bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Demonstration of Crosstalk Reduction to Achieve Turbulence-Resilient Multiple-OAM-Beam Free-Space Optical Communications using Pilot Tones to Mix Beams at the Receiver
Authors: Zhang Runzhou , Hu Nanzhe , Zou Kaiheng , Zhou Huibin , Su Xinzhou , Zhao Zhe , Song Haoqian , Song Hao , Almaiman Ahmed , Pang Kai , Liu Cong , Lynn Brittany , Tur Moshe , Willner Alan E.
Published: September 2020

We experimentally demonstrate a 4-Gbit/s turbulence-resilient two-OAM-mode-multiplexed FSO link using pilot tones at different wavelengths to mix beams at the receiver. Inter-modal crosstalk is measured to be resilient to turbulence for different OAM mode selections.
Spectrally and temporally resolved mid-infrared imaging by Adiabatic Sum Frequency upconversion
Authors: Mrejen Michael , Erlich Yoni , Levanon Assaf , Suchowski Haim
Published: September 2020

We introduce a broadband mid-infrared upconversion imaging scheme based on adiabatic frequency conversion. Contrary to state-of-the-art upconversion imaging limited by phase matching, our method does not require serial acquisitions to cover a large spectrum.
6-octave UV to MIR frequency comb driven by a <10 fs Er:fiber laser
Authors: Lesko Daniel , Timmers Henry , Xing Sida , Kowligy Abijith , Lind Alexander , Zawilski Kevin , Schunemann Peter , Diddams Scott
Published: September 2020

We present a 6-octave frequency comb (0.35-22.4 micron) driven by a 9.4 fs, 0.56 MW peak power Er:fiber laser at 100 MHz. Through intrapulse difference frequency generation we produce 5 mW of broadband MIR radiation.
Optical interrogation of Neurovascular Coupling
Authors: Pashaie Ramin
Published: September 2020

Brain function is the result of the interaction between neural and vascular networks. We have used optical and electrophysiology techniques to interrogate the coupling between these two networks. Here we report our latest results.
Near-infrared Femtosecond Time Lens Diode Laser with kW Peak Powers for Two-Photon Microscopy
Authors: Simmons Y. Lange , Underwood Kenneth J. , Heffernan Brendan M. , Supekar Omkar D. , Gibson Emily A. , Gopinath Juliet T.
Published: September 2020

A diode-based time lens laser with nonlinear pulse compression at 976 nm produces 700 fs pulses with 17 kW peak power. Two-photon imaging of a mouse brain slice is demonstrated with the laser.
All-PM dual-comb fiber ring laser using CNT-SA
Authors: Uyama Kota , Shirahata Takuma , Jin Lei , Set Sze Yun , Yamashita Shinji
Published: September 2020

We propose a new truly all-PM dual-comb fiber laser using CNT-SA. We demonstrated stable polarization-multiplexed mode-locked operation by compensating the PDL. The repetition frequency difference between the two polarization-multiplexed outputs was 413 Hz.
Dispersion-managed Tm-doped fiber laser mode-locked with a black phosphorus saturable absorber
Authors: Zhang Q. , Jin X. , Hu G. , Zhang M. , Zheng Z. , Hasan T.
Published: September 2020

We demonstrate an all-fiber Tm-doped mode-locked fiber laser utilizing a black phosphorus saturable absorber. The oscillator delivers self-starting, 139 fs pulses centered at 1859 nm with 55.6 nm spectral bandwidth.
ARC: Angle-Retaining Chromaticity diagram for color constancy error analysis
Authors: Buzzelli Marco , Bianco Simone , Schettini Raimondo
Published: September 2020

Color constancy algorithms are typically evaluated with a statistical analysis of the recovery angular error and the reproduction angular error between the estimated and ground truth illuminants. Such analysis provides information about only the magnitude of the errors, and not about their chromatic properties. We propose an Angle-Retaining Chromaticity diagram (ARC) for the visual analysis of the estimated illuminants and the corresponding errors. We provide both quantitative and qualitative proof of the superiority of ARC in preserving angular distances compared to other chromaticity diagrams, making it possible to quantify the reproduction and recovery errors in terms of Euclidean distances on a plane. We present two case studies for the application of the ARC diagram in the visualization of the ground truth illuminants of color constancy datasets, and the visual analysis of error distributions of color constancy algorithms.
Gaussian pulse behavior in nonlocal Kerr-like media: application to the generation of compact bright pulses
Authors: Kamgaing Mabou William , Pokam Nguewawe Chancelor , Yemélé David
Published: September 2020

The dynamics of the Gaussian pulse, usually generated by mode-locked lasers, is studied in the nonlinear waveguide supporting the propagation of compact bright (CB) pulse-like signals. By means of the collective variables approach, the state equations describing the evolution of the Gaussian pulse parameters are derived. It appears that the system exhibits an equilibrium state in which the pulse width is independent of the amplitude, as is the case for the CB pulse. In this state, the Gaussian pulse propagates without modification of its parameters. However, in the presence of a small kick, namely, induced by a weak linear dispersion, this pulse evolves towards the CB pulse, highlighting the unstable character of this equilibrium state and, more interestingly, the possibility of generation of the CB pulse by means of this waveguide operating in the anomalous dispersion regime. In addition, the newly formed CB pulse can exhibit the breathing mode with the frequency depending on the strength of the linear dispersion. Similarly, the Gaussian pulse is subjected to compression phenomena when its width is much higher compared to the value at the equilibrium state, followed by the emission of a CB pulse of very small amplitude.
Dentin Sialophosphoprotein Deletion Leads to Femoral Head Cartilage Attenuation and Subchondral Bone Ill-mineralization
Authors: Liu Qilin , Ma Ning , Zhu Qinglin , Duan Xiaoqin , Shi Haibo , Xiang Danwei , Kong Hui , Sun Hongchen
Published: September 2020

Dentin sialophosphoprotein (DSPP), which expresses and synthesizes in odontoblasts of dental pulp, is a critical protein for normal teeth mineralization. Originally, DSPP was identified as a dentin-specific protein. In 2010, DSPP was also found in femoral head cartilage, and it is still unclear what roles DSPP play in femoral head cartilage formation, growth, and maintenance. To reveal biological functions of DSPP in the femoral head cartilage, we examined Dspp null mice compared with wild-type (WT) mice to observe DSPP expression as well as localization in WT mice and to uncover differences of femoral head cartilage, bone morphology, and structure between these two kinds of mice. Expression data demonstrated that DSPP had heterogeneous fragments, expressed in each layer of femoral head cartilage and subchondral bone of WT mice. Dspp null mice exhibited a significant reduction in the thickness of femoral head cartilage, with decreases in the amount of proliferating cartilage cells and increases in apoptotic cells. In addition, the subchondral bone mineralization decreased, and the expressions of vessel markers (vascular endothelial growth factor [VEGF] and CD31), osteoblast markers (Osterix and dentin matrix protein 1 [DMP1]), osteocyte marker (sclerostin [SOST]), and osteoclast marker (tartrate-resistant acid phosphatase [TRAP]) were remarkably altered. These indicate that DSPP deletion can affect the proliferation of cartilage cells in the femoral head cartilage and endochondral ossification in subchondral bone. Our data clearly demonstrate that DSPP plays essential roles in the femoral head cartilage growth and maintenance and subchondral biomineralization.
STRUCTURE FROM MOTION PROCESSING OF ANALOGUE IMAGES CAPTURED BY ROLLEI METRIC CAMERA, DIGITIZED WITH VARIOUS SCANNING RESOLUTION
Authors: Dlesk Adam , Vach Karel , Pavelka Karel
Published: September 2020

SfM processing of archived analogue images gives an opportunity to efficiently create new and valuable 2D and 3D results. The SfM processing of digitized analogue images brings some challenges. How to digitize negatives of photogrammetric images? What scanning resolution is the most beneficial for processing? How to preprocess the digitized images to be able to process them using the SfM method? What accuracy of results is possible to expect? This paper tries to deal with all these questions. For this paper, 7 negatives of former photogrammetric documentation of a vault were chosen. The negatives were captured by Rollei 3003 metric camera in 1999. Two pieces of software were chosen for the SfM processing. A commercial alternative Agisoft PhotoScan and free open-source alternative MicMac. The results of the SfM processing were compared to the results of an original photogrammetric method, which was used for former processing in 1999.
CHAOTISED POLYMERIC HOLLOW FIBRE BUNDLE AS A CROSSFLOW HEAT EXCHANGER IN AIR-WATER APPLICATION
Authors: Kroulíková Tereza , Astrouski Ilya , Raudenský Miroslav
Published: September 2020

Fifteen years ago, polymeric hollow fibre heat exchangers were presented for the first time. Nowadays there are not only the shell-and-tube types as there were at the beginning. In this paper, six chaotised polymeric hollow fibre bundles with a different number of fibres were studied. The bundles presented varied in their fibre diameter, number and shape. These bundles were fixed into the module in such a way that the middle part serves as a cross-flow heat exchanger in an air tunnel. They were tested for air-water application with three different airflow rates. The overall heat transfer coefficients were determined, and the inner and outer heat transfer coefficients were derived. The modules presented achieved a heat transfer rate of up to 1309 W. The overall heat transfer coefficient reached a maximum of 339 Wm−2 K−1.
Reseña / Más allá de la supervivencia. Experiencias de economía social y solidaria en América Latina
Authors: Casse Nahuel Eduardo
Published: September 2020

El libro que se reseña es una obra colectiva organizada por Ruth Muñóz y Erika Loritz que se encarga de profundizar los estudios acerca de la Economía Social y Solidaria (ESS) e indaga diversas experiencias que se concretan en América Latina. Constituyéndose como una gran producción de investigación, este escrito es un llamado a la reflexión que provoca centrar la mirada en la pluralidad de formas que puede adquirir la economía, apostando a su carácter social y solidario. Además, consiste en un aporte para seguir transitando el intenso y enriquecedor camino de construcción de otra economía y, por tanto, de otra sociedad. Los principios, las instituciones y las prácticas conflictivas y de discusión por el sentido de la ESS son el hilo conductor de la obra que atraviesa el análisis de agentes, actores y sujetos que ponen en juego todas sus capacidades y sueños para seguir demostrando que una economía alternativa es posible y necesaria como un medio para el logro de la reproducción ampliada de la vida, más allá de la supervivencia.
SPORES
Authors: Wang Yisu Remy , Hutchison Shana , Leang Jonathan , Howe Bill , Suciu Dan
Published: September 2020

Machine learning algorithms are commonly specified in linear algebra (LA). LA expressions can be rewritten into more efficient forms, by taking advantage of input properties such as
sparsity
, as well as program properties such as
common subexpressions
and
fusible operators.
The complex interaction among these properties' impact on the execution cost poses a challenge to optimizing compilers. Existing compilers resort to intricate heuristics that complicate the codebase and add maintenance cost, but fail to search through the large space of equivalent LA expressions to find the cheapest one. We introduce a general optimization technique for LA expressions, by converting the LA expressions into Relational Algebra (RA) expressions, optimizing the latter, then converting the result back to (optimized) LA expressions. The rewrite rules we design in this approach are complete, meaning that any equivalent LA expression is covered in the search space. The challenge is the major size of the search space, and we address this by adopting and extending a technique used in compilers, called
equality saturation.
Our optimizer, SPORES, uses rule sampling to quickly cover vast portions of the search space; it then uses a constraint solver to extract the optimal plan from the covered space, or alternatively uses a greedy algorithm to shorten compile time. We integrate SPORES into SystemML and validate it empirically across a spectrum of machine learning tasks; SPORES can derive all existing hand-coded optimizations in SystemML, and perform new optimizations that lead to up to 10X speedup.
Relational data synthesis using generative adversarial networks
Authors: Fan Ju , Chen Junyou , Liu Tongyu , Shen Yuwei , Li Guoliang , Du Xiaoyong
Published: September 2020

The proliferation of big data has brought an urgent demand for privacy-preserving data publishing. Traditional solutions to this demand have limitations on effectively balancing the tradeoff between privacy and utility of the released data. Thus, the database community and machine learning community have recently studied a new problem of relational data synthesis using generative adversarial networks (GAN) and proposed various algorithms. However, these algorithms are not compared under the same framework and thus it is hard for practitioners to understand GAN's benefits and limitations. To bridge the gaps, we conduct so far the most comprehensive experimental study that investigates applying GAN to relational data synthesis. We introduce a unified GAN-based framework and define a space of design solutions for each component in the framework, including neural network architectures and training strategies. We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches. Through extensive experiments, we find that GAN is very promising for relational data synthesis, and provide guidance for selecting appropriate design solutions. We also point out limitations of GAN and identify future research directions.
Leaper
Authors: Yang Lei , Wu Hong , Zhang Tieying , Cheng Xuntao , Li Feifei , Zou Lei , Wang Yujie , Chen Rongyao , Wang Jianying , Huang Gui
Published: September 2020

Frequency-based cache replacement policies that work well on page-based database storage engines are no longer sufficient for the emerging LSM-tree (
Log-Structure Merge-tree
) based storage engines. Due to the append-only and copy-on-write techniques applied to accelerate writes, the state-of-the-art LSM-tree adopts mutable record blocks and issues frequent background operations (i.e., compaction, flush) to reorganize records in possibly every block. As a side-effect, such operations invalidate the corresponding entries in the cache for each involved record, causing sudden drops on the cache hit rates and spikes on access latency. Given the observation that existing methods cannot address this cache invalidation problem, we propose Leaper, a machine learning method to predict hot records in an LSM-tree storage engine and prefetch them into the cache without being disturbed by background operations. We implement Leaper in a state-of-the-art LSM-tree storage engine, X-Engine, as a light-weight plug-in. Evaluation results show that Leaper eliminates about 70% cache invalidations and 99% latency spikes with at most 0.95% overheads as measured in real-world workloads.
Approximate selection with guarantees using proxies
Authors: Kang Daniel , Gan Edward , Bailis Peter , Hashimoto Tatsunori , Zaharia Matei
Published: September 2020

Due to the falling costs of data acquisition and storage, researchers and industry analysts often want to find all instances of rare events in large datasets. For instance, scientists can cheaply capture thousands of hours of video, but are limited by the need to manually inspect long videos to identify relevant objects and events. To reduce this cost, recent work proposes to use cheap proxy models, such as image classifiers, to identify an approximate set of data points satisfying a data selection filter. Unfortunately, this recent work does not provide the statistical accuracy guarantees necessary in scientific and production settings.

In this work, we introduce novel algorithms for approximate selection queries with
statistical accuracy guarantees.
Namely, given a limited number of exact identifications from an oracle, often a human or an expensive machine learning model, our algorithms meet a minimum precision or recall target with high probability. In contrast, existing approaches can catastrophically fail in satisfying these recall and precision targets. We show that our algorithms can improve query result quality by up to 30 x for both the precision and recall targets in both real and synthetic datasets.
Pytheas
Authors: Christodoulakis Christina , Munson Eric B. , Gabel Moshe , Brown Angela Demke , Miller Renée J.
Published: September 2020

CSV is a popular Open Data format widely used in a variety of domains for its simplicity and effectiveness in storing and disseminating data. Unfortunately, data published in this format often does not conform to strict specifications, making automated data extraction from CSV files a painful task. While table discovery from HTML pages or spreadsheets has been studied extensively, extracting tables from CSV files still poses a considerable challenge due to their loosely defined format and limited embedded metadata.

In this work we lay out the challenges of discovering tables in CSV files, and propose Pytheas: a principled method for automatically classifying lines in a CSV file and discovering tables within it based on the intuition that tables maintain a coherency of values in each column. We evaluate our methods over two manually annotated data sets: 2000 CSV files sampled from four Canadian Open Data portals, and 2500 additional files sampled from Canadian, US, UK and Australian portals. Our comparison to state-of-the-art approaches shows that Pytheas is able to successfully discover tables with precision and recall of over 95.9% and 95.7% respectively, while current approaches achieve around 89.6% precision and 81.3% recall. Furthermore, Pytheas's accuracy for correctly classifying all lines per CSV file is 95.6%, versus a maximum of 86.9% for compared approaches. Pytheas generalizes well to new data, with a table discovery F-measure above 95% even when trained on Canadian data and applied to data from different countries. Finally, we introduce a confidence measure for table discovery and demonstrate its value for accurately identifying potential errors.
Hitting set enumeration with partial information for unique column combination discovery
Authors: Birnick Johann , Bläsius Thomas , Friedrich Tobias , Naumann Felix , Papenbrock Thorsten , Schirneck Martin
Published: September 2020

Unique column combinations (UCCs) are a fundamental concept in relational databases. They identify entities in the data and support various data management activities. Still, UCCs are usually not explicitly defined and need to be discovered. State-of-the-art data profiling algorithms are able to efficiently discover UCCs in moderately sized datasets, but they tend to fail on large and, in particular, on wide datasets due to run time and memory limitations.

In this paper, we introduce
HPIValid
, a novel UCC discovery algorithm that implements a faster and more resource-saving search strategy.
HPIValid
models the metadata discovery as a hitting set enumeration problem in hypergraphs. In this way, it combines efficient discovery techniques from data profiling research with the most recent theoretical insights into enumeration algorithms. Our evaluation shows that
HPIValid
is not only orders of magnitude faster than related work, it also has a much smaller memory footprint.
Magic mirror in my hand, which is the best in the land?
Authors: Kossmann Jan , Halfpap Stefan , Jankrift Marcel , Schlosser Rainer
Published: September 2020

Indexes are essential for the efficient processing of database workloads. Proposed solutions for the relevant and challenging index selection problem range from metadata-based simple heuristics, over sophisticated multi-step algorithms, to approaches that yield optimal results. The main challenges are (i) to accurately determine the effect of an index on the workload cost while considering the interaction of indexes and (ii) a large number of possible combinations resulting from workloads containing many queries and massive schemata with possibly thousands of attributes.

In this work, we describe and analyze eight index selection algorithms that are based on different concepts and compare them along different dimensions, such as solution quality, runtime, multi-column support, solution granularity, and complexity. In particular, we analyze the solutions of the algorithms for the challenging analytical Join Order, TPC-H, and TPC-DS benchmarks. Afterward, we assess strengths and weaknesses, infer insights for index selection in general and each approach individually, before we give recommendations on when to use which approach.
Sieve
Authors: Pappachan Primal , Yus Roberto , Mehrotra Sharad , Freytag Johann-Christoph
Published: September 2020

Current approaches for enforcing Fine Grained Access Control (FGAC) in DBMS do not scale to scenarios when the number of access control policies are in the order of thousands. This paper identifies such a use case in the context of emerging smart spaces wherein systems may be required by legislation, such as Europe's GDPR and California's CCPA, to empower users to specify who may have access to their data and for what purposes. We present Sieve, a layered approach of implementing FGAC in existing DBMSs, that exploits a variety of their features (e.g., UDFs, index usage hints, query explain) to scale to a large number of policies. Given a query, Sieve exploits its context to filter the policies that need to be checked. It also generates
guarded expressions
that save on evaluation cost by grouping policies and exploit database indices to cut on read cost. Our experimental results demonstrate that existing DBMSs can utilize Sieve to significantly reduce query-time policy evaluation cost. Using Sieve DBMSs can support real-time access control in applications such as emerging smart environments.
Maximizing the reduction ability for near-maximum independent set computation
Authors: Piao Chengzhi , Zheng Weiguo , Rong Yu , Cheng Hong
Published: September 2020

Finding the maximum independent set is a fundamental NP-hard problem in graph theory. Recent studies have paid much attention to designing efficient algorithms that find a maximal independent set of good quality (the more vertices the better). Kernelization is a widely used technique that applies rich reduction rules to determine the vertices that definitely belong to the maximum independent set. When no reduction rules can be applied anymore, greedy strategies including vertex addition or vertex deletion are employed to break the tie. It remains an open problem that how to apply these reduction rules and determine the greedy strategy to optimize the overall performance including both solution quality and time efficiency. Thus we propose a scheduling framework that dynamically determines the reduction rules and greedy strategies rather than applying them in a fixed order. As an important reduction rule, degree-two reduction exhibits powerful pruning ability but suffers from high time complexity
O
(
nm
), where
n
and
m
denote the number of vertices and edges respectively. We propose a novel data structure called representative graph, based on which the worst-case time complexity of degree-two reduction is reduced to
O
(
m
log
n
). Moreover, we enrich the naive vertex addition strategy by considering the graph topology and develop efficient methods (active vertex index and lazy update mechanism) to improve the time efficiency. Extensive experiments are conducted on both large real networks and various types of synthetic graphs to confirm the effectiveness, efficiency and robustness of our algorithms.
Detecting and preventing confused labels in crowdsourced data
Authors: Krivosheev Evgeny , Bykau Siarhei , Casati Fabio , Prabhakar Sunil
Published: September 2020

Crowdsourcing is a challenging activity for many reasons, from task design to workers' training, identification of low-quality annotators, and many more. A particularly subtle form of error is due to
confusion
of observations, that is, crowd workers (including diligent ones) that confuse items of a class
i
with items of a class
j
, either because they are similar or because the task description has failed to explain the differences.

In this paper we show that confusion of observations can be a frequent occurrence in many tasks, and that such confusions cause a significant loss in accuracy. As a consequence, confusion detection is of primary importance for crowdsourced data labeling and classification. To address this problem we introduce an algorithm for confusion detection that leverages an inference procedure based on Markov Chain Monte Carlo (MCMC) sampling. We evaluate the algorithm via both synthetic datasets and crowdsourcing experiments and show that it has high accuracy in confusion detection (up to 99%). We experimentally show that quality is significantly improved without sacrificing efficiency. Finally, we show that detecting confusion is important as it can alert task designers early in the crowdsourcing process and lead designers to modify the task or add specific training and information to reduce the occurrence of workers' confusion. We show that even simple modifications, such as alerting workers of the risk of confusion, can improve performance significantly.
Ordering heuristics for
k
-clique listing
Authors: Li Rong-Hua , Gao Sen , Qin Lu , Wang Guoren , Yang Weihua , Yu Jeffrey Xu
Published: September 2020

Listing all
k
-cliques in a graph is a fundamental graph mining problem that finds many important applications in community detection and social network analysis. Unfortunately, the problem of
k
-clique listing is often deemed infeasible for a large
k
, as the number of
k
-cliques in a graph is exponential in the size
k.
The state-of-the-art solutions for the problem are based on the ordering heuristics on nodes which can efficiently list all
k
-cliques in large real-world graphs for a small
k
(e.g.,
k
≤ 10). Even though a variety of heuristic algorithms have been proposed, there still lacks a thorough comparison to cover all the state-of-the-art algorithms and evaluate their performance using diverse real-world graphs. This makes it difficult for a practitioner to select which algorithm should be used for a specific application. Furthermore, existing ordering based algorithms are far from optimal which might explore unpromising search paths in the
k
-clique listing procedure. To address these issues, we present a comprehensive comparison of all the state-of-the-art
k
-clique listing and counting algorithms. We also propose a new color ordering heuristics based on greedy graph coloring techniques which is able to significantly prune the unpromising search paths. We compare the performance of 14 various algorithms using 17 large real-world graphs with up to 3 million nodes and 100 million edges. The experimental results reveal the characteristics of different algorithms, based on which we provide useful guidance for selecting appropriate techniques for different applications.
Do the best cloud configurations grow on trees?
Authors: Bilal Muhammad , Serafini Marco , Canini Marco , Rodrigues Rodrigo
Published: September 2020

Cloud configuration optimization is the procedure to determine the number and the type of instances to use when deploying an application in cloud environments, given a cost or performance objective. In the absence of a performance model for the distributed application, black-box optimization can be used to perform automatic cloud configuration. Numerous black-box optimization algorithms have been developed; however, their comparative evaluation has so far been limited to the hyper-parameter optimization setting, which differs significantly from the cloud configuration problem. In this paper, we evaluate 8 commonly used black-box optimization algorithms to determine their applicability for the cloud configuration problem. Our evaluation, using 23 different workloads, shows that in several cases Bayesian optimization with Gradient boosted regression trees performs better than methods chosen by prior work.
Practical client-side replication
Authors: van der Linde Albert , Leitão João , Preguiça Nuno
Published: September 2020

Client-side replication and direct client-to-client synchronization can be used to create highly available, low-latency interactive applications. Causal consistency, the strongest available consistency model under network partitions, is an attractive consistency model for these applications.

This paper focuses on how client misbehaviour impacts causal consistency. We analyze the possible attacks to causal consistency and derive secure consistency models that preclude different types of misbehaviour. We propose a set of techniques for implementing such secure consistency models, which exhibit different trade-offs between the application guarantees, and the latency and communication overhead.

Our evaluation shows that secure consistency models impose low overhead when compared with their insecure counterparts, while providing low user-to-user latency and server load compared with traditional client-server architectures. Secure consistency models can be used to enrich server-based architectures with fast and secure peer-to-peer interactions.
Approximate partition selection for big-data workloads using summary statistics
Authors: Rong Kexin , Lu Yao , Bailis Peter , Kandula Srikanth , Levis Philip
Published: September 2020

Many big-data clusters store data in large partitions that support access at a coarse, partition-level granularity. As a result, approximate query processing via row-level sampling is inefficient, often requiring reads of many partitions. In this work, we seek to answer queries quickly and approximately by reading a subset of the data partitions and combining partial answers in a weighted manner without modifying the data layout. We illustrate how to efficiently perform this query processing using a set of pre-computed summary statistics, which inform the choice of partitions and weights. We develop novel means of using the statistics to assess the similarity and importance of partitions. Our experiments on several datasets and data layouts demonstrate that to achieve the same relative error compared to uniform partition sampling, our techniques offer from 2.7x to 70x reduction in the number of partitions read, and the statistics stored per partition require fewer than 100KB.
Manycore clique enumeration with fast set intersections
Authors: Blanuša Jovan , Stoica Radu , Ienne Paolo , Atasu Kubilay
Published: September 2020

Listing all maximal cliques of a given graph has important applications in the analysis of social and biological networks. Parallelisation of maximal clique enumeration (MCE) algorithms on modern manycore processors is challenging due to the task-level parallelism that unfolds dynamically. Moreover, the execution time of such algorithms is known to be dominated by intersections between dynamically-created vertex sets. In this paper, we prove that the use of hash-join-based set-intersection algorithms within MCE leads to Pareto-optimal implementations in terms of runtime and memory space compared to those based on merge joins. Building on this theoretical result, we develop a scalable parallel implementation of MCE that exploits both data parallelism, by using SIMD-accelerated hash-join-based set intersections, and task parallelism, by using a shared-memory parallel processing framework that supports dynamic load balancing. Overall, our implementation is an order of magnitude faster than a state-of-the-art manycore MCE algorithm. We also show that a careful scheduling of the execution of the tasks leads to a two orders of magnitude reduction of the peak dynamic memory usage. In practice, we can execute MCE on graphs with tens of millions of vertices and up to two billion edges in just a few minutes on a single CPU.
SAQE
Authors: Bater Johes , Park Yongjoo , He Xi , Wang Xiao , Rogers Jennie
Published: September 2020

A private data federation enables clients to query the union of data from multiple data providers without revealing any extra private information to the client or any other data providers. Unfortunately, this strong end-to-end privacy guarantee requires cryptographic protocols that incur a significant performance overhead as high as 1,000 x compared to executing the same query in the clear. As a result, private data federations are impractical for common database workloads. This gap reveals the following key challenge in a private data federation: offering significantly fast and accurate query answers without compromising strong end-to-end privacy.

To address this challenge, we propose SAQE, the Secure Approximate Query Evaluator, a private data federation system that scales to very large datasets by combining three techniques --- differential privacy, secure computation, and approximate query processing --- in a novel and principled way. First, SAQE adds novel secure sampling algorithms into the federation's query processing pipeline to speed up query workloads and to minimize the noise the system must inject into the query results to protect the privacy of the data. Second, we introduce a query planner that jointly optimizes the noise introduced by differential privacy with the sampling rates and resulting error bounds owing to approximate query processing.

Our research shows that these three techniques are synergistic: sampling within certain accuracy bounds improves both query privacy and performance, meaning that SAQE executes over less data than existing techniques without sacrificing efficiency, privacy, or accuracy. Using our optimizer, we leverage this counter-intuitive result to identify an inflection point that maximizes all three criteria prior query evaluation. Experimentally, we show that this result enables SAQE to trade-off among these three criteria to scale its query processing to very large datasets with accuracy bounds dependent only on sample size, and not the raw data size.
Sentinel
Authors: Glasbergen Brad , Abebe Michael , Daudjee Khuzaima , Levi Amit
Published: September 2020

Systems continue to grow in complexity in response to the need to support vast quantities of data and a wide variety of workloads. Small changes in workloads and system configuration can result in significantly different system behaviour and performance characteristics. As a result, system administrators and developers spend many hours diagnosing and debugging performance problems in data systems and the applications that use them. In this paper, we present Sentinel, an analysis tool that assists these users by constructing fine-grained models of system behaviour and comparing these models to pinpoint differences in system behaviour for different workloads and system configurations. Importantly, Sentinel's insights are derived from built-in debug logging libraries without necessitating that their log messages be written to disk, thereby generalizing to all systems that use debug logging without incurring its overheads. Our experiments demonstrate Sentinel's superiority in analyzing the execution behaviour and performance characteristics of database systems, client applications, and web servers compared to prior approaches.
ATHENA++
Authors: Sen Jaydeep , Lei Chuan , Quamar Abdul , Özcan Fatma , Efthymiou Vasilis , Dalmia Ayushi , Stager Greg , Mittal Ashish , Saha Diptikalyan , Sankaranarayanan Karthik
Published: September 2020

Natural Language Interfaces to Databases (NLIDB) systems eliminate the requirement for an end user to use complex query languages like SQL, by translating the input natural language (NL) queries to SQL automatically. Although a significant volume of research has focused on this space, most state-of-the-art systems can at best handle simple select-project-join queries. There has been little to no research on extending the capabilities of NLIDB systems to handle complex business intelligence (BI) queries that often involve nesting as well as aggregation. In this paper, we present Athena++, an end-to-end system that can answer such complex queries in natural language by translating them into nested SQL queries. In particular, Athena++ combines linguistic patterns from NL queries with deep domain reasoning using ontologies to enable nested query detection and generation. We also introduce a new benchmark data set (
FIBEN
), which consists of 300 NL queries, corresponding to 237 distinct complex SQL queries on a database with 152 tables, conforming to an ontology derived from standard financial ontologies (FIBO and FRO). We conducted extensive experiments comparing Athena++ with two state-of-the-art NLIDB systems, using both
FIBEN
and the prominent
Spider
benchmark. Athena++ consistently outperforms both systems across all benchmark data sets with a wide variety of complex queries, achieving 88.33% accuracy on
FIBEN
benchmark, and 78.89% accuracy on
Spider
benchmark, beating the best reported accuracy results on the dev set by 8%.
COUNTATA
Authors: Moskovitch Yuval , Jagadish H. V.
Published: September 2020

Information regarding the counts of attributes combination is central to the profiling of a data set. It may reveal bias; it can help determine fitness for use. While counts of individual attribute values may be stored in some data set profiles, there are too many combinations of attributes for it to be practical to store counts for each combination. To this end, we present the notion of storing a "label" of limited size that can be used to obtain good estimates for these counts. A label contains information regarding the count of selected patterns-attributes values combinations-in the data. We define an estimation function, that uses this label to estimate the count of every pattern. Intuitively, there is a trade-off between the label size and its estimation error. We propose a demonstration of Countata, a system that allows the user to examine this trade-off as well as the label's count information. We will demonstrate the usefulness of Countata using real-life data, and illustrate the effectiveness of our estimation paradigm.
SQUARES
Authors: Orvalho Pedro , Terra-Neves Miguel , Ventura Miguel , Martins Ruben , Manquinho Vasco
Published: September 2020

Nowadays, many data analysts are domain experts, but they lack programming skills. As a result, many of them can provide examples of data transformations but are unable to produce the desired query. Hence, there is an increasing need for systems capable of solving the problem of Query Reverse Engineering (QRE). Given a database and output table, these systems have to find the query that generated this table. We present SQUARES, a program synthesis tool based on input-output examples that can help data analysts to extract and transform data by synthesizing SQL queries, and table manipulation programs using the R language.
ExplainED
Authors: Deutch Daniel , Gilad Amir , Milo Tova , Somech Amit
Published: September 2020

Exploratory Data Analysis (EDA) is an essential yet highly demanding task. To get a head start before exploring a new dataset, data scientists often prefer to view existing
EDA notebooks
- illustrative exploratory sessions that were created by fellow data scientists who examined the same dataset and shared their notebooks via online platforms. Unfortunately, creating an illustrative, well-documented notebook is cumbersome and time-consuming, therefore users sometimes share their notebook without explaining their exploratory steps and their results. Such notebooks are difficult to follow and to understand.

To address this, we present ExplainED, a system that automatically attaches explanations to views in EDA notebooks. ExplainED analyzes each view in order to detect what elements thereof are particularly interesting, and produces a corresponding textual explanation. The explanations are generated by first evaluating the interestingness of the given view using several measures capturing different interestingness facets, then computing the Shapely values of the elements in the view, w.r.t. the interestingness measure yielding the highest score. These Shapely values are then used to guide the generation of the textual explanation.

We demonstrate the usefulness of the explanations generated by ExplainED on real-life, undocumented EDA notebooks.
Tabula in action
Authors: Yu Jia , Chowdhury Kanchan , Sarwat Mohamed
Published: September 2020

In this paper, we demonstrate Tabula, a middleware that sits between the data system and the geospatial visualization dashboard to increase user interactivity. The proposed system adopts a sampling cube approach that stores prematerialized spatial samples and allows data scientists to define their own accuracy loss function such that the produced samples can be used for various user-defined visualization tasks. The system ensures that the difference between the sample fed into the dashboard and the raw query answer never exceeds the user-specified loss threshold. For demonstration purposes, we connect Apache Zeppelin, a visualization dashboard, to the system and show how Tabula accelerates interactive visualizations on NYC Taxi Trip data, Yelp review data and San Diego Smart Streetlights data.
Obi-Wan
Authors: Buron Maxime , Goasdoué François , Manolescu Ioana , Mugnier Marie-Laure
Published: September 2020

We consider the problem of integrating heterogeneous data (relational, JSON, key-values, graphs etc.) and querying it efficiently. Traditional data integration systems fall into two classes:
data warehousing
, where all data source content is materialized in a single repository, and
mediation
, where data remains in their original stores and all data can be queried through a
mediator.

We propose to demonstrate Obi-Wan, a novel mediator following the Ontology-Based Data access (OBDA) paradigm. Obi-Wan integrates data sources of many data models under an interface based on RDF graphs and ontologies (classes, properties, and relations between them). The novelty of Obi-Wan is to combine maximum integration power (GLAV mappings, see below) with the highest query answering power supported by an RDF mediator: RDF queries not only over the data but also over the integration ontologies. This makes it more flexible and powerful than comparable systems.
GraphAn
Authors: Boniol Paul , Palpanas Themis , Meftah Mohammed , Remy Emmanuel
Published: September 2020

Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the state-of-the-art approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and inefficient/ineffective in situations with recurrent anomalies of the same type. We recently proposed Series2Graph, a novel method based on a graph representation of a low-dimensionality embedding of subsequences, that detects anomalous subsequences. The experimental results, on the largest set of synthetic and real datasets used to date, demonstrate that the proposed approach correctly identifies single and recurrent anomalies of various types without any prior knowledge of the characteristics of these anomalies, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster. In this demonstration, we present GraphAn, a system based on Series2Graph, show-case the challenges of the problem, and demonstrate the advantages of the proposed system.
Scrutinizer
Authors: Karagiannis Georgios , Saeed Mohammed , Papotti Paolo , Trummer Immanuel
Published: September 2020

We demonstrate Scrutinizer, a system that supports human fact checkers in translating text claims into SQL queries on an associated database. Scrutinizer coordinates teams of human fact checkers and reduces their verification time by proposing queries or query fragments over relevant data. Those proposals are based on claim text classifiers, that gradually improve during the verification of multiple claims. 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 that plans effective question sequences to verify specific claims, and prioritizes claims for verification. In this demonstration, we first show how our system can assist users in verifying statistical claims. We then let users come up with new, unseen claims and show how the system effectively learns new queries with little user feedback.
nKV in action
Authors: Vinçon Tobias , Bernhardt A. , Petrov Ilia , Koch Andreas
Published: September 2020

Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-data processing (NDP) designs represent a feasible solution, which although not new, has yet to see widespread use.

In this paper we demonstrate various NDP alternatives in nKV, which is a key/value store utilizing
native computational storage
and
near-data processing.
We showcase the execution of classical operations (
GET, SCAN
) and complex graph-processing algorithms (
Betweenness Centrality
) in-situ, with 1.4x-2.7x better performance due to NDP. nKV runs on real hardware - the
COSMOS+
platform.
Debugging large-scale data science pipelines using dagger
Authors: Rezig El Kindi , Brahmaroutu Ashrita , Tatbul Nesime , Ouzzani Mourad , Tang Nan , Mattson Timothy , Madden Samuel , Stonebraker Michael
Published: September 2020

Data pipelines are the new code. Consequently, data scientists need new tools to support the often time-consuming process of debugging their pipelines. We introduce
Dagger
, an end-to-end system to debug and mitigate data-centric errors in data pipelines, such as a data transformation gone wrong or a classifier underperforming due to noisy training data.
Dagger
supports inter-module debugging, where the pipeline blocks are treated as black boxes, as well as intra-module debugging, where users can debug data objects in Python scripts (e.g., DataFrames). In this demo, we will walk the audience through a rich, real-world business intelligence use case from our industrial collaborators at Intel, to highlight how
Dagger
enables data scientists to productively identify and mitigate data-centric problems at different stages of pipeline development.