grep. We first adopt a graph model to encode data and query features, where vertices are columns, edges are query relations, and the weights of columns are computed based on the localized graph structures (e.g., data diversity, joined columns). We then utilize graph neural networks to embed the partitioning factors into embedding vectors in order to capture the data and query correlations. Next we propose a key-selection model to select appropriate partitioning keys based on the graph model. Finally, we propose an evaluation model to estimate the partitioning performance without actually partitioning the database. We have implemented Grep in a commercial distributed database, and experiments show the effectiveness of our system (e.g., 68% higher throughput for 30K queries in a real banking scenario).
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks
Authors: Zeighami Sepanta , Shahabi Cyrus , Sharan Vatsal
Published: May 2023

Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied answering RAQs using machine learning (ML) models, where a model of the data is learned to answer the queries. However, there is no theoretical understanding of why and when the ML based approaches perform well. Furthermore, since the ML approaches model the data, they fail to capitalize on any query specific information to improve performance in practice. In this paper, we focus on modeling "queries" rather than data and train neural networks to learn the query answers. This change of focus allows us to theoretically study our ML approach to provide a distribution and query dependent error bound for neural networks when answering RAQs. We confirm our theoretical results by developing NeuroSketch, a neural network framework to answer RAQs in practice. Extensive experimental study on real-world, TPC-benchmark and synthetic datasets show that NeuroSketch answers RAQs multiple orders of magnitude faster than state-of-the-art and with better accuracy.
Graph Learning for Interactive Threat Detection in Heterogeneous Smart Home Rule Data
Authors: Wang Guangjing , Ivanov Nikolay , Chen Bocheng , Wang Qi , Nguyen ThanhVu , Yan Qiben
Published: May 2023

The interactions among automation configuration rule data have led to undesired and insecure issues in smart homes, which are known as interactive threats. Most existing solutions use program analysis to identify interactive threats among automation rules, which is not suitable for closed-source platforms. Meanwhile, security policy-based solutions suffer from low detection accuracy because the pre-defined security policies in a single platform can hardly cover diverse interactive threat types across heterogeneous platforms. In this paper, we propose Glint, the first graph learning-based system for interactive threat detection in smart homes. We design a multi-scale graph representation learning model, called ITGNN, for both homogeneous and heterogeneous interaction graph pattern learning. To facilitate graph learning, we build large interaction graph training datasets by multi-domain data fusion from five different platforms. Moreover, Glint detects drifting samples with contrastive learning and improves the generalization ability with transfer learning across heterogeneous platforms. Our evaluation shows that Glint achieves 95.5% accuracy in detecting interactive threats across the five platforms. Besides, we examine a set of user-designed blueprints in the Home Assistant platform and reveal four new types of real-world interactive threats, called "action block", "action ablation", "trigger intake", and "condition duplicate", which are cross-platform interactive threats captured by Glint.
Kepler: Robust Learning for Parametric Query Optimization
Authors: Doshi Lyric , Zhuang Vincent , Jain Gaurav , Marcus Ryan , Huang Haoyu , Altinbüken Deniz , Brevdo Eugene , Fraser Campbell
Published: May 2023

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training anML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.
Dumpy: A Compact and Adaptive Index for Large Data Series Collections
Authors: Wang Zeyu , Wang Qitong , Wang Peng , Palpanas Themis , Wang Wei
Published: May 2023

Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and approximate similarity search, with approximate search providing high-quality results within milliseconds, which makes it very attractive for certain modern applications. Reducing the pre-processing (i.e., index building) time and improving the accuracy of search results are two major challenges. DSTree and the iSAX index family are state-of-the-art solutions for this problem. However, DSTree suffers from long index building times, while iSAX suffers from low search accuracy. In this paper, we identify two problems of the iSAX index family that adversely affect the overall performance. First, we observe the presence of a proximity-compactness trade-off related to the index structure design (i.e., the node fanout degree), significantly limiting the efficiency and accuracy of the resulting index. Second, a skewed data distribution will negatively affect the performance of iSAX. To overcome these problems, we propose Dumpy, an index that employs a novel multi-ary data structure with an adaptive node splitting algorithm and an efficient building workflow. Furthermore, we devise Dumpy-Fuzzy as a variant of Dumpy which further improves search accuracy by proper duplication of series. Experiments with a variety of large, real datasets demonstrate that the Dumpy solutions achieve considerably better efficiency, scalability and search accuracy than its competitors.
SAT-boosted Tabu Search for Coloring Massive Graphs
Authors: Schidler André , Szeider Stefan
Published: May 2023

Graph coloring is the problem of coloring the vertices of a graph with as few colors as possible, avoiding monochromatic edges. It is one of the most fundamental NP-hard computational problems. For decades researchers have developed exact and heuristic methods for graph coloring. While methods based on propositional satisfiability (SAT) feature prominently among these exact methods, the encoding size is prohibitive for large graphs. For such graphs, heuristic methods have been proposed, with tabu search among the most successful ones.

In this article, we enhance tabu search for graph coloring within the SAT-based local improvement (SLIM) framework. Our hybrid algorithm incrementally improves a candidate solution by repeatedly selecting small subgraphs and coloring them optimally with a SAT solver. This approach scales to dense graphs with several hundred thousand vertices and over 1.5 billion edges. Our experimental evaluation shows that our hybrid algorithm beats state-of-the-art methods on large dense graphs.
Health Impact Assessment of Short-Term Exposure to Particulate Matter (PM10) in Northern Thailand
Authors: Ngamsang Pakaporn , Amnuaylojaroen Teerachai , Parasin Nichapa , Pimonsree Sittichai
Published: May 2023

In northern Thailand, in recent decades, particulate pollution from the burning of biomass has become a serious issue with toxicological implications for human health, especially during the winter months of January to April. The purpose of this study was to explore short-term exposure to particulate matter (PM10) in northern Thailand. The high PM10 concentration in 2012 was used as a case study. We used the EPA’s Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) for the health impact assessment, along with ground-based measurement data. The annual average observed PM10 concentration was in the range of 43–61 μg/m3, with a maximum observed PM10 concentration of 300 μg/m3 in March. We then assessed the impacts of PM10 exposure in northern Thailand. When the PM10 concentration was reduced to 120 μg/m3, the undesirable effects on respiratory mortality decreased by 5%–11%. When the concentration of PM10 was reduced to 45 μg/m3, the deleterious effects on respiratory mortality decreased by 11–30%. In conclusion, adherence to the WHO-AQG, particularly for PM10 (45 μg/m3), tends to result in considerable reductions in respiratory disease mortality in northern Thailand.
Analysis of E-Commerce Marketing Strategy Based on Xgboost Algorithm
Authors: Chen Hong , Wan Wei
Published: May 2023

With the development and popularization of multimedia technology, people’s communication and information dissemination become more and more convenient. The use of new media technology in marketing accelerates the enterprise’s adaptation to market changes. With the help of new marketing technology, the quality of marketing can be improved and the effect of marketing can be maximized. This paper reviews the current literature on e-commerce marketing and then analyzes the feasibility of precision marketing in e-commerce market in the new media era. In order to screen potential consumers and improve the success rate of precision marketing, this paper establishes a prediction model for precision marketing of bank credit cards. A public dataset selected in this paper contains the basic characteristics of the customer, such as age, gender, monthly income, monthly consumption, and whether the customer has agreed to process the credit card after personalized recommendation. After data cleaning and preprocessing, XGboost algorithm is used to predict whether the customer will process the credit card. The calculation shows that the XGboost algorithm can still maintain a high accuracy when dealing with fewer characteristic variables. This study is helpful to better predict users’ consumption intention, further explore potential customers, and improve the success rate of precision marketing.
Waste Coir Nanofiller Fused Gallus-Gallus Fibres Reinforced PMC
Authors: Vijayan V. , Sathish T. , Saravanan R. , Kumar Kuldeep , Jadhav Ganesh K. , Sharun V. , Elangovan R. , Chellamuthu K. , Singh Mandeep , Teklemariam Aklilu
Published: May 2023

This research aims to increase the utility of globally and abundantly available waste natural fibres of Gallus-Gallus fibres coir waste from mattress and car seat manufacturing factories. The composite samples were prepared with a rally round of polyester resin of grade GP500 bio-epoxy by synthesizing specially treated Gallus-Gallus fibres selectively used for reinforcement and characterizing them through static and dynamic mechanical analyses to identify their wide range of applicability. The Gallus-Gallus fibres are preprocessed with sodium oxidative and a half per cent of potassium manganate (VII) chemical solution. The selective use includes 5 mm, 10 mm, 15 mm, and 20 mm length of the Gallus-Gallus fibre, and the quantity of reinforcement was 10%, 20%, and 30%. Five alternate layers of matrix and fibres, with vertical and horizontal orientation, are considered; 12 different samples of Gallus-Gallus fibres reinforced polyester polymer composites and a neat polyester composites were synthesized and characterized for moisture absorbability, tensile strength, tensile modulus, flexural strength, flexural modulus, wear resistance, and outperformed composites were included in microscopic examination and dynamic Mmchanical analysis. The interesting results are the preferred resin, supported for good surface finish, interface bonding, and totally in the enhancement of Composite properties. The composites are strong in tension (760.89 MPa) and sufficiently flexible (flexural modulus 5441.32 MPa), absorbed less moisture (5.8 g), high wear-resistant (least weight loss upon abrasion with a value of 0.1989 g), secured good results in dynamic analysis, and ensured homogeneous distribution of fibres in the matrix through a scanning electron microscopy image. The composites CPPC10, CPPC11, and CPPC12 performed well but composite CPPC12 outperformed.
Analyzing Trends in Demographic, Laboratory, Imaging, and Clinical Outcomes of ICU-Hospitalized COVID-19 Patients
Authors: Gholinataj Jelodar Mohsen , Rafieian Shahab , Allah Dini Azadeh , Khalaj Fatemeh , Zare Samira , Dehghanpour Hanieh , Mirzaei Samaneh
Published: May 2023

Background. COVID-19 has led to significant hospitalization and intensive care unit admission rates. The demographic parameters of COVID-19 patients, such as age, underlying illnesses, and clinical symptoms, substantially influence the incidence and mortality of these individuals. The current study examined the clinical and demographic characteristics of COVID-19 intensive care unit (ICU) patients in Yazd, Iran. Methods. The descriptive-analytical cross-sectional study was conducted on ICU patients with a positive RT-PCR test for coronavirus, admitted to the ICU in Yazd province, Iran, over 18 months. To this end, demographic, clinical, laboratory, and imaging data were collected. Moreover, patients were divided into good and worse clinical outcome groups based on their clinical outcomes. Subsequently, data analysis was performed at a 95% confidence interval (CI) using SPSS 26 software. Results. 391 patients with positive PCR were analyzed. The average age of the patients in the study was 63.59 ± 17.76, where 57.3% were male. On the high-resolution computed tomography (HRCT) scan, the mean lung involvement score was 14.03 ± 6.04, where alveolar consolidation (34%) and ground-glass opacity (25.6%) were the most prevalent type of lung involvement. The most common underlying illnesses in the study participants were hypertension (HTN) (41.4%), diabetes mellitus (DM) (39.9%), ischemic heart disease (IHD) (21%), and chronic kidney disease (CKD) (20.7%). In hospitalized patients, the rates of endotracheal intubation and mortality were 38.9% and 38.1%, respectively. Age, DM, HTN, dyslipidemia, CKD, cerebral vascular accident (CVA), cerebral hemorrhage, and cancer were reported to be significantly different between these two groups of patients, indicating an increase in the rate of intubation and mortality among these patients. Furthermore, the multivariate logistic regression analysis revealed that DM, HTN, CKD, CVA, neutrophil-to-lymphocyte ratio (NLR), the percentage of lung involvement, and initial O2 saturation significantly increase the mortality of ICU patients. Conclusion. Several features of COVID-19 patients influence the mortality in these individuals. According to the findings, early detection of this disease in people at high risk of death can prevent its progression and lower mortality rates.
The Association of the Cerebral Protection Strategy with Early Mortality and Postoperative Stroke in Acute Type A Aortic Dissection: A Systematic Review and Meta-Analysis
Authors: Mustonen Caius , Uimonen Mikko
Published: May 2023

Objective. The optimal cerebral protection strategy in acute type A aortic dissection (ATAAD) is still without a clear consensus. The purpose of this meta-analysis was to compare the outcome of different cerebral protection strategies on ATAAD patients. Materials and Methods. We conducted a systematic review including all studies concerning surgically managed ATAAD patients between 1.1.2010 and 28.2.2022 and reporting the use of cerebral protection strategies in three large databases (Pubmed, Cochrane library, and Scopus). The main outcome events were 30-day mortality and a postoperative stroke rate. The pooled event rates adjusted by age, gender, CPB duration, circulatory arrest duration, and total arch reconstruction rate were calculated. Results. Overall, 39 articles were included covering a total of 16, 876 ATAAD patients. The estimated adjusted pooled early mortality rate was 10.1% (95% confidence interval [CI] 9.1–11.3%) in the ACP group, 15.9% (13.3–18.9%) in the RCP group, and 11.6% (9.2–14.5%) in the HCA group. Compared to the RCP group, ACP and HCA demonstrated lower early mortality (RCP vs. ACP odds-ratio 1.66 [1.28–2.15],p < 0.001; RCP vs. HCA odds-ratio 1.45 [1.02–2.07],p = 0.039). The adjusted pooled stroke rate was 9.0% (8.3–9.8%) in the ACP group, 10.5% (9.3–11.7%) in the RCP group, and 9.1% (8.1–10.2%) in the HCA group. Conclusion. Early mortality might be more common in ATAAD patients treated with RCP compared to ACP and HCA. With regards to postoperative stroke, the results were inconclusive despite the trending inferiority of RCP compared to the other strategies.