bluuebunny/crossref_metadata_2025 huggingface dataset:
The Amplify Manifesto: Rewind, Replay, Reflect
Authors: Copeland Stacey , McGregor Hannah , Dusek Natalie
Published: November 2024

We are the Amplify Podcast Network, a research project working to develop sustainable models for producing, peer-reviewing, and publishing scholarly podcasts. The Amplify Manifesto aims to capture the spirit of our network: playful, experimental, and multi-voiced. As a radically political written form, the manifesto provides a creative ground to communicate a set of ideals, goals, and intentions with purpose. In revisiting the manifesto for Radio Doc Review, we unpack the construction of the manifesto as sound-first multimodal scholarship.
EDITORIAL: Subjectivity and Objectivity in Storytelling Podcasts
Authors: McHugh Siobhan
Published: November 2024

In this issue, storytelling podcasts and audio works from the US, UK, Australia and Canada receive in-depth critiques from expert reviewers in Latin America, Australia and the UK. The subjectivity-objectivity spectrum is one focus, along with ethics and aesthetics.
The View from Somewhere: A Review
Authors: Boynton Robert S.
Published: November 2024

Lewis Raven Wallace was fired from Marketplace for questioning the mainstream media's conception of journalistic neutrality. He developed his critique in his 2019 book, The View From Somewhere: Undoing the Myth of Journalistic Objectivity, a podcast of the same name, and in several ancillary products. Wallace concludes that “objectivity is a false ideal that upholds the status quo”, and news judgement has less to do with objective criteria than with “who controls the narrative, whose narratives matter, and how the appearance of mattering is created in a society rife with entrenched inequality”.
Bowraville and Phoebe's Fall: award-winning Australian podcasts from the media formerly known as print
Authors: Carlisle Wendy
Published: November 2024

Digital technology has democratised the audio storytelling space in a quite profound way. This article compares two major podcast investigations produced by established Australian newspaper mastheads: Bowraville by The Australian, and Phoebe’s Fall by The Age. Bowraville examines the unsolved murders of three Aboriginal children in the 1990s – all of whom came from the same small town. Phoebe’s Fall investigates the bizarre death in a garbage chute of a luxury Melbourne apartment building of 24-year-old Phoebe Handsjuk and her troubled relationship with her much older boyfriend.
In depicting what have been described as the three essential ingredients of a successful podcast – story, character, voice - these podcasts employ different approaches. Bowraville was structured chronologically, hosted by Dan Box cast as the sleuthing journo in true crime style. As co-hosts of Phoebe’s Fall, Richard Baker and Michael Bachelard deployed conversational banter, with episodes arranged thematically. Importantly, Bowraville had superior access to the main players in the drama and was strongly tethered against place. In contrast Phoebe’s Fall had limited access to the players and was tightly constrained by defamation laws.
For the audio journalist, the interview is partly a performance piece. Audio story telling exists as a series of dramatic events unfolding through compelling characters against a story arc. It has to have the appearance of sweeping simplicity. It needs scene-setting, relevant facts. It has to break across the listener like a good wave.
Partly as a result of Bowraville, legal changes are afoot. Phoebe's Fall left its mark, too. Public pressure has prompted a review of the Victorian Coroner’s Act, examining whether there ought to be more ways of revisiting a coronial finding. There’s never been a better time for long form audio investigative journalism.
Bibliometric analysis of scientific literature on the use of suppositories in the treatment of prostate cancer patients
Authors: Bushuieva I. V. , Parchenko M. V. , Maliuhina O. O.
Published: November 2024

Nowadays, cancer remains one of the main causes of death worldwide. Statistical data from various literary sources indicate that more than 130,000 people in Ukraine receive this diagnosis as a sentence every year. Cancer can affect any organs of the human body and, over time, the entire organism. In 2024, the agency of the World Health Organization on cancer and the International Agency of Cancer Research published the latest statistical estimates of this pathology in a global format, where prostate cancer is among the five most common oncological pathologies and takes the fourth place. Oncological diseases are one of the priority areas of healthcare development in accordance with the Order of the Ministry of Health of Ukraine No. 1832 dated 07.10.2022 “On Approval of Priority Areas of Healthcare Development for 2023–2025”. It was this fact that determined the choice of the direction of this research in the matter of finding information on the provision of pharmaceutical care, namely the use of a drug – a rectal suppository for the treatment of prostate cancer.
The aim of the work is to carry out a bibliometric analysis and further generalize the data of the scientific literature to study the prognostic value and features of the prescription of suppositories in the treatment of prostate cancer.
Materials and methods. The data search was conducted in the PubMed electronic database using the following key terms: “prostate cancer and suppositories”, “prostate cancer”. To monitor and analyze international scientific research, with the help of visualization tools and modern SciVal citation metrics using the online platform, bibliometric analysis can be performed. Using the VOSviewer functional program, bibliometric networks were constructed and visualized.
Results. A map of the relationships between the most used keywords on the topics of “prostate cancer” and “prostate cancer and suppositories”, grouped into clusters for the period from 1986 to 2024, was built. The clustering of the most used keywords on the subject of “prostate cancer” for the specified period is summarized. According to the bibliographic analysis, interest in the above topic was highest in 2014 and 2021.
Conclusions. According to the bibliographic analysis, the interest in the subject of the patients’ treatment with prostate cancer disease continues to grow from 1998 up to the present. The analysis of publication activity using the visualization tool of bibliometric networks VOSviewer for the period from 2014 to 2024 for the key words “prostate cancer” has shown the presence of 14 clusters. The largest cluster is devoted to modern methods of prostate cancer treatment, studies of gene rearrangement, target therapy, carcinogenesis.
Pharmacogenetic testing: current state of the issue
Authors: Kraidashenko O. V. , Kremzer O. O. , Samura T. O.
Published: November 2024

Aim. The purpose of this work is to emphasize the importance and practical benefits of pharmacogenetics in medicine, demonstrating how the integration of pharmacogenetics into clinical practice can improve treatment outcomes and enhance the safety of therapies.
Pharmacogenetics is a rapidly developing branch of pharmacology that studies how genetic variation affects an individual’s response to drugs. By understanding these genetic differences, healthcare providers can tailor drug therapy to maximize efficacy and minimize adverse drug reactions. The field aims to move away from the traditional one-size-fits-all approach and instead personalize medical treatment based on a person’s genetic makeup. The ability to predict a person’s response to medication based on their genetic profile has profound implications. For example, pharmacogenetic testing can identify patients at risk for life-threatening adverse drug reactions, such as drug-induced liver injury. This proactive approach can prevent adverse events, improving patient safety and the overall quality of care. In addition, pharmacogenetics aids in the development of new drugs by identifying genetic markers associated with drug responses, optimizing the drug development process, and reducing the time and costs associated with bringing new drugs to market. Key advances in pharmacogenetics include the identification of genetic polymorphisms in enzymes, such as cytochrome P450, that affect drug metabolism, thereby influencing both safety and efficacy. Pharmacogenetic testing allows doctors to predict the best drug and dosage for a patient, improving treatment outcomes and reducing the risk of adverse drug events.
Despite its potential, the integration of pharmacogenetics into clinical practice faces challenges, including a lack of reliable clinical evidence, inadequate physician education, and the need for a comprehensive health IT infrastructure to support the use of genetic data. Ethical and legal issues, such as patient privacy and the risk of genetic discrimination, also present significant obstacles. However, continued research, the development of genetic testing technologies, and interdisciplinary collaborations are paving the way for more widespread adoption of pharmacogenetics, which promises to significantly improve patient care and healthcare efficiency. Key resources such as PharmGKB, CPIC, and the NIH provide valuable information and guidance for clinicians, researchers, and students, helping bridge the gap between genetics research and clinical application.
Conclusions. Pharmacogenetics represents a significant advancement in personalized medicine, offering the potential to tailor drug therapies to individual genetic profiles. Although the field has made substantial progress, challenges remain in the form of insufficient clinical evidence, implementation barriers, and ethical concerns. Continued research and collaboration among stakeholders are essential to fully realize the benefits of pharmacogenetic testing in clinical practice.
Health-preserving physical culture and wellness competences as an important condition for ensuring and spreading a high level of public health (methodological aspects)
Authors: Hurieieva A. M. , Prykhodko V. V. , Sheviakov O. V. , Chernihivska S. A. , Vilianskyi V. M. , Cherepok O. O.
Published: November 2024

WHO indicates that the purpose of actions aimed at strengthening public health protection is to ensure conditions under which people can remain healthy, able to strengthen health and well-being, and prevent deterioration of health. However, in the basic documents, there is no place for health-preserving physical culture and health-improving competencies of a person in the system of general efforts.
The aim of the work is to reveal the need and place of health-preserving physical culture and wellness competencies of a person as a condition for ensuring and spreading a high level of public health in Ukraine.
Material and methods. The study was organized at the Dnipro State Academy of Physical Culture and Sports, the Ukrainian State University of Science and Technology, the National Technical University “Dnipro Polytechnic” (all in Dnipro) and the Zaporizhzhia State Medical and Pharmaceutical University. The used methods of theoretical research were: study and generalization of data from domestic and foreign official and literary sources, as well as abstraction, analysis and synthesis, induction and deduction.
Results. Conceptual principles and didactic technology of physical education of a non-professional physical education (NpPhE), with an emphasis on strengthening the general cultural component of the educational component “Physical education” in higher education, aimed at eliminating existing shortcomings. Provided with pedagogical technology, NpPhE contributes to the formation of the building blocks of one’s own health. And the system of effective pedagogical mechanisms of NpPhE is significantly different from the provision of information about health, which is the subject of modern valeology. In this context, the development of conceptual directions of creative valeology is relevant, as a means of supporting a person’s creativity in a safe effect on health. Namely, the formation in institutions of higher education of the builders of their own health.
Conclusions. A system of pedagogical mechanisms is proposed, which is significantly different from just providing information about human health as a benefit/harm of influences and actions, which is what valeology is focused on. This is a prerequisite for the formation and development of creative concepts of modern valeology based on the improvement of cultural and practical components of the educational component “Physical education” in the conditions of a higher education institution for specialists who take care of building their own health from specialties related to civilians. In conditions where the majority of their graduates are engaged not in physical, but intellectual work, the further implementation of approaches related to professional-applied physical training for professional activities is something that it is time to abandon.
GaussDB: A Cloud-Native Multi-Primary Database with Compute-Memory-Storage Disaggregation
Authors: Li Guoliang , Tian Wengang , Zhang Jinyu , Grosman Ronen , Liu Zongchao , Li Sihao
Published: November 2024

Cloud-native databases have been widely deployed due to high elasticity, high availability and low cost. However, most existing cloud-native databases do not support multiple writers and thus have limitations on write throughput and scalability. To alleviate this limitation, there is a need for multi-primary databases which provide high write throughput and high scalability.

In this paper, we present a cloud-native multi-primary database, GaussDB, which adopts a three layer (compute-memory-storage) disaggregation framework, where the compute layer is in charge of transaction processing, the memory layer is responsible for global buffer management and global lock management, and the storage layer is used for page and log persistence. To provide multi-primary capabilities, GaussDB logically partitions the pages to different compute nodes and then assigns the ownership of each page to a compute node. For each transaction posed to a compute node, if the compute node owns all relevant pages of this query, the compute node can process the query locally; otherwise, GaussDB transfers the ownership of relevant pages to this node. To capture data affinity and reduce page transmission costs, GaussDB designs a novel page placement and query routing method. To improve recovery performance, GaussDB employs a two-tier (memory-storage) checkpoint recovery method which uses memory checkpoints combined with on-demand page recovery to significantly improve recovery performance. We have implemented and deployed GaussDB internally at Huawei and with customers, and the results show that GaussDB achieves higher throughput, lower latency, and faster recovery than state-of-the-art baselines.
TDSQL: Tencent Distributed Database System
Authors: Chen Yuxing , Pan Anqun , Lei Hailin , Ye Anda , Han Shuo , Tang Yan , Lu Wei , Chai Yunpeng , Zhang Feng , Du Xiaoyong
Published: November 2024

Distributed databases have become indispensable in contemporary computing and data processing, owing to their pivotal role in ensuring high availability and scalability. They effectively cater to the requirements of data management and high-concurrency access. However, developing a distributed database system that is well-suited for diverse application scenarios, particularly for large-scale applications, presents several challenges. These challenges include ensuring data consistency and achieving high levels of performance.

This paper presents TDSQL, a distributed database system that prioritizes core design principles of distributed systems, including high availability, strong consistency, and scalability. In particular, TDSQL has achieved high performance through over a decade of practical experience and optimization in various modules, such as the kernel, synchronous replication, and transaction processing, in large-scale application scenarios. By conducting the TPC-C benchmark test, TDSQL demonstrated outstanding performance, achieving a throughput of 814 million tpmC across 1650 database nodes, with a jitter rate of less than 0.2%. This jitter rate is an order of magnitude lower than the standard required, showcasing the system's stability and reliability. During the 8-hour TPC-C standard stress test, TDSQL successfully completed over 860 billion transactions and processed 40 trillion order details, with zero forced rollbacks and zero data inconsistency.
Transparent Migration from Datastore to Firestore
Authors: Davisson Ed , Dickopp Tilo , Gay David , Karasuda Eric , Kesavan Ram , Yushprakh Vadim
Published: November 2024

Datastore was one of Google's first cloud databases, launched initially as part of App Engine, and built over Google's internal Megastore database system. Firestore was launched in 2019, both a re-implementation of Datastore over Google's Spanner database system and a new, mobile and web-friendly Firestore API. Spanner was chosen as the storage engine of Firestore in particular for technical reasons---it provides unrestricted transaction capabilities, strong consistency guarantees, and other improvements over Megastore.

To provide these improvements to all our customers, and simplify our overall system, a non-disruptive, zero-downtime migration was executed of all Datastore databases (stored in Megastore) to Firestore databases (stored in Spanner). This migration took a couple of years to design and plan, and about three to execute. This paper describes both the core engine for migrating databases, and various practical problems that were solved to make this journey successful. As of the writing of this paper, all (over one million) databases have been successfully migrated.
Apache TsFile: An IoT-Native Time Series File Format
Authors: Zhao Xin , Qiao Jialin , Huang Xiangdong , Wang Chen , Song Shaoxu , Wang Jianmin
Published: November 2024

The proliferation of the Internet of Things (IoT) has led to an exponential increase in time series data, distributed and applied in various contexts, demanding a dedicated storage solution. Based on our observations and analysis of IoT production systems, we have characterized 3 requirements for time series data: (1) a close association with devices and sensors, (2) continually synchronizing between cloud-edge, and (3) requiring the ability for high ingestion and low latency access on big volume data. Despite the growing trend, current time series database systems lack a standardized file format, and existing open file formats do not adequately leverage the unique characteristics of IoT time series data. In this paper, we introduce Apache TsFile, a specialized file format tailored for IoT time series data. TsFile organizes data by devices, creating indexes based on device-related information. Our experiments demonstrate the efficiency of TsFile in achieving high data ingestion rates, minimizing latency, and optimizing data compactness.
OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud
Authors: Zou Ding , Lu Wei , Zhu Zhibo , Lu Xingyu , Zhou Jun , Wang Xiaojin , Liu Kangyu , Wang Kefan , Sun Renen , Wang Haiqing
Published: November 2024

Autoscaling is a critical mechanism in cloud computing, enabling the autonomous adjustment of computing resources in response to dynamic workloads. This is particularly valuable for co-located, long-running applications with diverse workload patterns. The primary objective of autoscaling is to regulate resource utilization at a desired level, effectively balancing the need for resource optimization with the fulfillment of Service Level Objectives (SLOs). Many existing proactive autoscaling frameworks may encounter prediction deviations arising from the frequent fluctuations of cloud workloads. Reactive frameworks, on the other hand, rely on realtime system feedback, but their hysteretic nature could lead to violations of stringent SLOs. Hybrid frameworks, while prevalent, often feature independently functioning proactive and reactive modules, potentially leading to incompatibility and undermining the overall decision-making efficacy. In addressing these challenges, we propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module. The proactive module delivers reliable future workload predictions to the optimization module, while the reactive module offers a self-tuning estimator for real-time updates. By embedding a Model Predictive Control (MPC) mechanism and chance constraints into the optimization module, we further enhance its robustness. Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework, leading to over a 36% reduction in SLO violations compared to prevalent reactive, proactive, or hybrid autoscalers. Notably, OptScaler has been successfully deployed at Alipay, providing autoscaling support for the world-leading payment platform.
Differentially Private Stream Processing at Scale
Authors: Zhang Bing , Doroshenko Vadym , Kairouz Peter , Steinke Thomas , Thakurta Abhradeep , Ma Ziyin , Cohen Eidan , Apte Himani , Spacek Jodi
Published: November 2024

We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system -
Differential Privacy SQL Pipelines (DP-SQLP)
- is built using a streaming framework similar to Spark streaming, and is built on top of the Spanner database and the F1 query engine from Google.

Towards designing DP-SQLP we make both algorithmic and systemic advances, namely, we (i) design a novel (user-level) DP key selection algorithm that can operate on an unbounded set of possible keys, and can scale to one billion keys that users have contributed, (ii) design a preemptive execution scheme for DP key selection that avoids enumerating all the keys at each triggering time, and (iii) use algorithmic techniques from DP continual observation to release a continual DP histogram of user contributions to different keys over the stream length. We empirically demonstrate the efficacy by obtaining at least 16× reduction in error over meaningful baselines we consider. We implemented a streaming differentially private user impressions for Google Shopping with DP-SQLP. The streaming DP algorithms are further applied to Google Trends.
LLM for Data Management
Authors: Li Guoliang , Zhou Xuanhe , Zhao Xinyang
Published: November 2024

Machine learning techniques have been verified to be effective in optimizing data management systems and are widely researched in recent years. However, traditional small-sized ML models often struggle to generalize to new scenarios, and have limited context understanding ability (e.g., inputting discrete features only). The emergence of LLMs offers a promising solution to these challenges. LLMs have been trained over a vast number of scenarios and tasks and acquire human-competitive capabilities like context understanding and summarization, which can be highly beneficial for data management tasks (e.g., natural language based data analytics). In this tutorial, we present how to utilize LLMs to optimize data management systems and review new techniques for addressing these technical challenges, including hallucination of LLMs, high cost of interacting with LLMs, and low accuracy for processing complicated tasks. First, we discuss retrieval augmented generation (RAG) techniques to address the hallucination problem. Second, we present vector database techniques to improve the latency. Third, we present LLM agent techniques for processing complicated tasks by generating multi-round pipelines. We also showcase some real-world data management scenarios that can be well optimized by LLMs, including query rewrite, database diagnosis and data analytics. Finally, we summarize some open research challenges.
A Reproducible Tutorial on Reproducibility in Database Systems Research
Authors: Fischer Tim , Hirn Denis , Kul Gökhan
Published: November 2024

Reproducibility is a key aspect of the scientific method, and it is essential for building trust in the results of research. This tutorial aims to provide concrete guidance on how to leverage
containerized reproducibility
using Docker for database systems research.

In this tutorial, we present a step-by-step guide on how to prepare a Docker-based artifact for an experiment. We will cover topics such as Dockerfiles, Docker images, Docker Compose, automation using Python, Bash, and Make, and also artifact documentation and packaging best practices. The tutorial itself is a reproducible artifact, and we provide a public GitHub repository with all the code and examples used in the tutorial. This repository can serve as a starting point to prepare artifacts for experiments and publications.
Spatial Query Optimization With Learning
Authors: Zhang Xin , Eldawy Ahmed
Published: November 2024

Query optimization is a key component in database management systems (DBMS) and distributed data processing platforms. Recent research in the database community incorporated techniques from artificial intelligence to enhance query optimization. Various learning models have been extended and applied to the query optimization tasks, including query execution plan, query rewriting, and cost estimation. The tasks involved in query optimization differ based on the type of data being processed, such as relational data or spatial geometries. This tutorial reviews recent learning-based approaches for spatial query optimization tasks. We go over methods designed specifically for spatial data, as well as solutions proposed for high-dimensional data. Additionally, we present learning-based spatial indexing and spatial partitioning methods, which are also vital components in spatial data processing. We also identify several open research problems in these fields.
Spade: A Real-Time Fraud Detection Framework
Authors: Jiang Jiaxin , Zhang Zhen , Luo Bingqiao , He Bingsheng , Chen Min , Wang WeiYang , Chen Jia
Published: November 2024

In this demonstration, we introduce Spade, a sophisticated real-time fraud detection framework adept at navigating the complex transaction graph. Unlike conventional methods that are limited by performance and lack incremental update capabilities, Spade leverages advanced incremental updates in dense subgraph peeling algorithms to enhance efficiency, usability, and reduce latency, achieving a significantly better fraud prevention ratio. The demo showcases an interactive GUI prototype, allowing users to customize and explore dense subgraphs with various metrics and algorithms. This interactive demonstration also effectively highlights Spade's robust capacity to unearth fraudulent transactions within varied settings, including Grab's services and cryptocurrency transactions.
MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud
Authors: Kroth Brian , Matusevych Sergiy , Alotaibi Rana , Zhu Yiwen , Gruenheid Anja , Tian Yuanyuan
Published: November 2024

This paper presents MLOS (ML Optimized Systems), a flexible framework that bridges the gap between benchmarking, experimentation, and optimization of software systems. It allows users to create one-click benchmarking and experimentation scenarios for multi-VM setups in the cloud with optional standard and custom metrics collection and data management of the results. MLOS provides a collection of pluggable optimizers (ML or otherwise) for efficiently exploring the configuration space and finding optimal values for parameters across the entire software stack, including VM, OS kernel, and userland applications. It has a convenient lightweight interface for data storage, access, and visualization for a user-friendly notebook experience. These features make it a useful platform for both systems developers and auto-tuning researchers. MLOS is an active open-source project and is being used within Azure Data. A video demonstrating MLOS is available at https://aka.ms/MLOS/VLDB-2024-demo-video.
QPJVis Demo: Quality-Boost Progressive Join Query Processing System
Authors: Zhang Xin , Eldawy Ahmed
Published: November 2024

Progressive query processing enables data scientists to efficiently analyze and explore large datasets. Data scientists can start further analyses earlier if the progressive result can represent the complete results well. Most progressive processing frameworks carefully control which parts of the input to process in order to improve the quality of progressive results. The input control strategies work well when the data are processed uniformly. However, the progressive results will be biased towards the join keys if the processed data are not uniform. A recently proposed input&output framework named QPJ corrects the bias by temporarily hiding some results. The framework dynamically estimates the distribution of the complete result and outputs progressive results with a similar distribution to the estimated complete result. This demo presents QPJVis, which is a progressive query processing system designed to inherently process the progressive queries using the QPJ framework. Additionally, we also implement an input control framework, Prism, in QPJVis so that users can compare the difference between the input&output framework and a purely input framework.
RetClean: Retrieval-Based Data Cleaning Using LLMs and Data Lakes
Authors: Naeem Zan Ahmad , Ahmad Mohammad Shahmeer , Eltabakh Mohamed , Ouzzani Mourad , Tang Nan
Published: November 2024

Large language models (LLMs) have shown great potential in data cleaning, which is a fundamental task in all modern applications. In this demo proposal, we demonstrate that indeed LLMs can assist in data cleaning, e.g., filling in missing values in a data table, through different approaches. For example, cloud-based non-private LLMs, e.g., OpenAI GPT family or Google Gemini, can assist in cleaning non-private datasets that encompass world-knowledge information (Scenario 1). However, such LLMs may struggle with datasets that they have never encountered before, e.g., local enterprise data, or when the user requires an explanation of the source of the suggested clean values. In that case, retrieval-based methods using RAG (Retrieval Augmented Generation) that complements the LLM power with a user-provided data source, e.g., a data lake, are a must. The data lake is indexed, and each time a new request comes, we retrieve the top-
k
relevant tuples to the user's query tuple to be cleaned and leverage LLM inference power to infer the correct value (Scenario 2). Nevertheless, even in Scenario 2, sharing enterprise data with public LLMs (an externally hosted model) might not be feasible for privacy reasons. In this scenario, we showcase the practicality of locally hosted small LLMs in the cleaning process, especially after fine-tuning them on a small number of examples (Scenario 3). Our proposed system,
RetClean
, seamlessly supports all three scenarios and provides a user-friendly GUI that enables the VLDB audience to explore and experiment with different LLMs and investigate their trade-offs.
Mach: Firefighting Time-Critical Issues in Complex Systems Using High-Frequency Telemetry
Authors: Solleza Franco , Li Shihang , Sun William , Tang Richard , Schwarzkopf Malte , Tatbul Nesime , Crotty Andrew , Cohen David , Zdonik Stan
Published: November 2024

To understand the complex interactions in modern software, engineers often rely on high-frequency telemetry (HFT) data generated via tools like eBPF. However, today's database systems are too slow for HFT's rate and volume and cannot process HFT within the limited resources available on individual host machines.

Mach is a new storage engine for collecting and querying HFT. Key to Mach is the Temporal Skip Log (TSL)---a lightweight, write-optimized, log-based data structure specialized for HFT. Mach supports high ingest rates and makes data immediately queryable while operating within a limited on-host resource envelope.

Our demo shows how Mach helps engineers collect and query HFT in near real-time when diagnosing performance problems. In contrast, current systems and data reduction techniques fail to keep up. While a widely used time series database (InfluxDB) drops much of the HFT, the audience will see how Mach loses no data and allows them to interactively explore HFT from application and kernel events as they arrive.
Demonstration of the VeriEQL Equivalence Checker for Complex SQL Queries
Authors: Zhao Pinhan , He Yang , Wang Xinyu , Wang Yuepeng
Published: November 2024

Equivalence checking for SQL queries has many real-world applications but typically requires supporting an expressive SQL language in order to be practical. We develop VeriEQL, a system that can prove and disprove equivalence of
complex
SQL queries. Specifically, given two SQL queries under a database schema, VeriEQL can verify whether these two queries always produce identical results on all possible input databases up to a bounded size that conform to the schema. This paper demonstrates VeriEQL in three scenarios, including validating the correctness of query optimizations, grading SQL queries on online coding platforms, and finding implementation bugs in database management systems.
ModsNet: Performance-Aware Top-
k
Model Search Using Exemplar Datasets
Authors: Wang Mengying , Ma Hanchao , Guan Sheng , Bian Yiyang , Che Haolai , Daundkar Abhishek , Sehirlioglu Alp , Wu Yinghui
Published: November 2024

We demonstrate
ModsNet
, a search tool for pre-trained data science
MOD
el
s
recommendatio
N
using
E
xamplar da
T
aset. Given a set of pre-trained data science models, an "example" input dataset, and a user-specified performance metric, ModsNet answers the following query:
"what are top-k models that have the best expected performance for the input data?"
The need for searching high-quality pre-trained models is evident in data-driven analysis. Inspired by "query by example" paradigm, ModsNet does not require users to write complex queries, but only provide an "examplar" dataset, a task description, and a performance measure as input, and can automatically suggest top-
k
matching models that are expected to have desirable performance to perform the task over the provided sample dataset. ModsNet utilizes a knowledge graph to integrate model performances over datasets and synchronizes it with a bipartite graph neural network to
estimate
model performance, reduce inference cost, and promptly respond to top-
k
model search queries. To cope with strict cold-start (upon receiving a new dataset when no historical performance of registered models are observed), it performs a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising models. We demonstrate the application of ModsNet in enabling efficient scientific data analysis.
High-Performance Spatial Data Analytics: Systematic R&D for Scale-Out and Scale-Up Solutions from the Past to Now
Authors: Wang Fusheng , Lee Rubao , Teng Dejun , Zhang Xiaodong , Saltz Joel
Published: November 2024

We released open-source software Hadoop-GIS in 2011, and presented and published the work in VLDB 2013. This work initiated the development of a new spatial data analytical ecosystem characterized by its large-scale capacity in both computing and data storage, high scalability, compatibility with low-cost commodity processors in clusters and open-source software. After more than a decade of research and development, this ecosystem has matured and is now serving many applications across various fields. In this paper, we provide the background on why we started this project and give an overview of the original Hadoop-GIS software architecture, along with its unique technical contributions and legacy. We present the evolution of the ecosystem and its current state-of-the-art, which has been influenced by the Hadoop-GIS project. We also describe the ongoing efforts to further enhance this ecosystem with hardware accelerations to meet the increasing demands for low latency and high throughput in various spatial data analysis tasks. Finally, we will summarize the insights gained and lessons learned over more than a decade in pursuing high-performance spatial data analytics.
Intelligent Agents for Data Exploration
Authors: Amer-Yahia Sihem
Published: November 2024

Data Exploration is an incremental process that helps users express what they want through a conversation with the data. Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been proposed. We first summarize some RL solutions that were built for different applications. In this context, various data exploration operators are leveraged including traditional roll-up and drill-down operations and text-based operations. An RL agent is trained to generate the best policy according to a hand-crafted reward function.

The benefit of training RL policies for specific data exploration tasks has been demonstrated more than once for exploring finding a needle in a haystack, for serendipitous galaxy exploration, for helping a customer land on a satisfactory product, for helping a conference chair build a program committee in a stepwise fashion, for summarizing large datasets, etc.

With the advent of Large Language Models and their ability to reason sequentially, it has become legitimate to ask the question: would LLMs and AI planning outperform an RL policy in data exploration? More specifically, would LLMs help circumvent retraining for new tasks and striking a balance between specificity and generality? This led us to designing LLM-powered approaches that introduce a new way of thinking about data exploration.
Besov regularity for the elliptic p-harmonic equations in the non-quadratic case
Authors: Yao Fengping
Published: November 2024

In this article, we mainly establish the local extra fractional differentiability (Besov regularity) of weak solutions for the following divergence nonlinear elliptic equations of

p

p

-Laplacian type:

div

A

(

D
u
,
x

)

=

div

F
,

\hspace{0.1em}\text{div}\hspace{0.1em}A(Du,x)=\hspace{0.1em}\text{div}\hspace{0.1em}{\bf{F}},

where

A

A

is a Carathéodory function with

p
−
1

p-1

growth for

1
<
p
<
2

1\lt p\lt 2

. The standard example for the aforementioned equations is the classical elliptic

p

p

-Laplacian equation

div

(

∣

D
u

∣

p
−
2

D
u

)

=

div

F
,

for

1
<
p
<
2
.

\hspace{0.1em}\text{div}\hspace{0.1em}({| Du| }^{p-2}Du)=\hspace{0.1em}\text{div}\hspace{0.1em}{\bf{F}},\hspace{1.0em}\hspace{0.1em}\text{for}\hspace{0.1em}\hspace{0.33em}1\lt p\lt 2.

Remarkably, the case

1
<
p
<
2

1\lt p\lt 2

is very different from the case

p
≥
2

p\ge 2

since the modulus of ellipticity in the elliptic

p

p

-Laplacian equation tends to infinity when

∣
D
u
∣
→
0

| Du| \to 0

.
A zinc oxide–tin oxide–nerolidol hybrid nanomaterial: Efficacy against esophageal squamous cell carcinoma
Authors: Jiang Pu , Chen Zujian , Zhang Na , Gao Kun
Published: November 2024

The sixth most common cancer in the world, esophageal cancer, requires aggressive treatment such as surgery, chemotherapy, radiotherapy, and immunotherapy. Phytochemicals and medicinal plants are being used in the green synthesis of nanoparticles. Our study aimed to synthesize ZnO–SnO2 nerolidol nanocomposite and study its effects on esophageal squamous cell carcinoma cells. UV spectroscopy showed significant absorbance at 288 nm, transmission electron microscopy and DLS showed spherical shapes, and transmission electron microscopy also showed 108 nm average diameters. The ZnO–SnO2 nerolidol nanocomposite was also investigated using energy-dispersive X-ray analysis, Fourier transform infrared spectroscopy, photoluminescence, and field emission scanning electron microscopy. A cytotoxic effect was observed against KYSE-150 cells with an IC50 concentration of 14.9 μg/mL. The ZnO–SnO2 nerolidol nanocomposite inhibited cancer cell proliferation in KYSE-150 cells and enhanced apoptosis by altering its mitochondrial membrane potential. The ZnO–SnO2 nerolidol nanocomposite also enhanced oxidative stress, leading to a decrease in superoxide dismutase, catalase, and glutathione and an increase in lipid peroxidation. Ultimately, ZnO–SnO2 nerolidol nanocomposite enhanced the caspase cascade by inducing caspases 3, 8, and 9 in KYSE-150 cells. On the whole, we suggest that the ZnO–SnO2 nerolidol nanocomposite can be an effective treatment strategy against esophageal squamous cell carcinoma in KYSE-150 cells. However, understanding molecular circuits is still warranted.
The sequential Henstock-Kurzweil delta integral on time scales
Authors: Liu Yang , Shao Yabin
Published: November 2024

In this study, the basic theory of the sequential Henstock-Kurzweil delta integral on time scales will be discussed. First, we give the notion and the elementary properties of this integral; then we show the equivalence of the Henstock-Kurzweil delta integral and the sequential Henstock-Kurzweil delta integral on time scales. In addition, we consider the Cauchy criterion and the Fundamental Theorems of Calculus. Finally, we prove Henstock’s lemma and give some convergence theorems. As an application, we consider the existence theorem of a kind of functional dynamic equations.
Heat transfer characteristics in a non-Newtonian (Williamson) hybrid nanofluid with Hall and convective boundary effects
Authors: Alharbi Latifah Falah , Saadeh Rania , Ishak Anuar , Khan Umair , Hussain Syed Modassir , Madhukesh Javali Kotresh , Yılmaz Yalçın , Zaib Aurang
Published: November 2024

Heat transfer (HT) technology is rapidly increasing due to the petition for well-organized heating systems and cooling systems in the requisite posited by chemical, automotive, and aerospace industries. Thus, the present investigation examines mixed convective or buoyancy flow induced by the Williamson fluid closer to the stagnation point worsened by hybrid nanoparticles (alumina [Al2O3] and copper [Cu]) through a flat upright plate by the influence of the Hall effect. The water-based Al2O3 and copper Cu nanoparticles acknowledging convectively-heated as suitable in the industry or engineering are inspected. The dominant equations are non-dimensionalized using the appropriate similarity parameters, and subsequently, using the bvp4c, these are solved numerically. We thoroughly investigate the effects of numerous pertinent parameters on the transverse velocity, the axial velocity, drag force, temperature, and HT. Two dissimilar outputs are perceived in the circumstance of opposing flow, compared to simply one in the assisting flow. The solutions also showed that the thermal boundary layer length increases and the velocity thickness of the boundary layer decreases as a result of the nanofluid. The higher Weissenberg number causes the gradients for the stable result branch to increase, whereas the gradients for the lower result branch drop. The Lorentz force impact can also be utilized to modify the flow and physical characteristics of HT. In addition, the friction factor in the transverse axis enlarges with the magnetic number for both branches.
Ensemble learning and deep learning-based defect detection in power generation plants
Authors: Atemkeng Marcellin , Osanyindoro Victor , Rockefeller Rockefeller , Hamlomo Sisipho , Mulongo Jecinta , Ansah-Narh Theophilus , Tchakounté Franklin , Fadja Arnaud Nguembang
Published: November 2024

One of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses, including the telecommunications industry, are constantly emerging. Therefore, they must rely on generators to ensure their full functionality. These generators rely on fuel to function, and consumption is usually high if not properly monitored. Monitoring is usually done by a (non-expert) human. This can sometimes be a tedious process, as some companies have reported excessively high consumption rates. For anomaly detection in power generating plants, the studies by Mulongo et al. and Atemkeng and Jimoh used the same dataset to train a multilayer perceptron (MLP) and generative adversarial networks (GANs), respectively, achieving an accuracy of 96.1% with MLP and 98.9% with GAN. Through comparative analysis and the use of ensemble learning techniques, we found that ensemble learning models outperform both MLP and GAN as proposed by Mulongo et al. and Atemkeng and Jimoh using the same dataset. Furthermore, we investigated the potential of autoencoders to outperform MLPs, GANs, and ensemble learning models. To this end, we have introduced a label-assisted autoencoder approach for detecting anomalies in power-generating plants. This model includes a labelling assistance module that adjusts the thresholds. Our results indicate that the label-assisted autoencoder outperforms the MLP. However, GANs and all ensemble learning models outperformed the label-assisted autoencoder. Nevertheless, the use of a label-assisted autoencoder offers a distinct advantage in categorizing anomalies based on their severity, a capability not present in ensemble learning models and GANs.
Crystal structure of the hydrogen storage active high entropy phase Tb0.82Sm0.18Ni0.83Co0.17Mg
Authors: Pavlyuk Nazar , Kordan Vasyl , Dmytriv Grygoriy , Pavlyuk Volodymyr
Published: November 2024

Tb0.82Sm0.18Ni0.83Co0.17Mg, orthorhombic, Cmcm (no. 63), a = 3.6707(4) Å, b = 17.723(1) Å, c = 3.9863(4) Å, V = 259.33 (4) Å3, Z = 4, R

gt
(F) = 0.0334, wR

ref
(F
2) = 0.0861, T = 293 (2) K.
Benign bone and soft tissue tumors of the foot
Authors: Biz Carlo , Angelini Andrea , Fantoni Ilaria , Cerchiaro Mariachiara , Longhi Valentina , Ruggieri Pietro
Published: November 2024

Tumors of the foot are a heterogeneous group of neoplasms that either affect soft tissues or bone, with a predominance being benign.

Mistakes in the diagnosis of neoplastic conditions are common.

A correct diagnostic approach supported by radiological and histological examination is mandatory.

In this review, we highlight current standards in diagnosis, clinicopathological presentation, and imaging features.
Mitochondrial maintenance as a novel target for treating steroid-induced osteonecrosis of femoral head: a narrative review
Authors: Yang Yidan , Jian Yi , Liu Youwen , Ma Maoxiao , Guo Jiayi , Xu Bin , Yue Chen
Published: November 2024

The pathogenesis of steroid-induced osteonecrosis of the femoral head (SONFH) remains unclear; however, emerging evidence suggests that mitochondrial injury plays a significant role.

This review aims to elucidate the involvement of mitochondrial dysfunction in SONFH and explore potential therapeutic targets.

A comprehensive literature search was conducted in PubMed, Web of Science, and Elsevier ScienceDirect, focusing on mitochondrial homeostasis, including mitophagy, mitochondrial biogenesis, mitochondrial dynamics, and oxidative stress in SONFH. Ultimately, we included and analyzed a total of 16 studies.

Glucocorticoids initially promote but later inhibit mitochondrial biogenesis in osteoblasts, leading to excessive ROS production and mitochondrial dysfunction. This dysfunction impairs osteoblast survival and bone formation, contributing to SONFH progression.

Key proteins such as mitochondrial transcription factor A (TFAM) and peroxisome proliferator-activated receptor γ coactivator 1-α (PGC1α) are potential therapeutic targets for promoting mitochondrial biogenesis and reducing ROS-induced damage.

Enhancing mitochondrial function and reducing oxidative stress in osteoblasts may prevent or slow the progression of SONFH. Future research should focus on developing these strategies.