bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of lockdown on activities of daily living in built environment and well-being
Authors: Pasala Sudhir Kumar , Gumpeny Lakshmi , Kosuri Madhu , Tippana Snehalatha , Sridhar Gumpeny, R
Published: September 2020

In an effort to arrest the spread of COVID-19 infection, a nation-wide lockdown was declared in India in March 2020. To assess how personal built environment affected the citizens in the first few weeks, an explorative online survey was conducted, eliciting responses about the work habits before the lockdown, the psychological well-being, time spent in various activities, characteristics of those who worked from home and sleep patterns. The major difference entailed by thelockdown was a reduction of time and distance to go to their workplace, which was an average of 8.9 km. In terms of diet, subjects who were vegetarian did not experience any difference, unlike those who were non-vegetarians, who reduced the intake of meat. Forced social isolation did not alter the television channels that were viewed. Among those who worked from home, most preferred to work from their bedroom. There was no change in the quality or quantity of sleep during the lockdown. This study in the early weeks of the lockdown documents the way in which individuals lived through it in terms of the built environment at home.
Los BRT ¿nuevo paradigma de la movilidad urbana mundial?
Authors: Rosas Gutiérrez Jorge , Chías Becerril Luis
Published: September 2020

El objetivo principal del estudio es analizar el ascenso y la consolidación de un nuevo paradigma de movilidad urbana mundial. La investigación se desarrollará desde el enfoque de la geografía urbana, porque se realizará un análisis urbano espacial de los BRT (en inglés, Bus Rapit Transit) a nivel mundial, por región y ciudad. Hay una escasez de investigaciones de transporte que expliquen el cambio de paradigma de trenes urbanos por autobuses tipo BRT. Los resultados de los BRT no han sido tan exitosos como se dice, este tipo de autobuses se han extendido en gran parte del mundo, impulsados por diversas instituciones (ONG, AC y empresas privadas) que alientan a los gobiernos a implementarlos. Es evidente que muchas de las ciudades donde funcionan los BRT figuran como las más contaminadas, con graves problemas de congestionamiento y accidentes viales, e incluso con amenazas de colapso en las redes BRT. En el caso concreto de México se sugiere que los BRT no deben diseñarse de manera unimodal, ni proponerse como sustitutos del metro. Sin duda son un valioso componente del sistema de transporte de la Zona Metropolitana del Valle de México (ZMVM), pero difícilmente pueden ser el eje vertebral de todo el sistema.
Planejamento e controle em sistema integrado de produção agrícola do cerrado mato-grossense
Authors: Souza Costa Patrícia , Da Silva Ribeiro Taire , Lupi Gasparini Liz Vanessa , Minetto Sznitowski Adelice
Published: September 2020

Sistemas integrados de produção agrícola (SINPA) têm gerado ganhos financeiros, sociais e ambientais, mas necessitam de gestão integrada. Este estudo de caso explora o planejamento e o controle da produção (PCP) em uma empresa rural do cerrado mato-grossense, analisando tecnologias, processos produtivos e resultados dos sistemas integrado e convencional. Os resultados demonstram que o SINPA exigiu tecnologias, aumentou a produtividade da lavoura e permitiu incentivos fiscais à floresta. Apesar de o sistema produtivo ser integrado, seu PCP é realizado separadamente, o que prejudica obter ganhos de otimização. O PCP integrado pode aprimorar este sistema produtivo estratégico ao desenvolvimento rural e do agronegócio. A gestão de empresas rurais precisa avançar, em busca de rentabilidade, tecnologias de gestão de recursos e de processos capazes de gerá-la.
An analysis of project selection and assignment criteria of Danish tenders in Europe
Authors:   Department of Mechanical and Manufacturing Engineering, Denmark, Larsen Jesper , Ussing Lene ,   Department of Mechanical and Manufacturing Engineering, Denmark, Bruno Thomas ,   Department of Mechanical and Manufacturing Engineering, Denmark
Published: September 2020

Public construction agencies are one of the largest developers within the Danish construction industry, where such agencies own and develop new public construction projects. Most of these projects are put out in European tender. This study analyses the selection and assignment criteria employed by these agencies in different types of public sector projects.Some of the objectives pursued by the study include the determination of 1/ the selection and assignment criteria mostly used in Danish public tenders 2/how different types of projects use selection and assignment criteria in the bidding process, and 3/any significant difference between the use of selection and assignment criteria in Danish public construction projects. The study uses a quantitative research approach where 157 Danish public tender cases were selected from the European Tenders Electronic Daily database between the period: January 2010 to March 2013. Fisher's Exact Test was conducted to determine if there was any significant use of some selection and assignment criteria. The findings of the study showed that invited tenders with pre-qualification and lowest pricein 69.8% of the tenders are the most used selection and assignment criteria, with little regard to project type.
SmartBench
Authors: Gupta Peeyush , Carey Michael J. , Mehrotra Sharad , Yus oberto
Published: September 2020

This paper proposes SmartBench, a benchmark focusing on queries resulting from (near) real-time applications and longer-term analysis of IoT data. SmartBench, derived from a deployed smart building monitoring system, is comprised of: 1) An extensible schema that captures the fundamentals of an IoT smart space; 2) A set of representative queries focusing on analytical tasks; and 3) A data generation tool that generates large amounts of synthetic sensor and semantic data based on seed data collected from a real system. We present an evaluation of seven representative database systems and highlight some interesting findings that can be considered when deciding what database technologies to use under different types of IoT query workloads.
Adopting worst-case optimal joins in relational database systems
Authors: Freitag Michael , Bandle Maximilian , Schmidt Tobias , Kemper Alfons , Neumann Thomas
Published: September 2020

Worst-case optimal join algorithms are attractive from a theoretical point of view, as they offer asymptotically better runtime than binary joins on certain types of queries. In particular, they avoid enumerating large intermediate results by processing multiple input relations in a single multi-way join. However, existing implementations incur a sizable overhead in practice, primarily since they rely on suitable ordered index structures on their input. Systems that support worst-case optimal joins often focus on a specific problem domain, such as read-only graph analytic queries, where extensive precomputation allows them to mask these costs.

In this paper, we present a comprehensive implementation approach for worst-case optimal joins that is practical within general-purpose relational database management systems supporting both hybrid transactional and analytical workloads. The key component of our approach is a novel hash-based worst-case optimal join algorithm that relies only on data structures that can be built efficiently during query execution. Furthermore, we implement a hybrid query optimizer that intelligently and transparently combines both binary and multi-way joins within the same query plan. We demonstrate that our approach far outperforms existing systems when worst-case optimal joins are beneficial while sacrificing no performance when they are not.
A workload-adaptive mechanism for linear queries under local differential privacy
Authors: McKenna Ryan , Maity Raj Kumar , Mazumdar Arya , Miklau Gerome
Published: September 2020

We propose a new mechanism to accurately answer a user-provided set of linear counting queries under local differential privacy (LDP). Given a set of linear counting queries (the workload) our mechanism automatically adapts to provide accuracy on the workload queries. We define a parametric class of mechanisms that produce unbiased estimates of the workload, and formulate a constrained optimization problem to select a mechanism from this class that minimizes expected total squared error. We solve this optimization problem numerically using projected gradient descent and provide an efficient implementation that scales to large workloads. We demonstrate the effectiveness of our optimization-based approach in a wide variety of settings, showing that it outperforms many competitors, even outperforming existing mechanisms on the workloads for which they were intended.
Evaluating top-k queries with inconsistency degrees
Authors: Issa Ousmane , Bonifati Angela , Toumani Farouk
Published: September 2020

We study the problem of augmenting relational tuples with inconsistency awareness and tackling top-k queries under a set of denial constraints (DCs). We define a notion of inconsistent tuples with respect to a set of DCs and define two measures of inconsistency degrees, which consider single and multiple violations of constraints. In order to compute these measures, we leverage two models of provenance, namely why-provenance and provenance polynomials. We investigate top-k queries that allow to rank the answer tuples by their inconsistency degrees. Since one of our measure is monotonic and the other non-monotonic, we design an integrated top-k algorithm to compute the top-k results of a query w.r.t. both inconsistency measures. By means of an extensive experimental study, we gauge the effectiveness of inconsistency-aware query answering and the efficiency of our algorithm with respect to a baseline, where query results are fully computed and ranked afterwards.
Cerebro
Authors: Nakandala Supun , Zhang Yuhao , Kumar Arun
Published: September 2020

Deep neural networks (deep nets) are revolutionizing many machine learning (ML) applications. But there is a major bottleneck to wider adoption: the pain and resource intensiveness of
model selection.
This empirical process involves exploring deep net architectures and hyper-parameters, often requiring hundreds of trials. Alas, most ML systems focus on training one model at a time, reducing throughput and raising overall resource costs; some also sacrifice reproducibility. We present Cerebro, a new data system to raise deep net model selection throughput at scale without raising resource costs and without sacrificing reproducibility or accuracy. Cerebro uses a new parallel SGD execution strategy we call
model hopper parallelism
that hybridizes task- and data-parallelism to mitigate the cons of these prior paradigms and offer the best of both worlds. Experiments on large ML benchmark datasets show that Cerebro offers 3x to 10x runtime savings relative to data-parallel systems like Horovod and Parameter Server and up to 8x memory/storage savings or up to 100x network savings relative to task-parallel systems. Cerebro also supports heterogeneous resources and fault tolerance.
Efficiently approximating selectivity functions using low overhead regression models
Authors: Dutt Anshuman , Wang Chi , Narasayya Vivek , Chaudhuri Surajit
Published: September 2020

Today's query optimizers use fast selectivity estimation techniques but are known to be susceptible to large estimation errors. Recent work on supervised learned models for selectivity estimation significantly improves accuracy while ensuring relatively low estimation overhead. However, these models impose significant model construction cost as they need large numbers of training examples and computing selectivity labels is costly for large datasets. We propose a novel model construction method that incrementally generates training data and uses approximate selectivity labels, that reduces total construction cost by an order of magnitude while preserving most of the accuracy gains. The proposed method is particularly attractive for model designs that are faster-to-train for a given number of training examples, but such models are known to support a limited class of query expressions. We broaden the applicability of such supervised models to the class of select-project-join query expressions with range predicates and IN clauses. Our extensive evaluation on synthetic benchmark and real-world queries shows that the 95th-percentile error of our proposed models is 10-100X better than traditional selectivity estimators. We also demonstrate significant gains in plan quality as a result of improved selectivity estimates.
SSTD
Authors: Chen Yue , Chen Zhida , Cong Gao , Mahmood Ahmed R. , Aref Walid G.
Published: September 2020

Streaming
spatio-textual data
that contains geolocations and textual contents, e.g., geo-tagged tweets, is becoming increasingly available. Users can register
continuous queries
to receive up-to-date results continuously, or pose
snapshot queries
to receive results instantly. The large scale of spatio-textual data streams and huge amounts of queries pose great challenges to the current location-based services, and call for more efficient data management systems. In this paper, we present SSTD (Streaming Spatio-Textual Data), a distributed in-memory system supporting both continuous and snapshot queries with spatial, textual, and temporal constraints over data streams. Compared with existing distributed data stream management systems, SSTD has at least three novelty: (1) It supports more types of queries over streamed
spatio-textual data
; (2)SSTD adopts a novel workload partitioning method termed QT (Quad-Text) tree, that utilizes the joint distribution of queries and spatio-textual data to reduce query latency and enhance system throughput. (3) To achieve load balance and robustness, we develop three new workload adjustment methods for SSTD to fit the changes in the distributions of data or queries. Extensive experiments on real-life datasets demonstrate the superior performance of SSTD.
Continuous prefetch for interactive data applications
Authors: Mohammed Haneen , Wei Ziyun , Wu Eugene , Netravali Ravi
Published: September 2020

Interactive data visualization and exploration (DVE) applications are often network-bottlenecked due to bursty request patterns, large response sizes, and heterogeneous deployments over a range of networks and devices. This makes it difficult to ensure consistently low response times (< 100ms). Khameleon is a framework for DVE applications that uses a novel combination of prefetching and response tuning to dynamically trade-off response quality for low latency.

Khameleon exploits DVE's approximation tolerance: immediate lower-quality responses are preferable to waiting for complete results. To this end, Khameleon progressively encodes responses, and runs a server-side scheduler that proactively streams portions of responses using available bandwidth to maximize user-perceived interactivity. The scheduler involves a complex optimization based on available resources, predicted user interactions, and response quality levels; yet, decisions must also be made in real-time. To overcome this, Khameleon uses a fast greedy heuristic that closely approximates the optimal approach. Using image exploration and visualization applications with real user interaction traces, we show that across a wide range of network and client resource conditions, Khameleon outperforms existing prefetching approaches that benefit from
perfect
prediction models: Khameleon always lowers response latencies (typically by 2--3 orders of magnitude) while keeping response quality within 50--80%.
Stable learned bloom filters for data streams
Authors: Liu Qiyu , Zheng Libin , Shen Yanyan , Chen Lei
Published: September 2020

Bloom filter and its variants are elegant space-efficient probabilistic data structures for approximate set membership queries. It has been recently shown that the space cost of Bloom filters can be significantly reduced via a combination with pre-trained machine learning models, named Learned Bloom filters (LBF). LBF eases the space requirement of a Bloom filter by undertaking part of the queries using a classifier. However, current LBF structures generally target a static member set. Their performances would inevitably decay when there is a member update on the set, while this update requirement is not uncommon for real-world data streaming applications such as duplicate item detection, malicious URL checking, and web caching. To adapt LBF to data streams, we propose the Stable Learned Bloom Filters (SLBF) which addresses the performance decay issue on intensive insertion workloads by combining classifier with updatable backup filters. Specifically, we propose two SLBF structures, Single SLBF (s-SLBF) and Grouping SLBF (g-SLBF). The theoretical analysis on these two structures shows that the expected false positive rate (FPR) of SLBF is asymptotically a constant over the insertion of new members. Extensive experiments on real-world datasets show that SLBF introduces a similar level of false negative rate (FNR) but yields a better FPR/storage trade-off compared with the state-of-the-art (non-learned) Bloom filters optimized on data streams.
ODIN
Authors: Suprem Abhijit , Arulraj Joy , Pu Calton , Ferreira Joao
Published: September 2020

Recent advances in computer vision have led to a resurgence of interest in visual data analytics. Researchers are developing systems for effectively and efficiently analyzing visual data at scale. A significant challenge that these systems encounter lies in the drift in real-world visual data. For instance, a model for self-driving vehicles that is not trained on images containing snow does not work well when it encounters them in practice. This drift phenomenon limits the accuracy of models employed for visual data analytics.

In this paper, we present a visual data analytics system, called Odin, that automatically detects and recovers from drift. Odin uses adversarial autoencoders to learn the distribution of high-dimensional images. We present an unsupervised algorithm for detecting drift by comparing the distributions of the given data against that of previously seen data. When Odin detects drift, it invokes a drift recovery algorithm to deploy specialized models tailored towards the novel data points. These specialized models outperform their non-specialized counterpart on accuracy, performance, and memory footprint. Lastly, we present a model selection algorithm for picking an ensemble of best-fit specialized models to process a given input. We evaluate the efficacy and efficiency of Odin on high-resolution dashboard camera videos captured under diverse environments from the Berkeley DeepDrive dataset. We demonstrate that Odin's models deliver 6X higher throughput, 2X higher accuracy, and 6X smaller memory footprint compared to a baseline system without automated drift detection and recovery.
Fair task assignment in spatial crowdsourcing
Authors: Chen Zhao , Cheng Peng , Chen Lei , Lin Xuemin , Shahabi Cyrus
Published: September 2020

With the pervasiveness of mobile devices, wireless broadband and sharing economy, spatial crowdsourcing is becoming part of our daily life. Existing studies on spatial crowdsourcing usually focus on enhancing the platform interests and customer experiences. In this work, however, we study the fair assignment of tasks to workers in spatial crowdsourcing. That is, we aim to assign tasks, considered as a resource in short supply, to individual spatial workers in a fair manner. In this paper, we first formally define an online bi-objective matching problem, namely the Fair and Effective Task Assignment (FETA) problem, with its special cases/variants of it to capture most typical spatial crowdsourcing scenarios. We propose corresponding solutions for each variant of FETA. Particularly, we show that the dynamic sequential variant, which is a generalization of an existing fairness scheduling problem, can be solved with an
O
(
n
) fairness cost bound (
n
is the total number of workers), and give an
O
(
n/m
) fairness cost bound for the
m
-sized general batch case (
m
is the minimum batch size). Finally, we evaluate the effectiveness and efficiency of our algorithm on both synthetic and real data sets.
Scrutinizer
Authors: Karagiannis Georgios , Saeed Mohammed , Papotti Paolo , Trummer Immanuel
Published: September 2020

Organizations spend significant amounts of time and money to manually fact check text documents summarizing data. The goal of the Scrutinizer system is to reduce verification overheads by supporting human fact checkers in translating text claims into SQL queries on an database. Scrutinizer coordinates teams of human fact checkers. It reduces verification time by proposing queries or query fragments to the users. Those proposals are based on claim text classifiers, that gradually improve during the verification of a large document. In addition, Scrutinizer uses tentative execution of query candidates to narrow down the set of alternatives. The verification process is controlled by a cost-based optimizer. It optimizes the interaction with users and prioritizes claim verifications. For the latter, it considers expected verification overheads as well as the expected claim utility as training samples for the classifiers. We evaluate the Scrutinizer system using simulations and a user study with professional fact checkers, based on actual claims and data. Our experiments consistently demonstrate significant savings in verification time, without reducing result accuracy.
Optimizing DNN computation graph using graph substitutions
Authors: Fang Jingzhi , Shen Yanyan , Wang Yue , Chen Lei
Published: September 2020

Deep learning has achieved great success in various real-world applications. As deep neural networks (DNNs) are getting larger, the inference and training cost of DNNs increases significantly. Since one round of inference or one iteration in the training phase of a DNN is typically modeled as a computation graph, existing works propose to optimize computation graphs by performing a sequence of functionally equivalent graph substitutions, leading to higher inference and training efficiency. In this work, we formally define the Optimizing Computation Graph using Graph Substitutions (OCGGS) problem, and prove it to be NP-hard and Poly-APX-complete. We develop two exact and efficient methods to the OCGGS problem. The pruning-based algorithm eliminates the examination of redundant graph substitution sequences, and the dynamic programming with pruning algorithm makes use of the explored graph substitutions. To further speed up the search process, we propose a sampling heuristic which is effective to optimize complex computation graphs with polynomial time and space complexity. Extensive experiments on various DNN architectures and sizes are conducted to verify the effectiveness and efficiency of our proposed solutions compared with existing techniques.
CheetahVIS
Authors: Ning Wentao , Tang Qiandong , Zhao Yi , Yang Chuan , Wang Xiaofeng , Wang Teng , Liu Haotian , Zhang Chaozu , Zhou Zhiyuan , Shen Qiaomu , Tang Bo
Published: September 2020

Recently, the spatial-temporal data of urban moving objects, e.g., cars and buses, are collected and widely used in urban trajectory exploratory analysis. Urban bus service is one of the most common public transportation services. Urban bus data analysis plays an important role in smart city applications. For example, data analysts in bus companies use the urban bus data to optimize their bus scheduling plan. Map services providers, e.g., Google map, Ten-cent map, take urban bus data into account to improve their service quality (e.g., broadcast road update instantly). Unlike urban moving cars or pedestrians, urban buses travel on known bus routes. The operating buses form the "bus flows" in a city. Efficient analyzing urban bus flows has many challenges, e.g., how to analyze the dynamics of given bus routes? How to help users to identify traffic flow of interests easily?

In this work, we present CheetahVIS, a visual analytical system for efficient massive urban bus data analysis. CheetahVIS builds upon Spark and provides a visual analytical platform for the stakeholders (e.g., city planner, data analysts in bus company) to conduct effective and efficient analytical tasks. In the demonstration, demo visitors will be invited to experience our proposed CheetahVIS system with different urban bus data analytical functions, e.g., bus route analysis, public bus flow overview, multiple region analysis, in a real-world dataset. We also will present a case study, which compares different regions in a city, to demonstrate the effectiveness of CheetahVIS.
PiBench online
Authors: Hao Xiangpeng , Lersch Lucas , Wang Tianzheng , Oukid Ismail
Published: September 2020

The emergence of persistent memory (PM), such as Intel Optane DC Persistent Memory Modules (DCPMM), opened up many opportunities for building high-performance indexes directly on PM. However, the many PM indexes proposed by prior work had their evaluation based on PM emulation using DRAM and therefore it was not clear how they would perform on real PM hardware. Moreover, they typically used ad hoc, in-house benchmarks and did not collect PM-specific hardware metrics that are key performance indicators and are instrumental for users and developers to understand the performance behavior of PM indexes. These issues call for a systematic, fair and reproducible approach for evaluating PM indexes.

This demonstration highlights the principles and lessons learned from our recent evaluation of PM indexes on real DCPMM and showcases PiBench, a unified benchmarking framework that enables fair and reproducible evaluation of PM indexes. In addition to common metrics, PiBench uniquely integrates monitoring tools to collect PM-specific hardware counters, allowing in-depth performance analysis. Our demonstration is enabled by PiBench Online, a new interactive system built on top of PiBench. Using PiBench Online, users can upload their own index implementations, run preset or customized workloads, and analyze results interactively, all through an easy-to-use web interface. PiBench is open-source and PiBench Online is deployed at https://pibench.org. We hope PiBench Online can promote fair comparison and reproducibility in database and systems communities.
SuDocu
Authors: Fariha Anna , Brucato Matteo , Haas Peter J. , Meliou Alexandra
Published: September 2020

Text document summarization refers to the task of producing a brief representation of a document for easy human consumption. Existing text summarization techniques mostly focus on generic summarization, but users often require
personalized
summarization that targets their specific preferences and needs. However, precisely expressing preferences is challenging, and current methods are often ambiguous, outside the user's control, or require costly training data. We propose a novel and effective way to express summarization intent (preferences) via
examples
: the user provides a few example summaries for a small number of documents in a collection, and the system summarizes the rest. We demonstrate SuDocu, an example-based personalized Document Summarization system. Through a simple interface, SuDocu allows the users to provide example summaries, learns the summarization intent from the examples, and produces summaries for new documents that reflect the user's summarization intent. SuDocu further explains the captured summarization intent in the form of a
package query
, an extension of a traditional SQL query that handles complex constraints and preferences over answer sets. SuDocu combines topic modeling, semantic similarity discovery, and in-database optimization in a novel way to achieve example-driven document summarization. We demonstrate how SuDocu can detect complex summarization intents from a few example summaries and produce accurate summaries for new documents effectively and efficiently.
BitFun
Authors: Zalipynis Ramon Antonio Rodriges
Published: September 2020

Geospatial array DBMSs handle big georeferenced arrays. Due to the geospatial data peculiarities, many queries have tunable parameters with values not known in advance: users gradually tune them until they get a satisfactory result. This generates a series of queries with slightly different structures and very similar outputs. Modern array DBMSs spend the same efforts to answer each such query. BitFun provides novel bitmap indexing strategies to continuously re-index arrays during queries with similar mathematical functions. It can be up to 8x faster than computing the results from scratch. We describe BitFun and offer lessons on real-world geospatial data, related to real practical tasks. A lesson involves tuning a math function parameter while the rich web GUI details the indexing process and query execution. Conference attendees will appreciate BitFun approaches, its performance, and learn its internals via fascinating lessons.
X
2
R
2
Authors: Hagedoorn Tom Rolandus , Kumar Rohit , Bonchi Francesco
Published: September 2020

Reidentification-risk analysis and anonymity have received a great deal of attention in the last two decades. While the research community has been developing several privacy notions and the algorithms to achieve them, these tools have faced difficulties in being transferred to the wider audience of practitioners, for they require a considerable amount of data privacy technical knowledge.

We demonstrate X
2
R
2
(Explainable Explorative Reidentification Risk), a
data anonymization tool for the laymen.
X
2
R
2
guides the user through a transparent explorative process, during which the existing reidentification risks are explained and quantified, possible data transformation options are recommended, and the consequences of these operations, in terms of privacy risk and data utility, are clearly shown.
DeepTRANS
Authors: Tran Luan , Mun Min Y. , Lim Matthew , Yamato Jonah , Huh Nathan , Shahabi Cyrus
Published: September 2020

In the public transportation domain, accurate estimation of travel times helps to manage rider expectations as well as to provide a powerful tool for transportation agencies to coordinate the public transport vehicles. Although many statistical and machine learning methods have been proposed to estimate travel times, none of the methods consider utilizing predicted traffic information. Forecasting how congestion is going to evolve is critical for accurate travel time estimations. In this paper, we present DeepTRANS, which incorporates traffic forecasting information to our prior Deep Learning-based Bus Estimated Time of Arrival (ETA) model, increasing its accuracy by 21% in estimating bus travel time.
Asymmetric-partition replication for highly scalable distributed transaction processing in practice
Authors: Lee Juchang , Kim Kyu Hwan , Lee Hyejeong , Andrei Mihnea , Ko Seongyun , Keller Friedrich , Han Wook-Shin
Published: September 2020

Database replication is widely known and used for high availability or load balancing in many practical database systems. In this paper, we show how a replication engine can be used for three important practical cases that have not previously been studied very well. The three practical use cases include: 1) scaling out OLTP/OLAP-mixed workloads with partitioned replicas, 2) efficiently maintaining a distributed secondary index for a partitioned table, and 3) efficiently implementing an online re-partitioning operation. All three use cases are crucial for enabling a high-performance shared-nothing distributed database system. To support the three use cases more efficiently, we propose the concept of
asymmetric-partition replication
, so that replicas of a table can be independently partitioned regardless of whether or how its primary copy is partitioned. In addition, we propose the
optimistic synchronous commit
protocol which avoids the expensive two-phase commit without sacrificing transactional consistency. The proposed asymmetric-partition replication and its optimized commit protocol are incorporated in the production versions of the SAP HANA in-memory database system. Through extensive experiments, we demonstrate the significant benefits that the proposed replication engine brings to the three use cases.
AGL
Authors: Zhang Dalong , Huang Xin , Liu Ziqi , Zhou Jun , Hu Zhiyang , Song Xianzheng , Ge Zhibang , Wang Lin , Zhang Zhiqiang , Qi Yuan
Published: September 2020

Machine learning over graphs has been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance, parameter server that assumes data parallelism. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are three-fold. First, they cannot scale because of the limitations on the volume of the memories, or the bandwidth between graph stores and workers. Second, they require extra development of graph stores without well exploiting mature infrastructures such as MapReduce that guarantee good system properties. Third, they focus on training but ignore optimizing the performance of inference over graphs, thus makes them an unintegrated system.

In this paper, we design AGL, a scalable and integrated system, with fully-functional training and inference for GNNs. Our system design follows the message passing scheme underlying the computations of GNNs. We design to generate the
K
-hop neighborhood, an information-complete subgraph for each node, as well as do the inference simply by merging values from in-edge neighbors and propagating values to out-edge neighbors via MapReduce. In addition, the
K
-hop neighborhood contains information-complete subgraphs for each node, thus we simply do the training on parameter servers due to data independence. Our system AGL, implemented on mature infrastructures, can finish the training of a 2-layer GNN on a graph with billions of nodes and hundred billions of edges in 14 hours, and complete the inference in 1.2 hours.
POLARIS
Authors: Aguilar-Saborit Josep , Ramakrishnan Raghu , Srinivasan Krish , Bocksrocker Kevin , Alagiannis Ioannis , Sankara Mahadevan , Shafiei Moe , Blakeley Jose , Dasarathy Girish , Dash Sumeet , Davidovic Lazar , Damjanic Maja , Djunic Slobodan , Djurkic Nemanja , Feddersen Charles , Galindo-Legaria Cesar , Halverson Alan , Kovacevic Milana , Kicovic Nikola , Lukic Goran , Maksimovic Djordje , Manic Ana , Markovic Nikola , Mihic Bosko , Milic Ugljesa , Milojevic Marko , Nayak Tapas , Potocnik Milan , Radic Milos , Radivojevic Bozidar , Rangarajan Srikumar , Ruzic Milan , Simic Milan , Sosic Marko , Stanko Igor , Stikic Maja , Stanojkov Sasa , Stefanovic Vukasin , Sukovic Milos , Tomic Aleksandar , Tomic Dragan , Toscano Steve , Trifunovic Djordje , Vasic Veljko , Verona Tomer , Vujic Aleksandar , Vujic Nikola , Vukovic Marko , Zivanovic Marko
Published: September 2020

In this paper, we describe the Polaris distributed SQL query engine in Azure Synapse. It is the result of a multi-year project to re-architect the query processing framework in the SQL DW parallel data warehouse service, and addresses two main goals: (i) converge data warehousing and big data workloads, and (ii) separate compute and state for cloud-native execution.

From a customer perspective, these goals translate into many useful features, including the ability to resize live workloads, deliver predictable performance at scale, and to efficiently handle both relational and unstructured data. Achieving these goals required many innovations, including a novel "cell" data abstraction, and flexible, fine-grained, task monitoring and scheduling capable of handling partial query restarts and PB-scale execution. Most importantly, while we develop a completely new scale-out framework, it is fully compatible with T-SQL and leverages decades of investment in the SQL Server single-node runtime and query optimizer. The scalability of the system is highlighted by a 1PB scale run of all 22 TPC-H queries; to our knowledge, this is the first reported run with scale larger than 100TB.
Alibaba hologres
Authors: Jiang Xiaowei , Hu Yuejun , Xiang Yu , Jiang Guangran , Jin Xiaojun , Xia Chen , Jiang Weihua , Yu Jun , Wang Haitao , Jiang Yuan , Ma Jihong , Su Li , Zeng Kai
Published: September 2020

In existing big data stacks, the processes of analytical processing and knowledge serving are usually separated in different systems. In Alibaba, we observed a new trend where these two processes are fused: knowledge serving incurs generation of new data, and these data are fed into the process of analytical processing which further fine tunes the knowledge base used in the serving process. Splitting this fused processing paradigm into separate systems incurs overhead such as extra data duplication, discrepant application development and expensive system maintenance.

In this work, we propose Hologres, which is a cloud native service for hybrid serving and analytical processing (HSAP). Hologres decouples the computation and storage layers, allowing flexible scaling in each layer. Tables are partitioned into self-managed shards. Each shard processes its read and write requests concurrently independent of each other. Hologres leverages hybrid row/column storage to optimize operations such as point lookup, column scan and data ingestion used in HSAP. We propose
Execution Context
as a resource abstraction between system threads and user tasks. Execution contexts can be cooperatively scheduled with little context switching overhead. Queries are parallelized and mapped to execution contexts for concurrent execution. The scheduling framework enforces resource isolation among different queries and supports customizable schedule policy. We conducted experiments comparing Hologres with existing systems specifically designed for analytical processing and serving workloads. The results show that Hologres consistently outperforms other systems in both system throughput and end-to-end query latency.
Similarity query processing for high-dimensional data
Authors: Qin Jianbin , Wang Wei , Xiao Chuan , Zhang Ying
Published: September 2020

Similarity query processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications. Recently, embedding and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing for high-dimensional data. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. In this tutorial, we summarize existing solutions, especially recent advancements from both database (DB) and machine learning (ML) communities, and analyze their strengths and weaknesses. We review exact and approximate methods such as cover tree, locality sensitive hashing, product quantization, and proximity graphs. We also discuss the selectivity estimation problem and show how researchers are bringing in state-of-the-art ML techniques to address the problem. By highlighting the strong connections between DB and ML, we hope that this tutorial provides an impetus towards new ML for DB solutions and vice versa.
Dremel
Authors: Melnik Sergey , Gubarev Andrey , Long Jing Jing , Romer Geoffrey , Shivakumar Shiva , Tolton Matt , Vassilakis Theo , Ahmadi Hossein , Delorey Dan , Min Slava , Pasumansky Mosha , Shute Jeff
Published: September 2020

Google's Dremel was one of the first systems that combined a set of architectural principles that have become a common practice in today's cloud-native analytics tools, including disaggregated storage and compute, in situ analysis, and columnar storage for semistructured data. In this paper, we discuss how these ideas evolved in the past decade and became the foundation for Google BigQuery.
The relational data borg is learning
Authors: Olteanu Dan
Published: September 2020

This paper overviews an approach that addresses machine learning over relational data as a database problem. This is justified by two observations. First, the input to the learning task is commonly the result of a feature extraction query over the relational data. Second, the learning task requires the computation of group-by aggregates. This approach has been already investigated for a number of supervised and unsupervised learning tasks, including: ridge linear regression, factorisation machines, support vector machines, decision trees, principal component analysis, and k-means; and also for linear algebra over data matrices.

The main message of this work is that the runtime performance of machine learning can be dramatically boosted by a toolbox of techniques that exploit the knowledge of the underlying data. This includes theoretical development on the algebraic, combinatorial, and statistical structure of relational data processing and systems development on code specialisation, low-level computation sharing, and parallelisation. These techniques aim at lowering both the complexity and the constant factors of the learning time.

This work is the outcome of extensive collaboration of the author with colleagues from RelationalAI, in particular Mahmoud Abo Khamis, Molham Aref, Hung Ngo, and XuanLong Nguyen, and from the FDB research project, in particular Ahmet Kara, Milos Nikolic, Maximilian Schleich, Amir Shaikhha, Jakub Závodný, and Haozhe Zhang. The author would also like to thank the members of the FDB project for the figures and examples used in this paper.

The author is grateful for support from industry: Amazon Web Services, Google, Infor, LogicBlox, Microsoft Azure, RelationalAI; and from the funding agencies EPSRC and ERC. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 682588.
Racialization and the national body: (Re)defining selves and others in changing contexts of liberal democratic governance
Authors: Delfino Jennifer B. , Kosse Maureen
Published: September 2020

This introduction argues that understanding the co-construction of race, language, and nation is essential to understanding liberal democratic governance in today's world. Using the theories and methods of raciolinguistics, we argue that voicing and resemiotization are important discursive processes that people use to reconstitute selves and Others in relation to liberal democratic ideas about national belonging. Specifically, we examine how racialized redefinitions of “the body” are central to how right and left-leaning groups alike (re)define nationhood, albeit for different ends. We foreground an intersectional, international approach to understanding the role of language in constructing race and vice versa as well as the role of social media in how differently positioned groups seek empowerment.
Existence of almost periodic solutions to some nonautonomous higher-order stochastic difference equations
Authors: Bezandry Paul H.
Published: September 2020

The paper studies the existence of almost periodic solutions to some nonautonomous higher-order stochastic difference equation of the form:

X

(

t
+
n

)

+

∑

r
=
1

n
-
1

A

r

(
t
)

X

(

t
+
r

)

+

A

0

(
t
)

X

(
t
)

=
f

(

t
,
X

(
t
)

)

,

X\left( {t + n} \right) + \sum\limits_{r = 1}^{n - 1} {{A_r}\left( t \right)X\left( {t + r} \right) + {A_0}\left( t \right)X\left( t \right) = f\left( {t,X\left( t \right)} \right),}

n ∈ 𝕑, by means of discrete dichotomy techniques.
Influence of plutonyl ion on electrochemical characterization of zirconium in plutonium nitrate solutions
Authors: Nakahara Masaumi , Sano Yuichi , Nomura Kazunori
Published: September 2020

With the aim of evaluating the corrosion behavior of a Pu evaporator made from Zr in the Rokkasho Reprocessing Plant (RRP), the electrochemical characterization of Zr was investigated in Pu nitrate solutions. Before and after experiments, spectra of Pu nitrate solutions were recorded, and Pu4+ was oxidized to PuO2
2+ by heating these solutions. The ratio of PuO2
2+ in 3 mol dm−3 HNO3 was higher than that in 7 mol dm−3 HNO3 in our experiments. The maximum open circuit potential of Zr in the Pu nitrate solution was approximately 1 V in the Pu nitrate solution containing 7 mol dm−3 HNO3. However, there were no significant changes at high PuO2
2+ concentrations, and this value remained below the passivity breakdown potential under our experimental conditions. In this study, Zr showed high corrosion resistance, even though many PuO2
2+ ions existed in the Pu nitrate solution containing 7 mol dm−3 HNO3.
Die Massenstabilität des Elektrons
Authors: Bopp Fritz
Published: September 2020

Abstrakt

Es werden Mindestbedingungen entwickelt, denen eine von der Maxwell sehen Theorie abweichende Elektrodynamik genügen muß, um mit grundlegenden Erfahrungen verträglich zu sein. Insbesondere wird die Bedingung der Massenstabilität untersucht, die in einer Reihe bisher diskutierter Ansätze für die Feldtheorie (von Dirac , Stueckelbergu. d. Verf.) nicht gewahrt ist. Diese Bedingung besagt, daß die mit dem Felde variierende Masse des Elektrons nach Wechselwirkung mit äußeren Kräften wieder ihrem Gleichgewichtswert zustreben soll. Zum Schluß werden verschiedene in der Literatur diskutierten Ansätze zur Abänderung der Maxwell sehen Gleichungen im Lichte der formulierten Bedingungen verglichen und um einen ergänzt, der sämtlichen Bedingungen genügt.
Über einige Konstanten von Zinntetramethyl und Zinntetraäthyl
Authors: Korsching Horst
Published: September 2020

Es wird die Darstellung von reinem Zinntetramethyl und Zinntetraäthyl beschrieben. An den reinen Substanzen werden einige charakteristische Konstanten gemessen
Selektiv-Filterung des Elektrenkephalogramms
Authors: Prast Johannes , Franek Bruno
Published: September 2020

Abstrakt

Gegenüber den bisherigen Versuchen einer nachträglichen Frequenzanalyse des aufgenommenen EEG wird in dem beschriebenen Verfahren durch selektive, elektrische Filterung jede Einzelfrequenz für sich isoliert und dann aufgezeichnet. Auf diese Weise konnten mehrere, verschiedene Frequenzen ihren quantitativen wie qualitativen Eigenschaften nach unterschieden, und frequenzisolierte, zeitlich und örtlich in Zusammenhang mit dem Reiz stehende Hirnaktionsschwingungen betrachtet werden. Mit der Erschließung höher frequenter Bereiche für die analytische Forschung werden die Feinheiten des EEG erst der Möglichkeit einer Deutung zugeführt.
Über die Anwendung der Neutronendiffusion durch Grenzflächen zur Messung von Wirkungsquerschnitten
Authors: Raether Heinz
Published: September 2020

Abstrakt

Durchsetzt ein Strom thermischer Neutronen die Trennfläehe zweier Medien, so gilt die Stetigkeit der Normalkomponpnten der Stromdichte

D1 gradx n1= D2 gradx n2

(D Diffusionskoeffizient, n Neutroncndifhte, x Richtung senkrecht zur Trennfläehe). Setzt man D = λv/3 (λ. Streuweglänge und v Geschwindigkeit der Neutronen), so kann nach einem Vorschlag von Heisenberg aus dem Verhältnis der Dichtegradicnten das Verhältnis d)?r Weglängen ermittelt werden. - In der vorliegenden Arbeit wird auf diese Weise das Verhältnis der Streuweglängen thermischer Neutronen in schwerem und leichtem Wasser gemessen. Legt man für den Wasserstoff Wirkungsquerschnitt σH = 45 • 10-24 cm2 zugrunde, so erhält man aus einer größeren Zahl von Messungen des Gradientenverhältnisses bei verschiedenen Versuchsbedingungen einen Deuterium-Wirkungsquersehnitt von 5,6 ± 0,3 • 10-24 cm2. Dieser Wert stimmt mit öd= 5,7 ± 0,14 • 10-24 cm2 überein, wolcher aus Messungen des Absorptionskoeffizienten von D20-Schichten erhalten wurde. Einige für die Diffusion durch Grenzschichten abgeleitete Beziehungen werden bestätigt.