bluuebunny/crossref_metadata_2025_split huggingface dataset:
Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs
Authors: Choi Minguk , Yoo Seehwan , Choi Jongmoo 
Published: May 2024

By embedding the distribution of keys in indexing structure, learned indexes can minimize the index size and maximize the lookup performance. Yet, one of the problems in the present learned index is the long index-building time. The conventional learned index requires a complete traversal of the entire dataset, which makes it less practical than traditional index. This paper challenges the efficiency of build time to make the learned index practical.
Our approach for a build time-efficient learned index is to employ sampled learning. In this paper, we present two error-bounded sampling schemes: Sample EB-PLA, and Sample EB-Histogram. Although sampling is a simple idea, there are several considerations to make it practical. For example, sampling interval, error-boundness, and index hyper-parameters are inter-related each other, presenting complicated trade-offs between build-time, index size, accuracy and lookup latency.
Throughout the extensive experiments over six real-world datasets, we show that the index-building time can be efficiently reduced over an order of magnitude by our sampling schemes. The results reveal that the sampling expands the design space of learned indexes, including the build-time as well as lookup performance and index size. Our Pareto analysis shows that a learned index can be built more efficiently than a traditional index through sampling.
Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations
Authors: Chen Sibei , He Yeye , Cui Weiwei , Fan Ju , Ge Song , Zhang Haidong , Zhang Dongmei , Chaudhuri Surajit 
Published: May 2024

Spreadsheets are widely recognized as the most popular end-user programming tools, which blend the power of formula-based computation, with an intuitive table-based interface. Today, spreadsheets are used by billions of users to manipulate tables, most of whom are neither database experts nor professional programmers.
Despite the success of spreadsheets, authoring complex formulas remains challenging, as non-technical users need to look up and understand non-trivial formula syntax. To address this pain point, we leverage the observation that there is often an abundance of similar-looking spreadsheets in the same organization, which not only have similar data, but also share similar computation logic encoded as formulas. We develop an Auto-Formula system that can accurately predict formulas that users want to author in a target spreadsheet cell, by learning and adapting formulas that already exist in similar spreadsheets, using contrastive-learning techniques inspired by "similar-face recognition" from compute vision. Extensive evaluations on over 2K test formulas extracted from real enterprise spreadsheets show the effectiveness of Auto-Formula over alternatives. Our benchmark data is available at https://github.com/microsoft/Auto-Formula to facilitate future research.
CaaS-LSM: Compaction-as-a-Service for LSM-based Key-Value Stores in Storage Disaggregated Infrastructure
Authors: Yu Qiaolin , Guo Chang , Zhuang Jay , Thakkar Viraj , Wang Jianguo , Cao Zhichao 
Published: May 2024

Optimizing LSM-based Key-Value Stores (LSM-KVS) for disaggregated storage is essential to achieve better resource utilization, performance, and flexibility. Most of the existing studies focus on offloading the compaction to the storage nodes to mitigate the performance penalties caused by heavy network traffic between computing and storage. However, several critical issues are not addressed including the strong dependency between offloaded compaction and LSM-KVS, resource load-balancing, compaction scheduling, and complex transient errors.
To address the aforementioned issues and limitations, in this paper, we propose CaaS-LSM, a novel disaggregated LSM-KVS with a new idea of Compaction-as-a-Service. CaaS-LSM brings three key contributions. First, CaaS-LSM decouples the compaction from LSM-KVS and achieves stateless execution to ensure high flexibility and avoid coordination overhead with LSM-KVS. Second, CaaS-LSM introduces a performance- and resource-optimized control plane to guarantee better performance and resource utilization via an adaptive run-time scheduling and management strategy. Third, CaaS-LSM addresses different levels of transient and execution errors via sophisticated error-handling logic. We implement the prototype of CaaS-LSM based on RocksDB and evaluate it with different LSM-based distributed databases (Kvrocks and Nebula). In the storage disaggregated setup, CaaS-LSM achieves up to 8X throughput improvement and reduces the P99 latency up to 98% compared with the conventional LSM-KVS, and up to 61% of improvement compared with state-of-the-art LSM-KVS optimized for disaggregated storage.
DecoPa: Query Decomposition for Parallel Complex Event Processing
Authors: Akili Samira , Purtzel Steven , Weidlich Matthias 
Published: May 2024

Systems for Complex Event Processing (CEP) enable the detection of predefined patterns in event streams. While the evaluation of CEP queries is computationally hard, scalability may be achieved by parallelization. Yet, existing approaches for parallel CEP are driven by static query properties, such as partitioning keys and states of the evaluation model. They largely neglect the rates with which processing units may ingest and compare events for query evaluation.
In this paper, we present an approach for parallel CEP that is based on a flexible decomposition of CEP queries. Our idea is to guide the decomposition by the sustainable throughput of each processing unit, in order to maximize the overall performance. To this end, we introduce DecoPa plans for parallel CEP, provide a cost model for them, elaborate on their correctness and optimality, and present an algorithm for their construction. Experiments using a DecoPa implementation in Flink illustrate throughput gains of up to 12 orders of magnitude compared to state-of-the-art approaches.
Graph Summarization: Compactness Meets Efficiency
Authors: Chu Deming , Zhang Fan , Zhang Wenjie , Zhang Ying , Lin Xuemin 
Published: May 2024

As the volume and ubiquity of graphs increase, a compact graph representation becomes essential for enabling efficient storage, transfer, and processing of graphs. Given a graph, the graph summarization problem asks for a compact representation that consists of a summary graph and the corrections, such that we can recreate the original graph from the representation exactly. Although this problem has been studied extensively, the existing works either trade summary compactness for efficiency, or vice versa. In particular, a well-known greedy method provides the most compact summary but incurs prohibitive time cost, while the state-of-the-art algorithms with practical overheads are more than 20% behind in summary compactness in our comparison with the greedy method.
This paper presents Mags and Mags-DM, two algorithms that aim to bridge the compactness and efficiency in graph summarization. Mags adopts the existing greedy paradigm that provides state-of-the-art compactness, but significantly improves its efficiency with a novel algorithm design. Meanwhile, Mags-DM follows a different paradigm with practical efficiency and overcomes its limitations in compactness. Moreover, both algorithms can support parallel computing environments. We evaluate Mags and Mags-DM on graphs up to billion-scale and demonstrate that they achieve state-of-the-art in both compactness and efficiency, rather than in one of them. Compared with the method that offers state-of-the-art compactness, Mags and Mags-DM have a small difference (< 0.1% and < 2.1%) in compactness. For efficiency, Mags is on average 11.1x and 4.2x faster than the two state-of-the-art algorithms with practical overheads, while Mags-DM can further reduce the running time by 13.4x compared with Mags. This shows that graph summarization algorithms can be made practical while still offering a compact summary.
Nexus: Correlation Discovery over Collections of Spatio-Temporal Tabular Data
Authors: Gong Yue , Galhotra Sainyam , Castro Fernandez Raul 
Published: May 2024

Causal analysis is essential for gaining insights into complex real-world processes and making informed decisions. However, performing accurate causal analysis on observational data is generally infeasible, and therefore, domain experts start exploration with the identification of correlations. The increased availability of data from open government websites, organizations, and scientific studies presents an opportunity to harness observational datasets in assisting domain experts during this exploratory phase.
In this work, we introduce Nexus, a system designed to align large repositories of spatio-temporal datasets and identify correlations, facilitating the exploration of causal relationships. Nexus addresses the challenges of aligning tabular datasets across space and time, handling missing data, and identifying correlations deemed "interesting". Empirical evaluation on Chicago Open Data and United Nations datasets demonstrates the effectiveness of Nexus in exposing interesting correlations, many of which have undergone extensive scrutiny by social scientists.
SchemaPile: A Large Collection of Relational Database Schemas
Authors: Döhmen Till , Geacu Radu , Hulsebos Madelon , Schelter Sebastian 
Published: May 2024

Access to fine-grained schema information is crucial for understanding how relational databases are designed and used in practice, and for building systems that help users interact with them. Furthermore, such information is required as training data to leverage the potential of large language models (LLMs) for improving data preparation, data integration and natural language querying. Existing single-table corpora such as GitTables provide insights into how tables are structured in-the-wild, but lack detailed schema information about how tables relate to each other, as well as metadata like data types or integrity constraints. On the other hand, existing multi-table (or database schema) datasets are rather small and attribute-poor, leaving it unclear to what extent they actually represent typical real-world database schemas.
In order to address these challenges, we present SchemaPile, a corpus of 221,171 database schemas, extracted from SQL files on GitHub. It contains 1.7 million tables with 10 million column definitions, 700 thousand foreign key relationships, seven million integrity constraints, and data content for more than 340 thousand tables. We conduct an in-depth analysis on the millions of schema metadata properties in our corpus, as well as its highly diverse language and topic distribution. In addition, we showcase the potential of \corpus to improve a variety of data management applications, e.g., fine-tuning LLMs for schema-only foreign key detection, improving CSV header detection and evaluating multi-dialect SQL parsers. We publish the code and data for recreating SchemaPile and a permissively licensed subset SchemaPile-Perm.
Vexless: A Serverless Vector Data Management System Using Cloud Functions
Authors: Su Yongye , Sun Yinqi , Zhang Minjia , Wang Jianguo 
Published: May 2024

Cloud functions, exemplified by AWS Lambda and Azure Functions, are emerging as a new computing paradigm in the cloud. They provide elastic, serverless, and low-cost cloud computing, making them highly suitable for bursty and sparse workloads, which are quite common in practice. Thus, there is a new trend in designing data systems that leverage cloud functions. In this paper, we focus on vector databases, which have recently gained significant attention partly due to large language models. In particular, we investigate how to use cloud functions to build high-performance and cost-efficient vector databases. This presents significant challenges in terms of how to perform sharding, how to reduce communication overhead, and how to minimize cold-start times.
In this paper, we introduce Vexless, the first vector database system optimized for cloud functions. We present three optimizations to address the challenges. To perform sharding, we propose a global coordinator (orchestrator) that assigns workloads to Cloud function instances based on their available hardware resources. To overcome communication overhead, we propose the use of stateful cloud functions, eliminating the need for costly communications during synchronization. To minimize cold-start overhead, we introduce a workload-aware Cloud function lifetime management strategy. Vexless has been implemented using Azure Functions. Experimental results demonstrate that Vexless can significantly reduce costs, especially on bursty and sparse workloads, compared to cloud VM instances, while achieving similar or higher query performance and accuracy.
Structural Parameterizations for Two Bounded Degree Problems Revisited
Authors: Lampis Michael , Vasilakis Manolis 
Published: May 2024

We revisit two well-studied problems,
Bounded Degree Vertex Deletion
and
Defective Coloring
, where the input is a graph
G
and a target degree Δ, and we are asked either to edit or partition the graph so that the maximum degree becomes bounded by Δ. Both problems are known to be parameterized intractable for the most well-known structural parameters, such as treewidth.

We revisit the parameterization by treewidth, as well as several related parameters and present a more fine-grained picture of the complexity of both problems. In particular, we present the following:

—

Both problems admit straightforward DP algorithms with table sizes

\((\Delta +2)^\mathrm{tw}\)

and

\((\chi _\mathrm{d}(\Delta +1))^{\mathrm{tw}}\)

, respectively, where tw is the input graph’s treewidth and

\(\chi _\mathrm{d}\)

the number of available colors. We show that, under the SETH, both algorithms are essentially optimal, for any non-trivial fixed values of

\(\Delta , \chi _\mathrm{d}\)

, even if we replace treewidth by pathwidth. Along the way, we obtain an algorithm for
Defective Coloring
with complexity quasi-linear in the table size, thus settling the complexity of both problems for treewidth and pathwidth.

—

Given that the standard DP algorithm is optimal for treewidth and pathwidth, we then go on to consider the more restricted parameter tree-depth. Here, previously known lower bounds imply that, under the ETH,
Bounded Vertex Degree Deletion
and
Defective Coloring
cannot be solved in time

\(n^{o(\sqrt [4]{\mathrm{td}})}\)

and

\(n^{o(\sqrt {\mathrm{td}})}\)

, respectively, leaving some hope that a qualitatively faster algorithm than the one for treewidth may be possible. We close this gap by showing that neither problem can be solved in time

\(n^{o(\mathrm{td})}\)

, under the ETH, by employing a recursive low tree-depth construction that may be of independent interest.

—

Finally, we consider a structural parameter that is known to be restrictive enough to render both problems FPT: vertex cover. For both problems the best known algorithm in this setting has a super-exponential dependence of the form

\(\mathrm{vc}^{\mathcal {O}(\mathrm{vc})}\)

. We show that this is optimal, as an algorithm with dependence of the form

\(\mathrm{vc}^{o(\mathrm{vc})}\)

would violate the ETH. Our proof relies on a new application of the technique of
d
-detecting families introduced by Bonamy et al. [ToCT 2019].

Our results, although mostly negative in nature, paint a clear picture regarding the complexity of both problems in the landscape of parameterized complexity, since in all cases we provide essentially matching upper and lower bounds.
Temperature Variations of Spectra and Images of Photoluminescence from Heavily Cr-Doped Ruby
Authors: Hitomi Ami , Aizawa Hiroaki , Katsumata Toru 
Published: May 2024

Photoluminescence (PL) spectra, intensities, and image brightness of 0.1–6.0 at% Cr-doped ruby were investigated across temperatures ranging from 24° to 600 °C. PL emissions at λ = 670, 694, 707, and 715 nm were observed in rubies doped with 0.1–1.0 at% Cr. In rubies with higher doping levels of 1.5–6.0 at% Cr, an additional PL emission at λ = 770 nm was detected. PL intensities at λ = 694, 707, 715, and 770 nm decreased with increasing temperatures from 24° to 600 °C, while the PL intensity at λ = 670 nm showed a similar temperature-dependent decrease. The variation in PL image brightness with temperature, particularly the increase observed from 24° to 200 °C, is predominantly influenced by the PL intensity variations at λ = 670 nm. Decay curves and fluorescence lifetimes of PL from ruby with various Cr concentrations were also evaluated at temperatures RT-200 °C.
Optical and Spectroscopic Features of K2B4O7-Li2B4O7 -TeO2 Glasses Modified with Cu2+ Ions
Authors: Satyanarayana T. , K. Chandra Sekhar , Hameed Abdul , Sangeetha G. , Md Shareefuddin 
Published: May 2024

This work aims to analyze the structural properties of lithium and potassium tetra borate glasses that have undergone modification with tellurium oxide and Cu2+ ions with the chemical composition xK2B4O7-(80-x) Li2B4O7−19TeO2−1CuO [KLTC] (with x = 50, 60, 70 & 80 mole%). The analysis is performed by using different spectroscopic methods. The absence of sharp Braggs peaks in the X-ray diffraction spectra confirmed the amorphous nature of the processed glasses. The optical parameter values were obtained from Tauc and Urbach plots. The band gap values decreased in proportion with the content of K2B4O7 in KLTC. On these samples EPR studies were put on in order to determine the ligand field surrounded by the Cu2+ ions. The spin-Hamiltonian parameters suggest that the Cu2+ ions are located in tetragonally stretched octahedral locations. The existence of metal cations along with the characteristics borate as well as tellurite structural units were confirmed by the peaks observed in the Fourier transform infrared and Raman spectra. Among the prepared KLTC glass samples, KLTC-80 is found to be the best sample in the field of fiber optics communication.
Autoposição e autodestruição na filosofia de Schelling
Authors: Pacheco Marília Cota 
Published: May 2024

Falar da possibilidade ou não de aniquilação da natureza, pelo próprio homem, é no limite uma polêmica infindável se considerarmos que a não sustentabilidade dos recursos naturais implica a aniquilação da própria humanidade. O tema por si mesmo expõe uma contradição, porquanto todo imaginário individual, enquanto um ideal, contém a noção de autopreservação. Mas o fato é que em nossos dias tal possibilidade atingiu um alto grau de probabilidade. Então podemos nos perguntar: como isso aconteceu? Neste trabalho, iremos falar sobre a possibilidade, ou não, de aniquilação da natureza, através da noção de Schelling de autoposição do espírito como liberdade incondicionada. Primeiramente, buscaremos esclarecer as razões pelas quais chegamos ao impasse de nossos dias apresentando a dificuldade com a qual Schelling se depara ao considerar o Eu como princípio no texto Sistema do Idealismo Transcendental.Em seguida, a passagem para sua filosofia da identidade como uma tentativa de mostrar como o indivíduo da consciência empírica pode ser livre e, ao mesmo tempo, estabelece uma relação histórico-universal válida para o todo da diversidade de consciências finitas. Num terceiro momento apresentaremos a sua noção de Abfall (“Filosofia e Religião”) através da qual o autor demonstra como o universal é intuído pelo particular. Por fim, considerando a dialética da imaginação implícita na noção de Abfall, tentaremos indicar uma direção norteadora sobre a questão da preservação da natureza e, por conseguinte, do homem.
Teenagers’ perceptions of Mexican primates: A participatory environmental education program for the conservation of mantled howler monkeys (Alouatta palliata) in Los Tuxtlas, Mexico
Authors: Hernández-Cortés Alejandra , Silva-Rivera Evodia , Rangel-Negrín Ariadna , Franquesa-Soler Montserrat 
Published: May 2024

Abstract
This study explores the perceptions that high school students have about mantled howler monkeys in the Mexican community of Balzapote, Veracruz. We systematized the learning process derived from a Primate Conservation Education Program (PCEP) based on participatory action research (PAR) and arts-based education. Our results indicate a decrease in students’ fear of monkeys, the recovery of myths and legends about this species, as well as a complex knowledge about monkeys and their habitat. The inclusion of local knowledge enriched the learning experience and reinforced primate conservation concepts. The PAR and arts-based approaches allowed students to internalize and apply acquired knowledge and fostered empathy. In addition to learning, the approach helped develop teamwork, critical thinking, and creative skills. Quantitative indicators showed shifts in emotions and knowledge post-program, and qualitative data revealed a deep articulation of learned content. We also identified migration and mining activities as socio-political factors impacting local people’s livelihoods and wildlife conservation in this region.
4. Semantic Roles and Decomposition of Agency
Authors: Canu Højgaard Christian 
Published: May 2024

Social network analysis relies on a mapping of relationships or interactions among persons or groups. Often a particular relationship is assumed (e.g., marriage or trade ties), but all sorts of relationships can be taken into account. One way to deal with social network analysis of texts is to extract all event structures consisting of an actor, undergoer, and event, grammatically realized as subject, (in)direct object, and verbal predicate. Leviticus 17–26 contains 181 different verbs resulting in a complex social network. This prompts the question of how to differentiate between various verbal events in a quantifiable way.
The question is addressed by invoking linguistic theories of agency since all events presuppose a certain amount of effort on behalf of the agents. By implication, verbs can be measured according to agency required, that is, amount of force and volition. This chapter develops a framework for capturing agency from verbal events on the basis of the Role and Reference Grammar theory for deriving semantic roles from the lexical aspect of verbs, also known as Aktionsart. It is argued that dynamicity and causation are the two most important verbal features for decoding agency.
5. Dynamicity
Authors: Canu Højgaard Christian 
Published: May 2024

Dynamicity is one of the most important verbal features with respect to agency. Essentially, dynamic or active events presuppose an active agent in contrast to stative situations. In practice, it is complicated to analyse verbal semantics, especially for ancient languages given the lack of native language users to consult.
The chapter investigates previous approaches to the analysis of the Hebrew verbal system and concludes that new quantitative methods are needed to provide more objective measures of verbal semantics. In particular, it is demonstrated how collostructional analysis can help to differentiate between semantic roles, most importantly between dynamic and static verbs but also between goal and source.
6. Causation
Authors: Canu Højgaard Christian 
Published: May 2024

Causative events are essentially agentive because they presuppose an agent to cause the event to happen. But causative events are not equally agentive. A window can be broken by a deliberate human agent, by accident, or by a physical force, each of which presupposes a different amount of force involved. This chapter investigates how Hebrew causative verbs can be measured for agency. Firstly, the two causative stem formations, hifʿil and piʿel, are analysed in order to detail how the stems vary in semantic roles and agency. Secondly, lexical causatives are investigated in light of semantic transitivity, in particular the three semantic features: instigation, volition, and affectedness.
It is argued that a hierarchy of eight semantic roles can be established with corresponding levels of agency, and the chapter concludes by associating Hebrew event structures with concrete agency scores.