bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultrashort all-fiber Fabry–Perot interferometer fabricated by a CO2 laser
Authors: Zhang Qianqian , Fan Zhiyuan , Zhang Jianping , Zhang Fengbo , Zhang Qiang , Li Yongmin
Published: September 2020

We proposed and demonstrated a method to fabricate ultrashort all-fiber Fabry–Perot interferometers by splicing a standard single-mode fiber and another single-mode fiber with a concave surface constructed by a

C
O

2

laser pulse. The geometric parameters of the concave surface could be controlled flexibly by adjusting the laser pulse and the relative position between the laser beam and the optical fiber. In our experiments, the minimum depth of the concave surfaces is 0.12 µm, which offers a means of fabricating an all-fiber Fabry−Perot interferometer with submicrometer cavity length. Moreover, the ultralow-roughness concave surface fabricated by a

C
O

2

laser pulse is beneficial to improve the fringe visibility of the interferometer. These advantages make it attractive for practical applications.
Multimode VCSEL Enables Multi-Data-Format Encoding up to 124 Gbit/s
Authors: Lo Wei-Chi , Wu Wei-Li , Peng Chun-Yen , Wang Huai-Yung , Tsai Cheng-Ting , Su Borching , Wu Chao-Hsin , Lin Gong-Ru
Published: September 2020

The 850-nm multimode VCSEL designed with dual-oxide-layer confined 7.5-um-large aperture and doped DBR shortened RC response improves its modulation linearity to 0.3±0.1- mW/mA for 61-Gbps NRZ-OOK, 102-Gbps PAM-4 and 124-Gbit/s 32-QAM GFDM data transmissions.
THz power optimization and analysis of plasmonic unbiased photoconductive THz emitters coupled to a spiral-like dipole antenna
Authors: Mohammad-Zamani Mohammad Javad
Published: September 2020

We propose a new spiral-like dipole antenna-coupled and unbiased terahertz (THz) photomixer with plasmonic unsymmetrical interdigitated electrodes (UIEs). Each electrode pair is made of subwavelength metal–semiconductor–metal (MSM) structures constituting heterogeneous Schottky contacts. From the optical view and analysis of the electrical transport in the semiconductor, by applying a systematic procedure to achieve optimum design, it is possible to benefit from advantages of having high THz photocurrent derived from the plasmonic enhanced optical absorption and, hence, photogeneration as well as severe built-in Schottky field in the photoconductor. Furthermore, from the electromagnetic view, by introducing an output resonant THz antenna having great impedance matching with the photomixer device, the efficiency of free-space coupling has significantly been enhanced. By exploiting all aspects of design, it has been provided to achieve the THz power of 409 µW from the spiral-like dipole antenna coupled to the proposed plasmonic UIEs with

10
×
10

µ

m

2

active area and pitch of

Λ
=
395

n
m

. This is more than 1 order of magnitude larger THz power than the highest THz power radiated from the same area of a conventional dipole antenna-coupled unbiased array of the near-field emitters. Also, this improvement is more than 3 orders of magnitude higher THz power as compared with the power radiated from a non-plasmonic far-field operating antenna-less THz chip of the same size.
Extreme local field enhancement by hybrid epsilon-near-zero–plasmon mode in thin films of transparent conductive oxides
Authors: Reddy Innem V. A. K. , Jornet Josep M. , Baev Alexander , Prasad Paras N.
Published: September 2020

Epsilon-near-zero (ENZ) materials display unique properties, and among them, large local field enhancement at ENZ frequency is of particular interest for many potential applications. In this Letter, we introduce the concept that a combination of epsilon-near-zero and surface plasmon polariton modes can be excited over an interface between a dielectric and a single ENZ layer in a specific frequency region, which can lead to extreme enhancement of local electric field. We demonstrate it with a systematic numerical simulation using finite element analysis and consider two configurations (Kretschmann configuration and a grating configuration), where an indium tin oxide (ITO) layer is sandwiched between two dielectric slabs. We confirm the formation of a hybrid mode at the ITO–dielectric interface at the wavelength of ENZ, as the ITO layer thickness reduces. The hybrid mode provides both high confinement and long propagation distance, which makes it more attractive for many applications than just a pure ENZ mode.
Theory of slow-light semiconductor optical amplifiers
Authors: Saldutti Marco , Rasmussen Thorsten S. , Gioannini Mariangela , Mørk Jesper
Published: September 2020

We have developed an efficient framework for analyzing the reflection and transmission properties of semiconductor photonic crystal optical amplifiers. Specifically, we have investigated the use of slow light to enhance the gain of short integrated amplifiers. We find that the expected enhancement in transmission is limited by distributed feedback induced by the material gain itself. Such back-scattering is further enhanced by the refractive index variation associated with the linewidth enhancement factor. The inclusion of this effect reveals that for a given material gain, devices with smaller linewidth enhancement factor may offer better performance.
Near-single-mode 3  kW monolithic fiber oscillator based on a longitudinally spindle-shaped Yb-doped fiber
Authors: Zeng Lingfa , Xi Xiaoming , Ye Yun , Zhang Hanwei , Wang Xiaolin , Pan Zhiyong , Wang Zefeng , Xu Xiaojun
Published: September 2020

In order to balance the mitigation of transverse mode instability and
stimulated Raman scattering effect in the high power fiber lasers, a
specially designed Yb-doped fiber, named spindle-shaped Yb-doped
fiber, was fabricated with a core/cladding diameter of 20/400 µm at
both ends and 30/600 µm in the middle. Based on this fiber, an
all-fiber laser oscillator was built and over 3 kW near-single-mode (

M

2

factor

∼

1.3

) laser was achieved with an
optical-to-optical conversion efficiency of 78.4%. While operating at
the maximum power, the transverse mode instability is well mitigated
and the stimulated Raman scattering effect is well suppressed (

>

34

d
B

lower than signal laser). Further
power scaling is promising by optimizing the structure of the Yb-doped
fiber.
Few-cycle pulses tunable from 3 to 7  µm via intrapulse difference-frequency generation in oxide LGN crystals
Authors: Liu Jinsheng , Ma Jingui , Lu Dazhi , Gu Xingbin , Cui Ziruo , Yuan Peng , Wang Jing , Xie Guoqiang , Yu Haohai , Zhang Huaijin , Qian Liejia
Published: September 2020

An ultrashort mid-infrared (IR) source beyond 5 µm is crucial for a plethora of existing and emerging applications in spectroscopy, medical diagnostics, and high-field physics. Nonlinear generation of such sources from well-developed near-IR lasers, however, remains a challenge due to the limitation of mid-IR crystals. Based on oxide

L
a

3

G
a

5.5

N
b

0.5

O

14

(LGN) crystals, here we report the generation of femtosecond pulses tunable from 3 to 7 µm by intrapulse difference-frequency generation of 7.5 fs, 800 nm pulses. The efficiency and bandwidth dependences on pump polarization and crystal length are studied for both Type-I and Type-II phase-matching configurations. Maximum pulse energy of 

∼

10

n
J

is generated at 5.2 µm with a conversion efficiency of

∼

0.14

%

. Because of the few-cycle pump pulse duration, the generated mid-IR pulses are as short as about three cycles. These results, to the best of our knowledge, represent the first experimental demonstration of LGN in generating mid-IR ultrashort pulses.
Effects of Sauropus androgynus extract and its combination with ampicillin against Methicillin-resistant Staphylococcus aureus: An in vitro study
Authors: Rahayu Asih , Rini Chylen Setiyo , Prakoso Yos Adi , Palgunadi Bagus Uda , Munandar Muhammad Aris
Published: September 2020

Background and Aim: The massive utilization of antibiotics has increased resistant genes produced by bacteria. Many bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), have become resistant against ampicillin (AMP). The combination of an herbal extract with AMP is expected to generate synergistic effects and may restore the susceptibility of MRSA against AMP. This study aimed to analyze the potency of Sauropus androgynous extract (SAE) as a single extract and combination with AMP against MRSA.

Materials and Methods: Sauropus androgynous was extracted using 60% ethanol. SAE biochemical compounds were analyzed qualitatively and quantitatively. SAE, AMP, and SAE+AMP were tested against MRSA isolates to determine the minimum inhibitory concentration and fractional inhibitory concentration. The inhibition of penicillin-binding proteins 2a (PBP2a) was analyzed using a latex agglutination test. Further, the disruptive membrane effects of SAE, AMP, and SAE+AMP were analyzed using a scanning electron microscope. The analysis of data was conducted using SPSS version 16 with p=0.01.

Results: SAE contained bioactive compounds such as phenolics and flavonoids. Further, 2 mg/mL of SAE could be used as the potential concentration against MRSA isolates in vitro. In addition, the utilization of SAE+AMP generated synergistic effects, restored the susceptibility of isolates against AMP, decreased the synthesis of PBP2a by the MRSA, and induced ultrastructural changes in the bacterial membrane.

Conclusion: This study indicated that the utilization of SAE potentially inhibits the growth of MRSA through decreasing of PBP2a expression, disruption of the MRSA membrane, while the combination of SAE+AMP showed synergistic effects against MRSA.
Variation of rhizosphere bacterial community diversity in the desert ephemeral plant Ferula sinkiangensis across environmental gradients
Authors: tao Zhang , Zhongke Wang , Xinhua Lv , Hanli Dang , Li Zhuang
Published: September 2020

Ferula sinkiangensis is a desert short-lived medicinal plant, and its number is rapidly decreasing. Rhizosphere microbial community plays an important role in plant growth and adaptability. However,
Ferula sinkiangensis rhizosphere bacterial communities and the soil physicochemical factors that drive the bacterial community distribution are currently unclear. On this study, based on high-throughput sequencing, we explored the diversity, structure and composition of
Ferula sinkiangensis rhizosphere bacterial communities at different slope positions and soil depths and their correlation with soil physicochemical properties. Our results revealed the heterogeneity and variation trends of
Ferula sinkiangensis rhizosphere bacterial community diversity and abundance on a fine spatial scale (Slope position and soil depth) and Found Actinobacteria (25.5%), Acidobacteria (16.9%), Proteobacteria (16.6%), Gemmatimonadetes (11.5%) and Bacteroidetes (5.8%) were the dominant bacterial phyla in
Ferula sinkiangensis rhizosphere soil. Among all soil physicochemical variables shown in this study, there was a strong positive correlation between phosphorus (AP) and the diversity of rhizosphere bacterial community in
Ferula sinkiangensis. In addition, Soil physicochemical factors jointly explained 24.28% of variation in
Ferula sinkiangensis rhizosphere bacterial community structure. Among them, pH largely explained the variation of
Ferula sinkiangensis rhizosphere bacterial community structure (5.58%), followed by total salt (TS, 5.21%) and phosphorus (TP, 4.90%).
Annual Income Contribution of Wood fuel production from Small-scale Plantation Forest Experience from Farmers in Fagta Lekoma, Awi zone, Amhara, Ethiopia
Authors: MITKU ALEMU MENGISTU
Published: September 2020

This study aims to examine the annual income contribution of wood fuel production from small - scale plantation forestry in Fagta Lekoma district of Amhara region, Ethiopia. Household surveys were conducted by following the snowball-sampling technique. The data analyzed by using STATA software and multiple linear regression model also employed to evaluate the contribution of wood fuel annual income from the total annual income of the households. About 9000 Ethiopian Birr (ETB) household income comes from charcoal selling which is the highest average annual income. The result of multiple linear regression analysis indicates that an increase in one unit (ETB) income from charcoal making leads to an increase in total annual income by 0.972 units (ETB). Annual income contribution of wood fuel production is the largest amount of income recorded followed by annual income from crop production. Plantation forestry plays a significant role not only economically but also by the provision of alternative livelihood options for households. Plantation forestry used as a means of soil reclamation as well as means of households income. Inaction and implementation of policies supporting plantation forestry sustain rural livelihood by increasing their adaptive potential for shock and stress by creating alternative means of livelihood.
COMPLEXITY REDUCTION OF CYCLOSTATIONARY SENSING TECHNIQUE USING IMPROVED HYBRID SENSING METHOD
Authors: Abdullah Hikmat N. , Dawood Zinah O. , Abdelkareem Ammar E. , Abed Hadeel S.
Published: September 2020

In cognitive radio system, the spectrum sensing has a major challenge in needing a sensing method, which has a high detection capability with reduced complexity. In this paper, a low-cost hybrid spectrum sensing method with an optimized detection performance based on energy and cyclostationary detectors is proposed. The method is designed such that at high signal-to-noise ratio SNR values, energy detector is used alone to perform the detection. At low SNR values, cyclostationary detector with reduced complexity may be employed to support the accurate detection. The complexity reduction is done in two ways: through reducing the number of sensing samples used in the autocorrelation process in the time domain and through using the Sliding Discrete Fourier Transform (SDFT) instead of the Fast Fourier Transform (FFT). To evaluate the performance, two versions of the proposed hybrid method are implemented, one with the FFT and the other with the SDFT. The proposed method is simulated for cooperative and non-cooperative scenarios and investigated under a multipath fading channel. Obtained results are evaluated by comparing them with other methods including: cyclostationary feature detection (CFD), energy detector and traditional hybrid. The simulation results show that the proposed method with the FFT and the SDFT successfully reduced the complexity by 20% and 40% respectively, when 60 sensing samples are used with an acceptable degradation in the detection performance. For instance, when Eb/N0 is 0 dB , the probability of the detection of Pd is decreased by 20 % and 10% by the proposed method with the FFT and the SDFT respectively, as compared with the hybrid method existing in the literature.
Desenvolvimento econômico no Matopiba: os arranjos produtivos locais da soja
Authors: J. Arruda de Oliveira Thiago , H. Dörner Stefan , B. Schneider Mirian
Published: September 2020

O artigo identifica no Matopiba (Maranhão, Tocantins, Piauí e Bahia) os arranjos produtivos locais do complexo da soja. Para isso, são utilizadas informações provenientes da Produção Agrícola Municipal, fornecidas pelo Instituto Brasileiro de Geografia e Estatística, e da Relação Anual de Informações Sociais, disponibilizada pelo Ministério da Economia. Detectam-se os agrupamentos por meio do índice de concentração normalizado, calculado para 2006 e 2017. Os resultados mostram que os arranjos produtivos locais se localizam no Oeste baiano, no alto do rio Parnaíba (divisa entre o Maranhão e Piauí) e no Tocantins (ao longo da rodovia BR-153 e da ferrovia Norte-Sul, indo do município de Colinas até a divisa com Goiás) formando um eixo de produção especializada na soja.
Tingkat Keberlanjutan Pemanfaatan Ruang Publik Multifungsi di Permukiman Kumuh
Authors: Caesandra Violla , Wiranegara Hanny Wahidin , Sugihartoyo Sugihartoyo
Published: September 2020

Due to the limitation of slum dweller’s residence, they use public spaces for their daily activities. One kind of them  is a park. Public space used for various kinds of activities by slum dwellers is called a multifunction public space. Utilization of multifunction public space can potentially lead to spatial use conflict which interfere with its functional sustainability. The aim of this research was to identify the level of sustainability of the utilization of a multifunction public space in the slum area. This research used a quantitative approach and a questionnaires survey method. Respondents were slum dwellers using the park. The total respondents was 96 by using the Lemeshow formula. The results showed that there were differences in the level of sustainability among three parks. The highest level of sustainable utilization is Pendongkelan, the medium level is Melati Indah, and the lowest level is Taman Anggrek. There were four aspects of sustainability which were measured. Diversity and harmony were two aspects determining sustainability. For sustainability of spatial utilization of the multifunction public space in the slum area need management to take care of the size of the park, the availability of park amenities, and its accessibility.
Monarch
Authors: Adams Colin , Alonso Luis , Atkin Benjamin , Banning John , Bhola Sumeer , Buskens Rick , Chen Ming , Chen Xi , Chung Yoo , Jia Qin , Sakharov Nick , Talbot George , Tart Adam , Taylor Nick
Published: September 2020

Monarch is a globally-distributed in-memory time series database system in Google. Monarch runs as a multi-tenant service and is used mostly to monitor the availability, correctness, performance, load, and other aspects of billion-user-scale applications and systems at Google. Every second, the system ingests terabytes of time series data into memory and serves millions of queries. Monarch has a regionalized architecture for reliability and scalability, and global query and configuration planes that integrate the regions into a unified system. On top of its distributed architecture, Monarch has flexible configuration, an expressive relational data model, and powerful queries. This paper describes the structure of the system and the novel mechanisms that achieve a reliable and flexible unified system on a regionalized distributed architecture. We also share important lessons learned from a decade's experience of developing and running Monarch as a service in Google.
TransNet
Authors: He Qijian , Yang Wei , Chen Bingren , Geng Yangyang , Huang Liusheng
Published: September 2020

The accuracy of neural network can be improved by training over multi-participants' pooled dataset, but privacy problem of sharing sensitive data obstructs this collaborative learning. To solve this contradiction, we propose TransNet, a novel solution for privacy-preserving collaborative neural network, whose main idea is to add a transformed layer to the neural network. It has the advantage of lower computation and communication complexity than previous secure multi-party computation based and homomorphic encryption based schemes, and has the superiority of supporting arbitrarily partitioned dataset compared to previous differential privacy based and stochastic gradient descent based schemes, which support horizontally partitioned dataset only. TransNet is trained by a server which pools the transformed data, but has no special security requirement on the training server. We evaluate TransNet's performance over four datasets using different neural network algorithms. Experimental results demonstrate that TransNet is not affected by the number of participants, and trains as quickly as the original neural network does. With proper variables, TransNet gets close accuracy to the baseline which trains over pooled original dataset.
Dynamic parameter allocation in parameter servers
Authors: Renz-Wieland Alexander , Gemulla Rainer , Zeuch Steffen , Markl Volker
Published: September 2020

To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed training---, but can induce severe communication overhead. To reduce communication overhead, distributed machine learning algorithms use techniques to increase parameter access locality (PAL), achieving up to linear speed-ups. We found that existing parameter servers provide only limited support for PAL techniques, however, and therefore prevent efficient training. In this paper, we explore whether and to what extent PAL techniques can be supported, and whether such support is beneficial. We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse, and experimentally compare its performance to existing parameter servers across a number of machine learning tasks. We found that Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers.
Data market platforms
Authors: Fernandez Raul Castro , Subramaniam Pranav , Franklin Michael J.
Published: September 2020

Data only generates value for a few organizations with expertise and resources to make data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover and integrate is hard because data owners lack
information
(who needs what data) and they do not have
incentives
to prepare the data in a way that is easy to consume by others.

In this paper, we propose data market platforms to address the lack of information and incentives and tackle the problems of data sharing, discovery, and integration. In a data market platform, data owners want to
share
data because they will be rewarded if they do so. Consumers are encouraged to share their data needs because the market will solve the
discovery
and
integration
problem for them in exchange for some form of currency.

We consider internal markets that operate within organizations to bring down data silos, as well as external markets that operate across organizations to increase the value of data for everybody. We outline a research agenda that revolves around two problems. The problem of market design, or how to design rules that lead to desired outcomes, and the systems problem, how to implement the market and enforce the rules. Treating data as a first-class asset is sorely needed to extend the value of data to more organizations, and we propose data market platforms as one mechanism to achieve this goal.
Knowledge translation
Authors: Bashardoost Bahar Ghadiri , Miller Renée J. , Lyons Kelly , Nargesian Fatemeh
Published: September 2020

We introduce Kensho, a tool for generating mapping rules between two Knowledge Bases (KBs). To create the mapping rules, Kensho starts with a set of correspondences and enriches them with additional semantic information automatically identified from the structure and constraints of the KBs. Our approach works in two phases. In the first phase, semantic associations between resources of each KB are captured. In the second phase, mapping rules are generated by interpreting the correspondences in a way that respects the discovered semantic associations among elements of each KB. Kensho's mapping rules are expressed using SPARQL queries and can be used directly to exchange knowledge from source to target. Kensho is able to automatically rank the generated mapping rules using a set of heuristics. We present an experimental evaluation of Kensho and assess our mapping generation and ranking strategies using more than 50 synthesized and real world settings, chosen to showcase some of the most important applications of knowledge translation. In addition, we use three existing benchmarks to demonstrate Kensho's ability to deal with different mapping scenarios.
LOG-Means
Authors: Fritz Manuel , Behringer Michael , Schwarz Holger
Published: September 2020

Clustering is a fundamental primitive in manifold applications. In order to achieve valuable results, parameters of the clustering algorithm, e.g., the number of clusters, have to be set appropriately, which is a tremendous pitfall. To this end, analysts rely on their domain knowledge in order to define parameter search spaces. While experienced analysts may be able to define a small search space, especially novice analysts often define rather large search spaces due to the lack of in-depth domain knowledge. These search spaces can be explored in different ways by estimation methods for the number of clusters. In the worst case, estimation methods perform an exhaustive search in the given search space, which leads to infeasible runtimes for large datasets and large search spaces. We propose LOG-Means, which is able to overcome these issues of existing methods. We show that LOG-Means provides estimates in sublinear time regarding the defined search space, thus being a strong fit for large datasets and large search spaces. In our comprehensive evaluation on an Apache Spark cluster, we compare LOG-Means to 13 existing estimation methods. The evaluation shows that LOG-Means significantly outperforms these methods in terms of runtime and accuracy. To the best of our knowledge, this is the most systematic comparison on large datasets and search spaces as of today.
Identifying insufficient data coverage in databases with multiple relations
Authors: Lin Yin , Guan Yifan , Asudeh Abolfazl , Jagadish H. V.
Published: September 2020

In today's data-driven world, it is critical that we use appropriate datasets for analysis and decision-making. Datasets could be biased because they reflect existing inequalities in the world, due to the data scientists' biased world view, or due to the data scientists' limited control over the data collection process. For these reasons, it is important to ensure adequate data coverage across different groups over the intersection of multiple attributes. Often, the dataset to be analyzed is obtained through complex joins and predicate combinations over multiple relational tables in a database. Due to the sheer data volume we often have to deal with, determining adequate coverage can require an unacceptably long execution time.

In this paper, we provide an efficient approach for coverage analysis, given a set of attributes across multiple tables. To identify regions with insufficient coverage in the combinatorially large set of value combinations, we design an index scheme to avoid explicit table joins, achieve efficient memory usage, and support predicate combination at a high level of parallelism. We also propose
P-WALK
, a priority-based search algorithm, to traverse the lattice space. Since in practice, coverage assessment typically does not require precise COUNT aggregation results, we further present approximate methods to reduce computation time. Experimental evaluation using three real-world datasets shows the effectiveness, efficiency, and accuracy of proposed methods.
Continuously monitoring alternative shortest paths on road networks
Authors: Li Lingxiao , Cheema Muhammad Aamir , Ali Mohammed Eunus , Lu Hua , Taniar David
Published: September 2020

Modern navigation systems do not only provide shortest paths but also some alternative paths to provide more options to the users. This paper is the first to study the problem of continuously reporting alternative paths for a user traveling along a given path. Specifically, given a path
P
on which a user is traveling, we continuously report to the user
k
paths from the user's current location on
P
to the target
t.
We present several algorithms each improving on the previous based on non-trivial observations and novel optimisations. The proposed algorithms maintain and exploit the previously computed useful information to efficiently update the
k
alternative paths as the user moves. We provide space and time complexity analysis for each proposed algorithm. We conduct a comprehensive experimental study on large real-world road networks. The results demonstrate that the proposed algorithms are up to several orders of magnitude faster than the straightforward algorithms.
Fast and effective distribution-key recommendation for amazon redshift
Authors: Parchas Panos , Naamad Yonatan , Van Bouwel Peter , Faloutsos Christos , Petropoulos Michalis
Published: September 2020

How should we split data among the nodes of a distributed data warehouse in order to boost performance for a forecasted workload? In this paper, we study the effect of different data partitioning schemes on the overall network cost of pairwise joins. We describe a generally-applicable data distribution framework initially designed for Amazon Redshift, a fully-managed petabyte-scale data warehouse in the cloud. To formalize the problem, we first introduce the
Join Multi-Graph
, a concise graph-theoretic representation of the workload history of a cluster. We then formulate the
"Distribution-Key Recommendation"
problem - a novel combinatorial problem on the
Join Multi-Graph
- and relate it to problems studied in other subfields of computer science. Our theoretical analysis proves that
"Distribution-Key Recommendation"
is NP-complete and is hard to approximate efficiently. Thus, we propose BaW, a hybrid approach that combines heuristic and exact algorithms to find a good data distribution scheme. Our extensive experimental evaluation on real and synthetic data showcases the efficacy of our method into recommending optimal (or close to optimal) distribution keys, which improve the cluster performance by reducing network cost up to 32x in some real workloads.
Rank aggregation algorithms for fair consensus
Authors: Kuhlman Caitlin , Rundensteiner Elke
Published: September 2020

Aggregating multiple rankings in a database is an important task well studied by the database community. High-stakes application domains include hiring, lending, and education where multiple decision makers rank candidates and their input is then combined into an overall consensus ranking. While state-of-art aggregation methods succeed in giving equal say to each decision maker, to date no methods ensure fair representation for groups of candidates being ranked, who risk being impacted by discriminatory bias. We present the first solution to this open problem of guaranteeing fairness for disadvantaged groups of candidates, while still producing a good consensus of the base rankings. We design a family of exact fair algorithms achieving optimality for fair rank aggregation. We also develop approximate methods achieving fairness with guaranteed minimal approximation error scaling to millions of candidates in the rankings. A comparative study evaluates our proposed methods, revealing trade-offs between aggregation accuracy and different degrees of unfair bias in a rich variety of rank aggregation scenarios. Our real-world case study demonstrates that our solutions mitigate unfair bias using real-world data.
G
3
Authors: Liu Husong , Lu Shengliang , Chen Xinyu , He Bingsheng
Published: September 2020

This paper demonstrates G
3
, a framework for Graph Neural Network (GNN) training, tailored from Graph processing systems on Graphics processing units (GPUs). G
3
aims at improving the efficiency of GNN training by supporting graph-structured operations using parallel graph processing systems. G
3
enables users to leverage the massive parallelism and other architectural features of GPUs in the following two ways: building GNN layers by writing sequential C/C++ code with a set of flexible APIs (Application Programming Interfaces); creating GNN models with essential GNN operations and layers provided in G
3
. The runtime system of G
3
automatically executes the user-defined GNNs on the GPU, with a series of graph-centric optimizations enabled. We demonstrate the steps of developing some popular GNN models with G
3
, and the superior performance of G
3
against existing GNN training systems, i.e., PyTorch and TensorFlow.
Like water and oil
Authors: Funke Henning , Teubner Jens
Published: September 2020

In response to physical limitations, hardware has changed significantly during the past two decades. As the database community we have no chance but adapt to those changes in order to benefit from these and further hardware advances.
Demonstrating the voice-based exploration of large data sets with CiceroDB-zero
Authors: Trummer Immanuel
Published: September 2020

This demonstration enables participants to explore large data sets via voice interfaces. The focus of the demonstration is on methods generating concise speech descriptions of query results, specified by users via voice input. The technical novelty of the demonstrated system lies in the fact that processing overheads are mostly moved into a pre-processing phase, generating speeches for batches of queries defined via templates. Visitors can access the demo via smart speakers on-site or via their own smart phones. They will be able to customize the generated voice descriptions and to tune voice output methods.
SPHINX
Authors: Chatzopoulos Serafeim , Patroumpas Kostas , Zeakis Alexandros , Vergoulis Thanasis , Skoutas Dimitrios
Published: September 2020

We present SPHINX, a system for metapath-based entity exploration in Heterogeneous Information Networks (HINs). SPHINX allows users to define different views over a HIN based on both automatically selected and user-defined meta-paths. Then, entity ranking and similarity search can be performed over these views to find and explore entities of interest, taking also into account any spatial or temporal properties of entities. A Web-based user interface is provided to facilitate users in performing the various functionalities supported by the system, including metapath-based view definition, index construction, search parameters specification, and visual comparison of the results.
ESTOCADA
Authors: Alotaibi R. , Cautis B. , Deutsch A. , Latrache M. , Manolescu I. , Yang Y.
Published: September 2020

Big data applications increasingly involve diverse datasets, conforming to different data models. Such datasets are routinely hosted in heterogeneous stores, each capable of handling one or a few data models, and each efficient for some, but not all, kinds of data processing. Systems capable of exploiting disparate data in this fashion are usually termed
polystores.
A current limitation of polystores is that applications are written taking into account which part of the data is stored in which store and how. This fails to take advantage of (
i
) possible redundancy, when the same data may be accessible (with different performance) from distinct data stores; (
ii
) previous query results (in the style of materialized views), which may be available in the stores.

We propose to demonstrate ESTOCADA [4], a novel approach that can be used in a polystore setting to transparently enable each query to benefit from the best combination of stored data and available processing capabilities. The system leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe the various data models and stored data.
Demonstration of interactive runtime debugging of distributed dataflows in Texera
Authors: Wang Zuozhi , Kumar Avinash , Ni Shengquan , Li Chen
Published: September 2020

We are developing Texera, an open source system that allows users to perform data analysis on a computing cluster using a GUI-based workflow. A unique functionality of the system is its support for interactive and responsive debugging on dataflows during their execution, while still being scalable and fault tolerant. In particular, users can pause/resume a workflow, investigate the state of operators, change the behavior of an operator, and set conditional breakpoints. In this way, a user will not feel "in the dark" during the long-running execution of an analytics task, a problem faced by other big data processing frameworks. In this demonstration we show this powerful functionality in Texera.
PANDA
Authors: Cao Yang , Takagi Shun , Xiao Yonghui , Xiong Li , Yoshikawa Masatoshi
Published: September 2020

In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from COVID-19 crises attempt to access citizen's location data to eliminate the outbreak. However, it raises privacy concerns and may open the doors to more invasive forms of surveillance in the name of public health. It also brings a challenge for privacy protection techniques: how can we leverage people's mobile data to help combat the pandemic without scarifying location privacy. We demonstrate that we can achieve this by implementing policy-based location privacy for epidemic surveillance. Our system has three primary functions for epidemic surveillance: people flow monitoring, epidemic analysis, and contact tracing. We provide an interactive tool allowing the attendees to explore and examine the usability of our system: (1) the utility of location monitor and disease transmission model estimation, (2) the procedure of contact tracing in our systems, and (3) the privacy-utility trade-offs w.r.t. different policy graphs. The attendees will find that we can have the high usability for epidemic surveillance while preserving location privacy.
A system design for elastically scaling transaction processing engines in virtualized servers
Authors: Anadiotis Angelos-Christos , Appuswamy Raja , Ailamaki Anastasia , Bronshtein Ilan , Avni Hillel , Dominguez-Sal David , Goikhman Shay , Levy Eliezer
Published: September 2020

Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of redesigning the engine, which has led to the introduction of several, new, cloud-based transaction processing systems mainly focusing on: (i) the transaction coordination protocol, (ii) the data partitioning strategy, and, (iii) the resource isolation across multiple tenants. As a result, standalone OLTP engines cannot be easily deployed with an elastic setting in the cloud and they need to migrate to another, specialized deployment.

In this paper, we focus on workload variations that can be addressed by modern multi-socket, multi-core servers and we present a system design for providing fine-grained elasticity to multi-tenant, scale-up OLTP deployments. We introduce novel components to the virtualization software stack that enable on-demand addition and removal of computing and memory resources. We provide a bi-directional, low-overhead communication stack between the virtual machine and the hypervisor, which allows the former to adapt to variations coming both from the workload and the resource availability. We show that our system achieves NUMA-aware, millisecond-level, stateful and fine-grained elasticity, while it is not intrusive to the design of state-of-the-art, in-memory OLTP engines. We evaluate our system through novel use cases demonstrating that scale-up elasticity increases resource utilization, while allowing tenants to pay for actual use of resources and not just their reservation.
Oracle AutoML
Authors: Yakovlev Anatoly , Moghadam Hesam Fathi , Moharrer Ali , Cai Jingxiao , Chavoshi Nikan , Varadarajan Venkatanathan , Agrawal Sandeep R. , Idicula Sam , Karnagel Tomas , Jinturkar Sanjay , Agarwal Nipun
Published: September 2020

Machine learning (ML) is at the forefront of the rising popularity of data-driven software applications. The resulting rapid proliferation of ML technology, explosive data growth, and shortage of data science expertise have caused the industry to face increasingly challenging demands to keep up with fast-paced develop-and-deploy model lifecycles. Recent academic and industrial research efforts have started to address this problem through automated machine learning (AutoML) pipelines and have focused on model performance as the first-order design objective. We present Oracle AutoML, a novel
iteration-free
AutoML pipeline designed to not only provide accurate models, but also in a shorter runtime. We are able to achieve these objectives by eliminating the need to continuously iterate over various pipeline configurations. In our feed-forward approach, each pipeline stage makes decisions based on metalearned proxy models that can predict candidate pipeline configuration performances before building the full final model. Our approach, which builds and tunes only the best candidate pipeline, achieves better scores at a fraction of the time compared to state-of-the-art open source AutoML tools, such as H2O and Auto-sklearn. This makes Oracle AutoML a prime candidate for addressing current industry challenges.
Robust query processing
Authors: Haritsa Jayant R.
Published: September 2020

Robust query processing with strong performance guarantees is an extremely desirable objective in the design of industrial-strength database engines. However, it has proved to be a largely intractable and elusive challenge in spite of sustained efforts spanning several decades. The good news is that in recent times, there have been a host of exciting technical advances, at different levels in the database architecture, that collectively promise to materially address this problem. In this tutorial, we will present these novel research approaches, characterize their strengths and limitations, and enumerate open technical problems that remain to be solved to make robust query processing a contemporary reality.
JIT works
Authors: Ailamaki Anastasia
Published: September 2020

When the time comes to make a critical decision, it is of paramount importance to prepare enough so that all the information necessary is available at decision time. Under-preparation leads to uninformed decisions; over-preparation, however, may lead to conservative decisions. Often a decision seems suboptimal after its consequences are known; regret is a typical reaction in such a situation.

In life and in my research I have often followed the wrong path. More accurately, I have often followed a path that led to suboptimal results or unwanted consequences. The difficulty is to fight the urge to regret and rather
learn
from the process; divide the input that led to the wrong path into the part that could have been made better (and is the reason for the suboptimal outcome) and the part that was already as good as it gets. Next time, prepare less for the former while keeping the latter an invariant.

We are often faced with daunting decisions when designing database systems on modern heterogeneous, ever-faster hardware platforms. After having built hierarchical, object-oriented, relational, and just-in-time (JIT) systems by myself or with a team, I realize that a great deal of the performance is determined less from determining what data model to use or what index to build ahead of time and more from making decisions at the right time. Educated JIT works using mathematics and dynamic programming, and it helps prepare for invariants. We make decisions only when all the information is at hand for the more flexible (and more decisive) part of the input. Even if the response time of the first few requests is high, these serve as "teaching" exercises for the subsequent ones. Tools like code generation and data virtualization help make JIT useful in data management. I hope that our community will build many JIT systems and benefit from the dynamic nature of this philosophy.

As many computer scientists do, I routinely use what I learn in my research to optimize performance in a multi-dimensionally unpredictable everyday life, and vice versa. I've built systems by mapping design choices to life challenges, and have tried to relate to my students' personalities and help them overcome hurdles while filtering through my (often boring, but hopefully useful) personal experiences. In my talk I will share some of these experiences, intertwined with exciting research directions.

I am humbled and grateful to receive the VLDB Women in Database Research award, and I thank the community for this distinction.
Out-of-order execution of database queries
Authors: Goda Kazuo , Hayamizu Yuto , Yamada Hiroyuki , Kitsuregawa Masaru
Published: September 2020

Intra-query parallelism is a key for database software to offer acceptable responsiveness for data-intensive queries. Many researchers have studied how to achieve greater execution parallelism for database queries.
Partitioning
is a representative approach, which divides a query into multiple sub-tasks and executes them in parallel. However, given a new query, optimal division is not necessarily obvious. Database software utilizes heuristic rules or statistical information to decide how to divide the query before execution. As yet another approach to achieve execution parallelism, this paper presents
out-of-order database execution
(OoODE), a massively-parallel query execution method to offer significant speedup for database queries consistently. OoODE dynamically decomposes query work by making the best use of the exact knowledge of the potential execution parallelism for each operation ready to be performed during query execution. With OoODE, the database software is allowed to automatically squeeze out the execution parallelism that the query inherently holds. Hence, for a wide spectrum of queries, OoODE performs significantly faster than the serial (non-parallelized) execution, while it performs better than or comparably with alternative parallelizing methods without the need for dividing the query before execution. This paper presents the experiments that we conducted using the prototyped database software and demonstrates that OoODE is two to three orders of magnitude faster than the serial execution, whereas it is substantially (up to 2.07 times) faster than the best achievable case of partitioning. Besides, OoODE performs two to four orders of magnitude faster than major DBMSs.
Die Hiatregel in den Jamben von Gregor von Nazianz
Authors: De Stefani Claudio
Published: September 2020

In the Iambs of Gregory of Nazianzus occur many hiatuses: this might suggest that his verses had been composed with carelessness. In fact, if we examine the various kinds of hiatuses, we notice that some of them should not be considered as such, because they occur after words, or along with iuncturae, that usually admit them. There remains, however, a considerable number of hiatus in caesura. The article strives to demonstrate that these hiatuses are due to the imitation of the well-known hiatus in trocaic caesura in the hexameter, which was allowed since Homer’s age. As a matter of fact, in some cases, the same words that produce a hiatus after a trochaic caesura in Homer show an identical one after a caesura in Gregory’s trimeters. Moreover, hexameter and iambic trimeter were frequently juxtaposed by the late ancient metric-grammatical tradition. The article further analyzes the occurence of the hiatus in caesura in the Byzantine dodecasyllables, which, among other reasons, might be due also to the influence of Gregory in Byzantine poetry.
Old statues, new meanings. Literary, epigraphic and archaeological evidence for Christian reidentification of statuary
Authors: Jacobs Ine
Published: September 2020

This article examines literary, epigraphic and archaeological evidence for the Christian reidentification of statuary and reliefs as biblical scenes and protagonists, saints and angels. It argues that Christian identifications were promulgated, amongst others by local bishops, to make sense of imagery of which the original identity had been lost and/or was no longer meaningful. Three conditions for a new identification are discussed: the absence of an epigraphic label, geographical and/or chronological distance separating the statue from its original context of display, and the presence of a specific attribute or characteristic that could become the prompt for reidentification. In their manipulation and modernization of older statuary, Christians showed a much greater appreciation of the statuary medium than generally assumed.