bluuebunny/crossref_metadata_2025 huggingface dataset:
ILE655VAL Genotyping Study of HER2 - Positive Breast Cancer of Patients from Padang - Indonesia With High Resolution Melting Technique
Authors: Wulandari Dwi , Azamris Azamris , Nurhayati Isna , Warisman M Ali , Budiarto Bugi Ratno , Desriani Desriani
Published: September 2018

Transtuzumab has proven to be a great improvement in the treatment of HER2 + of breast cancer patients, but it is associated with relevant adverse cardiac events and with significantly elevated cost of treatment.  One of the risk factors for cardiotoxicity due to transtuzumab is the I655V HER2 polymorphism (GTC> ATC mutation) in which the allele mutant (Ile val or val / val) has a higher risk than the wild type (Ile/Ile). The detection of specific alleles is very important for therapeutic decision-making. In this study, our group have developed a HRM with EvaGreen dye method to discriminate specific allele of I655V HER2 (wild type, heterozygote mutant or homozygote mutant) in 66 frozen section samples of HER2+ of breast cancer patients. Our group reported the wild type is the most prevalent allele (77,27%), whereas heterozygous mutation is significantly present in this research (21,21%) and only 1,52% of sample detected as minor allele.  It showed that only one sample detected as a minor allele (val/val) and may have relatively low abundance in the population. This method has been compared with sanger sequencing and giving 100% of validity.
ANÁLISIS EXPERIMENTAL DEL EFECTO DE CAMBIOS ESTRUCTURALES EN INVERNADEROS CON TECHO A DOS VERTIENTES
Authors: BOUZO C. A. , LENSCAK M. P.
Published: September 2018

Fue realizado un análisis experimental de un invernadero a dos vertientes al cual se le realizaron modificaciones dimensionales y de diseño de las ventanas cenitales. Se compararon las mediciones internas del aire en su velocidad (m s -1 ), caudal (m 3 h -1 ), temperatura (oC) y déficit de presión de vapor, DPV (kPa) entre invernaderos denominados aquí ̈Capilla Modificado Tradicional ̈ (CMT) y ̈Capilla Modificado Alternativo ̈ (CMA). El trabajo fue realizado en Goya, Corrientes (29o 02 ́S, 59o 10 ́W) durante los meses de febrero, marzo y noviembre del año 2016. Se observó una mejor condición micro-meteorológica en el invernadero CMA, al presentar menor temperatura interna diurna, mayores velocidades, caudal del aire y renovación horaria. Se discuten los resultados basa- dos en las diferencias dimensionales y de diseño de los invernaderos teniendo en cuenta los resultados de otros trabajos de investigación.
EVALUACIÓN DE Azotobacter vinelandii Y Pseudomonas denitrifi cans EN Dioscorea rotundata EN CONDICIONES DE CAMPO
Authors: SÁNCHEZ LÓPEZ D. B. , PÉREZ PAZOS J. V. , LUNA CASTELLANOS L. L. , GARCÍA PEÑA J. A. , ESPITIA MONTES A. A.
Published: September 2018

Las rizobacterias de vida libre o asociativas promueven el crecimiento vegetal, fi jan nitrógeno y sintetizan fi tohormonas. La combinación de estos mecanismos contribuye a mejorar la efi ciencia en el uso de fertilizantes nitrogenados y aumentar la productividad de los cultivos. El objetivo de esta investigación fue evaluar el efecto de Azotobacter vinelandii DVS9 y Pseudomonas denitrifi cans DVS10 bajo diferentes niveles de fertilización química nitrogenada (50% y 75%) sobre el rendimiento y calidad nutricional de los tubérculos de Dioscorea rotundata. El estudio se realizó en el municipio de Morindó, Córdoba (Colombia), empleando un diseño experimental completamente aleatorizado con un arreglo factorial aumentado 3x2+2, con 3 repeticiones. Los resultados obtenidos indican que la inoculación de la cepa Pseudomonas denitrifi cans DVS10 redujo un 25% la fertilización nitrogenada el cultivo de ñame espino, asimismo, esta bacteria produjo incrementos signifi cativos en los contenidos de proteína y fi bra cruda del tubérculo.
EVOLUCIÓN DE LOS PESOS Y TASA DE CRECIMIENTO DE CORDEROS PROVENIENTES DE MADRES DE BAJA CONDICIÓN CORPORAL
Authors: SOSA J. L. , LEVA P. E. , TOFFOLI G. D. , FERNANDEZ G. B. , BOGGERO C. , GARCIA M. S.
Published: September 2018

El objetivo del ensayo fue evaluar el peso y el crecimiento de corderos Pampinta mantenidos al pie de la madre con una alimentación diferencial (Creep Feeding) con ovejas de condición corporal entre 1 y 1,5. El ensayo se llevó a cabo en dependencias de la Facultad de Ciencias Agrarias y Ciencias Veterinarias de la UNL, se utilizaron 20 ovejas Pampinta y 20 corderos nacidos en el mes de septiembre del 2017. La alimentación de los corderos se realizó bajo lactancia natural no restringida con alimentación diferencial. Durante la noche los corderos separados de sus madres fueron conducidos al patio de comida y recibían una ración de mezcla de soja y maíz espolvoreados con leche en polvo. Los resultados fueron analizados mediante ANOVA para un diseño completamente aleatorizado. Las variables peso al nacer (p=0,0065) y tasa de crecimiento (p=0,039) fueron significativamente diferentes entre sexo. La ganancia de peso diaria y el peso a los 45días no fueron diferentes entre sexo.
VACCINATION IN RHEUMATIC DISEASES: AN ALLY OR AN ENEMY?
Authors: Belov B. S.
Published: September 2018

The importance of comorbid infections in rheumatology has recently increased substantially, particularly due to the introduction of biological agents into clinical practice. One of the ways to solve the above problem is to investigate and actively use different vaccines. The paper deals with the issues concerning the use of vaccines against influenza and pneumococcal infections in rheumatic patients. It discusses the safety and immunogenicity of vaccination in preventing respiratory infections as the most common cause of an adverse outcome in rheumatic diseases. Main areas for future investigations of the considered problem are shown.
PULMONARY ARTERIAL HYPERTENSION IN SYSTEMIC CONNECTIVE TISSUE DISEASES: THE CURRENT STATE OF THE PROBLEM
Authors: Volkov A. V. , Martynyuk T. V.
Published: September 2018

Pulmonary arterial hypertension (PAH) is a complex, multidisciplinary problem of modern medicine. The basis for the disease is a microcirculatory lesion in the vessels, which leads to a decrease in their blood flow, to increased pulmonary vascular resistance and, as an outcome, right ventricular failure, and death. PAH may also be associated with systemic connective tissue diseases (SCTDs), in particular with systemic sclerosis (SS), as one of the manifestations of the disease. As well as idiopathic pulmonary hypertension, PAH-SCTDs is an orphan disease: according to the data of the V.A. Nasonova Research Institute of Rheumatology, the incidence of PAH is 5.2% in SS, 0.3% in systemic lupus erythematosus, and 7.4% in mixed connective tissue disease. Low prevalence is responsible for late diagnosis, which is always associated with a poor therapy response and poor prognosis. The purpose of the lecture is to acquaint rheumatologists with the possibilities of diagnosis and treatment of this rare, but prognostic severe manifestation of SCTDs.
CLINICAL SIGNIFICANCE OF CALPROTECTIN IN RHEUMATIC DISEASES
Authors: Avdeeva A. S.
Published: September 2018

Calprotectin (CP) belongs to the S100 leukocyte protein family, consists of two calcium-binding protein molecules, S100A8 and S100A9 (MRP8/14, calgranulin A/B). MRP8/14 is the main intracellular protein of neutrophil granulocytes and monocytes, which exerts a proinflammatory effect on phagocytes, endothelial cells in vitro and favors the development of the inflammatory process in vivo; it is an important mediator of many regulatory functions, such as chemotaxis; activation of neutrophil degranulation and phagocytosis; inhibition of immunoglobulin synthesis, cell proliferation and differentiation. CP is currently considered as a potential acute-phase marker in many inflammatory and autoimmune diseases. In rheumatoid arthritis (RA), CP maintains chronic inflammation, by activating the endothelium and increasing the migration of neutrophils into the inflamed synovial fluid. A number of studies have demonstrated the role of CP in monitoring RA activity, detecting subclinical inflammation, and predicting disease exacerbations. CP is of great importance in monitoring the efficiency of RA therapy with disease-modifying antirheumatic drugs and biological agents. The value of CP in spondyloarthritis (SpA) is ambiguous: on the one hand, this marker is highly expressed in the synovial tissue of patients with SpA and correlates with the level of acute-phase reactants; on the other hand, its correlation with disease activity indices is very contradictory and requires further clarification. By and large, CP is a promising laboratory marker of great clinical significance.
Cвойства диаграмм нагружения и разгрузки, порождаемых нелинейным определяющим соотношением типа Максвелла для реономных материалов
Authors: Хохлов Андрей Владимирович , Khokhlov Andrew Vladimirovich
Published: September 2018

В статье продолжены исследование и аттестация физически нелинейного определяющего соотношения типа Максвелла для нестареющих упруговязкопластичных материалов с двумя материальными функциями: комплекса моделируемых им реологических эффектов, области применимости, способов идентификации, настройки и дальнейшей модификации. Аналитически изучены общие качественные свойства семейства кривых нагружения и разгрузки с постоянными скоростями, порождаемых этим соотношением с произвольными материальными функциями в условиях одноосного квазистатического нагружения. Исследованы интервалы монотонности и выпуклости кривых и условия существования точек перегиба на участках нагрузки и разгрузки, величины максимальной деформации, скачков скорости деформации и пластической деформации за цикл нагрузки-разгрузки, их зависимость от максимального напряжения, скорости и длительности нагружения и разгрузки и от характеристик материальных функций. Обнаруженные свойства сопоставлены с типичными свойствами экспериментальных диаграмм нагружения и разгрузки классов реономных материалов с целью выявления феноменологических ограничений на материальные функции, арсенала возможностей и индикаторов (не)применимости определяющего соотношения.
Хаотические режимы фрактального нелинейного осциллятора
Authors: Паровик Роман Иванович , Parovik Roman Ivanovich
Published: September 2018

В работе проведено исследование фрактального нелинейного осциллятора с целью идентификации его хаотических колебательных режимов. Мерой хаоса для динамической системы являются максимальные показатели Ляпунова. Они рассматриваются как мера разбегания нескольких фазовых траекторий, построенных при разных начальных условиях. Для определения максимальных показателей Ляпунова используются алгоритмы, которые связаны либо с исследованием временных рядов (алгоритм Бенеттина), либо с непосредственным решением расширенной динамической системы (алгоритм Вольфа). В работе в качестве методики построения максимальных показателей Ляпунова был выбран алгоритм Вольфа с процедурой ортогонализации Грама - Шмидта. Этот алгоритм использует решение расширенной исходной динамической системы совместно с уравнениями в вариациях, а процедура ортогонализации Грама - Шмидта позволяет нивелировать составляющую максимального показателя Ляпунова при вычислении векторов вдоль фазовых траекторий. Далее алгоритм Вольфа был использован для построения спектров показателей Ляпунова в зависимости от значений управляющих параметров исходной динамической системы. В работе было показано, что некоторые спектры показателей Ляпунова содержат наборы положительных значений, что подтверждает наличие хаотического режима, а также это подтверждается фазовыми траекториями. Установлено, что фрактальный нелинейный осциллятор имеет режимы не только колебания, но и вращения. Эти вращения могут быть хаотическими и регулярными.
Rudolf Berliner i jego wkład w rozumienie chrześcijańskiego obrazu
Authors: Suckale Robert
Published: September 2018

Rudolf Berliner i jego wkład w rozumienie chrześcjańskiego obrazu
Co lze „vyčíst z ruky” aneb somatické frazémy v proměnách času
Authors: PACOVSKÁ Jasňa , ŠKODOVÁ Svatava , LÁBUS Václav
Published: September 2018

Příspěvek se zabývá tzv. somatickými frazémy, jež jsou založeny na bázovém lexému ruka. Pro potřeby tohoto příspěvku bylo analyzováno užití jednoho konkrétního spojení, a to v rukou. Jazykový materiál pro excerpci poskytl archiv mluvených pořadů Českého rozhlasu, konkrétně zpravodajské pořady Rozhlasové noviny z roku 1977 a Ozvěny dne z roku 1997; pro porovnání byla využita i data z Českého národního korpusu z týchž let. Cílem je postihnout poměr doslovného a metaforického užití vybrané vazby, kvalitativně analyzovat sémantické profily, v nichž se vyskytuje, a identifikovat případný ideologický podtext.
Warrs Wage Wars – vhled do lexikonu českých hráčů počítačových her využívajících adaptovaná anglická substantiva
Authors: SÁDLO Petr
Published: September 2018

Hráči počítačových her často vyžadují transformaci již existujícího anglického výrazu, jenž se ve hře objevuje, do jejich rodného jazyka. Cílem příspěvku je určit způsob, jakým jsou původní anglická substantiva převáděna do českého gramatického systému. Teoretická část referuje o pozici angličtiny ve světě, pojednává o filozofii gramaticko-lexikální adaptace obecně a rovněž nastiňuje základní principy dané hry. Dále pak popisuje již existující slang českých hráčů počítačových her. V praktické části jsou podstatná jména analyzována z hlediska slovotvorby, sémantické motivace a širšího situačního kontextu. U každého analyzovaného substantiva je uveden typický příklad jeho užití ve hře. Výsledky výzkumu jsou ověřeny v rámci rozboru praktické části příspěvku.
Automating large-scale data quality verification
Authors: Schelter Sebastian , Lange Dustin , Schmidt Philipp , Celikel Meltem , Biessmann Felix , Grafberger Andreas
Published: September 2018

Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream. Therefore, a crucial, but tedious task for everyone involved in data processing is to verify the quality of their data. We present a system for automating the verification of data quality at scale, which meets the requirements of production use cases. Our system provides a declarative API, which combines common quality constraints with user-defined validation code, and thereby enables 'unit tests' for data. We efficiently execute the resulting constraint validation workload by translating it to aggregation queries on Apache Spark. Our platform supports the incremental validation of data quality on growing datasets, and leverages machine learning, e.g., for enhancing constraint suggestions, for estimating the 'predictability' of a column, and for detecting anomalies in historic data quality time series. We discuss our design decisions, describe the resulting system architecture, and present an experimental evaluation on various datasets.
Fusion insight librA
Authors: Cai Le , Chen Jianjun , Chen Jun , Chen Yu , Chiang Kuorong , Dimitrijevic Marko , Ding Yonghua , Dong Yu , Ghazal Ahmad , Hebert Jacques , Jagtiani Kamini , Lin Suzhen , Liu Ye , Ni Demai , Pei Chunfeng , Sun Jason , Wang Yongyan , Zhang Li , Zhang Mingyi , Zhu Cheng
Published: September 2018

Huawei Fusion Insight Libr A (FI-MPPDB) is a petabyte scale enterprise analytics platform developed by the Huawei data-base group. It started as a prototype more than five years ago, and is now being used by many enterprise customers over the globe, including some of the world's largest financial institutions. Our product direction and enhancements have been mainly driven by customer requirements in the fast evolving Chinese market.

This paper describes the architecture of FI-MPPDB and some of its major enhancements. In particular, we focus on top four requirements from our customers related to data analytics on the cloud: system availability, auto tuning, query over heterogeneous data models on the cloud, and the ability to utilize powerful modern hardware for good performance. We present our latest advancements in the above areas including online expansion, auto tuning in query optimizer, SQL on HDFS, and intelligent JIT compiled execution. Finally, we present some experimental results to demonstrate the effectiveness of these technologies.
Data integration and machine learning
Authors: Dong Xin Luna , Rekatsinas Theodoros
Published: September 2018

As data volume and variety have increased, so have the ties between machine learning and data integration become stronger. For machine learning to be effective, one must utilize data from the greatest possible variety of sources; and this is why data integration plays a key role. At the same time machine learning is driving automation in data integration, resulting in overall reduction of integration costs and improved accuracy. This tutorial focuses on three aspects of the synergistic relationship between data integration and machine learning: (1) we survey how state-of-the-art data integration solutions rely on machine learning-based approaches for accurate results and effective human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant data for their analytics exercises, and (3) we discuss open research challenges and opportunities that span across data integration and machine learning.
Graph data models, query languages and programming paradigms
Authors: Deutsch Alin , Papakonstantinou Yannis
Published: September 2018

Numerous databases support semi-structured, schemaless and heterogeneous data, typically in the form of graphs (often restricted to trees and nested data). They also provide corresponding high-level query languages or graph-tailored programming paradigms.

The evolving query languages present multiple variations: Some are superficial syntactic ones, while other ones are genuine differences in modeling, language capabilities and semantics. Incompatibility with SQL presents a learning challenge for graph databases, while table orientation often leads to cumbersome syntactic/semantic structures that are contrary to graph data. Furthermore, the query languages often fall short of full-fledged semistructured and graph query language capabilities, when compared to the yardsticks set by prior academic efforts.

We survey features, the designers' options and differences in the approaches taken by current systems. We cover both declarative query languages, whose semantics is independent of the underlying model of computation, as well as languages with an operational semantics that is more tightly coupled with the model of computation. For the declarative languages over both general graphs and tree-shaped graphs (as motivated by XML and the recent generation of nested formats, such as JSON and Parquet) we compare to an SQL baseline and present SQL reductions and extensions that capture the essentials of such database systems. More precisely, rather than presenting a single SQL extension, we present multiple configuration options whereas multiple possible (and different) semantics are formally captured by the multiple options that the language's semantic configuration options can take. We show how appropriate setting of the configuration options morphs the semantics into the semantics of multiple surveyed languages, hence providing a compact and formal tool to understand the essential semantic differences between different systems.

Finally we compare with prior nested and graph query languages (notably OQL, XQuery, Lorel, StruQL, PigLatin) and we transfer into the modern graph database context lessons from the semistructured query processing research of the 90s and 00s, combining them with insights on current graph databases.
Computational fact checking
Authors: Cazalens Sylvie , Leblay Julien , Lamarre Philippe , Manolescu Ioana , Tannier Xavier
Published: September 2018

Data journalism designates journalistic work inspired by digital data sources. A particularly popular and active area of data journalism is concerned with fact-checking. The term was born in the journalist community and referred the process of verifying and ensuring the accuracy of published media content; since 2012, however, it has increasingly focused on the analysis of politics, economy, science, and news content shared in any form, but first and foremost on the Web (social and otherwise). These trends have been noticed by computer scientists working in the industry and academia. Thus, a very lively area of digital content management research has taken up these problems and works to propose foundations (models), algorithms, and implement them through concrete tools.

Our tutorial: (i) Outlines the current state of affairs in the area of digital (or computational) fact-checking in newsrooms, by journalists, NGO workers, scientists and IT companies; (ii) Shows which areas of digital content management research, in particular those relying on the Web, can be leveraged to help fact-checking, and gives a comprehensive survey of efforts in this area; (iii) Highlights ongoing trends, unsolved problems, and areas where we envision future scientific and practical advances.
A demonstration of the ottertune automatic database management system tuning service
Authors: Zhang Bohan , Van Aken Dana , Wang Justin , Dai Tao , Jiang Shuli , Lao Jacky , Sheng Siyuan , Pavlo Andrew , Gordon Geoffrey J.
Published: September 2018

Database management systems (DBMSs) have a plethora of tunable knobs that control almost everything in the system. The performance of a DBMS is highly dependent on these configuration knobs, however, getting this tuning right is hard. Many organizations resort to hiring experts to configure these knobs, but this is prohibitively expensive. As databases grow in both size and complexity, optimizing a DBMS has surpassed the abilities of even the best human experts. We recently introduced OtterTune, a tuning service that is able to automatically find good settings for a DBMS's configuration knobs. OtterTune leverages data collected from previous tuning efforts to train machine learning models, and recommends new configurations that are as good as or better than ones generated by existing tools or a human expert. In this demonstration, we showcase OtterTune's ability to automatically select a configuration that improves a DBMS's performance.
CoreKG
Authors: Beheshti Amin , Benatallah Boualem , Nouri Reza , Tabebordbar Alireza
Published: September 2018

With Data Science continuing to emerge as a powerful differentiator across industries, organisations are now focused on transforming their data into actionable insights. This task is challenging as in today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of raw data from a variety of sources. Data Lakes introduced as a storage repository to organize this raw data in its native format (supporting from relational to NoSQL DBs) until it is needed. The rationale behind a Data Lake is to store raw data and let the data analyst decide how to cook/curate them later. In this paper, we present the notion of Knowledge Lake, i.e. a contextualized Data Lake. The Knowledge Lake will provide the foundation for big data analytics by automatically curating the raw data in the Data Lake and to prepare them for deriving insights. We present CoreKG-an open source Data and Knowledge Lake service- which offers researchers and developers a single REST API to organize, curate, index and query their data and metadata in the Lake and over time. CoreKG manages multiple database technologies (from Relational to NoSQL) and offers a built-in design for data curation, security and provenance.
Tooling framework for instantiating natural language querying system
Authors: Jammi Manasa , Sen Jaydeep , Mittal Ashish , Verma Sagar , Pahuja Vardaan , Ananthanarayanan Rema , Lohia Pranay , Karanam Hima , Saha Diptikalyan , Sankaranarayanan Karthik
Published: September 2018

Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicability in the medical domain.
X
2
Q
Authors: Lissandrini Matteo , Mottin Davide , Velegrakis Yannis , Palpanas Themis
Published: September 2018

Exploring knowledge graphs can be a daunting task for any user, expert or novice. This is due to the complexity of the schema or because they are unfamiliar with the contents of the data, or even because they do not know precisely what they are looking for. For the same reason there is a significant demand for exploratory methods for this kind of data. We propose X
2
Q, a system that facilitates the exploration of knowledge graphs with a hands-on approach. X
2
Q embodies the flexible multi-exemplar query paradigm, in which easy to express examples serve as the basis for formulating sophisticated, and hard to express queries. Our system helps building examples in an interactive fashion, by showing results of the partial exemplar query as well as suggestions for improving the current examples. Then, the user feedback is incorporated in our scores to filter the irrelevant suggestions upfront. X
2
Q returns answers in real-time on Freebase, one of the largest available knowledge graphs.
Connectionlens
Authors: Chanial Camille , Dziri Rédouane , Galhardas Helena , Leblay Julien , Nguyen Minh-Huong Le , Manolescu Ioana
Published: September 2018

Nowadays, journalism is facilitated by the existence of large amounts of publicly available digital data sources. In particular, journalists can do investigative work, which typically consists on keyword-based searches over many heterogeneous, independently produced and dynamic data sources, to obtain useful, interconnecting and traceable information. We propose to demonstrate C
onnection
L
ens
, a system based on a novel algorithm for keyword search across heterogeneous data sources. Our demonstration scenarios are based on use cases suggested by journalists from the french journal Le Monde, with whom we collaborate.
In-RDBMS hardware acceleration of advanced analytics
Authors: Mahajan Divya , Kim Joon Kyung , Sacks Jacob , Ardalan Adel , Kumar Arun , Esmaeilzadeh Hadi
Published: September 2018

The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently. As such, there is a void of solutions that enables hardware acceleration at the intersection of these disjoint fields. This paper sets out to be the initial step towards a unifying solution for in-
D
atabase
A
cceleration of Advanced
A
nalytics (DAnA). Deploying specialized hardware, such as FPGAs, for in-database analytics currently requires hand-designing the hardware and manually routing the data. Instead, DAnA automatically maps a high-level specification of advanced analytics queries to an FPGA accelerator. The accelerator implementation is generated for a User Defined Function (UDF), expressed as a part of an SQL query using a Python-embedded Domain-Specific Language (DSL). To realize an efficient in-database integration, DAnA accelerators contain a novel hardware structure,
Striders
, that directly interface with the buffer pool of the database.
Striders
extract, cleanse, and process the training data tuples that are consumed by a multi-threaded FPGA engine that executes the analytics algorithm. We integrate DAnA with PostgreSQL to generate hardware accelerators for a range of real-world and synthetic datasets running diverse ML algorithms. Results show that DAnA-enhanced PostgreSQL provides, on average, 8.3× end-to-end speedup for real datasets, with a maximum of 28.2×. Moreover, DAnA-enhanced PostgreSQL is, on average, 4.0× faster than the multi-threaded Apache MADLib running on Greenplum. DAnA provides these benefits while hiding the complexity of hardware design from data scientists and allowing them to express the algorithm in ≈30-60 lines of Python.
Question answering over knowledge graphs
Authors: Zheng Weiguo , Yu Jeffrey Xu , Zou Lei , Cheng Hong
Published: September 2018

The gap between unstructured natural language and structured data makes it challenging to build a system that supports using natural language to query large knowledge graphs. Many existing methods construct a structured query for the input question based on a syntactic parser. Once the input question is parsed incorrectly, a false structured query will be generated, which may result in false or incomplete answers. The problem gets worse especially for complex questions. In this paper, we propose a novel systematic method to understand natural language questions by using a large number of binary templates rather than semantic parsers. As sufficient templates are critical in the procedure, we present a low-cost approach that can build a huge number of templates automatically. To reduce the search space, we carefully devise an index to facilitate the online template decomposition. Moreover, we design effective strategies to perform the two-level disambiguations (i.e., entity-level ambiguity and structure-level ambiguity) by considering the query semantics. Extensive experiments over several benchmarks demonstrate that our proposed approach is effective as it significantly outperforms state-of-the-art methods in terms of both precision and recall.
RHEEM: enabling cross-platform data processing
Authors: Agrawal Divy , Chawla Sanjay , Contreras-Rojas Bertty , Elmagarmid Ahmed , Idris Yasser , Kaoudi Zoi , Kruse Sebastian , Lucas Ji , Mansour Essam , Ouzzani Mourad , Papotti Paolo , Quiané-Ruiz Jorge-Arnulfo , Tang Nan , Thirumuruganathan Saravanan , Troudi Anis
Published: September 2018

Solving business problems increasingly requires going beyond the limits of a single data processing platform (platform for short), such as Hadoop or a DBMS. As a result, organizations typically perform tedious and costly tasks to juggle their code and data across different platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging: finding the most efficient platform for a given task requires quite good expertise for all the available platforms. We present R
heem
, a general-purpose cross-platform data processing system that decouples applications from the underlying platforms. It not only determines the best platform to run an incoming task, but also splits the task into subtasks and assigns each subtask to a specific platform to minimize the overall cost (e.g., runtime or monetary cost). It features (i) an interface to easily compose data analytic tasks; (ii) a novel cost-based optimizer able to find the most efficient platform in almost all cases; and (iii) an executor to efficiently orchestrate tasks over different platforms. As a result, it allows users to focus on the business logic of their applications rather than on the mechanics of how to compose and execute them. Using different real-world applications with R
heem
, we demonstrate how cross-platform data processing can accelerate performance by more than one order of magnitude compared to single-platform data processing.
Distributed representations of tuples for entity resolution
Authors: Ebraheem Muhammad , Thirumuruganathan Saravanan , Joty Shafiq , Ouzzani Mourad , Tang Nan
Published: September 2018

Despite the efforts in 70+ years in all aspects of entity resolution (ER), there is still a high demand for democratizing ER - by reducing the heavy human involvement in labeling data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representations of words (
a.k.a
. word embeddings), we present a novel ER system, called D
eep
ER, that achieves good accuracy, high efficiency, as well as ease-of-use (
i.e
., much less human efforts). We use sophisticated composition methods, namely uni- and bi-directional recurrent neural networks (RNNs) with long short term memory (LSTM) hidden units, to convert each tuple to a distributed representation (
i.e
., a vector), which can in turn be used to effectively capture similarities between tuples. We consider both the case where pre-trained word embeddings are available as well the case where they are not; we present ways to learn and tune the distributed representations that are customized for a specific ER task under different scenarios. We propose a locality sensitive hashing (LSH) based blocking approach that takes all attributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that consider only a few attributes. We evaluate our algorithms on multiple datasets (including benchmarks, biomedical data, as well as multi-lingual data) and the extensive experimental results show that D
eep
ER outperforms existing solutions.
Homerun
Authors: Almutairi Faisal M. , Yang Fan , Song Hyun Ah , Faloutsos Christos , Sidiropoulos Nicholas , Zadorozhny Vladimir
Published: September 2018

Recovering a time sequence of events from multiple aggregated and possibly overlapping reports is a major challenge in historical data fusion. The goal is to reconstruct a higher resolution event sequence from a mixture of lower resolution samples as accurately as possible. For example, we may aim to disaggregate overlapping monthly counts of people infected with measles into weekly counts. In this paper, we propose a novel data disaggregation method, called H
ome
R
un
, that exploits an alternative representation of the sequence and finds the spectrum of the target sequence. More specifically, we formulate the problem as so-called
basis pursuit
using the Discrete Cosine Transform (DCT) as a sparsifying dictionary
and
impose non-negativity and smoothness constraints. H
ome
R
un
utilizes the energy compaction feature of the DCT by finding the sparsest spectral representation of the target sequence that contains the largest (most important) coefficients. We leverage the
Alternating Direction Method of Multipliers
to solve the resulting optimization problem with scalable and memory efficient steps. Experiments using real epidemiological data show that our method considerably outperforms the state-of-the-art techniques, especially when the DCT of the sequence has a high degree of energy compaction.
Differentially private hierarchical count-of-counts histograms
Authors: Kuo Yu-Hsuan , Chiu Cho-Chun , Kifer Daniel , Hay Michael , Machanavajjhala Ashwin
Published: September 2018

We consider the problem of privately releasing a class of queries that we call
hierarchical count-of-counts histograms
. Count-of-counts histograms partition the rows of an input table into groups (e.g., group of people in the same household), and for every integer
j
report the number of groups of size
j
. Hierarchical count-of-counts queries report count-of-counts histograms at different granularities as per hierarchy defined on an attribute in the input data (e.g., geographical location of a household at the national, state and county levels). In this paper, we introduce this problem, along with appropriate error metrics and propose a differentially private solution that generates count-of-counts histograms that are consistent across all levels of the hierarchy.
Efficient document analytics on compressed data
Authors: Zhang Feng , Zhai Jidong , Shen Xipeng , Mutlu Onur , Chen Wenguang
Published: September 2018

Today's rapidly growing document volumes pose pressing challenges to modern document analytics, in both space usage and processing time. In this work, we propose the concept of
compression-based direct processing
to alleviate issues in both dimensions. The main idea is to enable direct document analytics on compressed data. We present how the concept can be materialized on Sequitur, a compression algorithm that produces hierarchical grammar-like representations. We discuss the major challenges in applying the idea to various document analytics tasks, and reveal a set of guidelines and also assistant software modules for developers to effectively apply
compression-based direct processing
. Experiments show that our proposed techniques save 90.8% storage space and 77.5% memory usage, while speeding up data processing significantly, i.e., by 1.6X on sequential systems, and 2.2X on distributed clusters, on average.
You say 'what', i hear 'where' and 'why'
Authors: Müller Tobias , Dietrich Benjamin , Grust Torsten
Published: September 2018

SQL declaratively specifies
what
the desired output of a query is. This work shows that a non-standard interpretation of the SQL semantics can, instead, disclose
where
a piece of the output originated in the input and
why
that piece found its way into the result. We derive such data provenance for very rich SQL dialects---including recursion, windowed aggregates, and user-defined functions---at the fine-grained level of individual table cells. The approach is non-invasive and implemented as a compositional source-level SQL rewrite: an input SQL query is transformed into its own interpreter that wields data dependencies instead of regular values. We deliberately design this transformation to preserve the shape of both data and query, which allows provenance derivation to scale to complex queries without overwhelming the underlying database system.
ModelarDB
Authors: Jensen Søren Kejser , Pedersen Torben Bach , Thomsen Christian
Published: September 2018

Industrial systems, e.g., wind turbines, generate big amounts of data from reliable sensors with high velocity. As it is unfeasible to store and query such big amounts of data, only simple aggregates are currently stored. However, aggregates remove fluctuations and outliers that can reveal underlying problems and limit the knowledge to be gained from historical data. As a remedy, we present the distributed Time Series Management System (TSMS)
ModelarDB
that uses
models
to store sensor data. We thus propose an online, adaptive multi-model compression algorithm that maintains data values within a user-defined error bound (possibly zero). We also propose (i) a database schema to store time series as models, (ii) methods to push-down predicates to a key-value store utilizing this schema, (iii) optimized methods to execute aggregate queries on models, (iv) a method to optimize execution of projections through static code-generation, and (v) dynamic extensibility that allows new models to be used without recompiling the TSMS. Further, we present a general modular distributed TSMS architecture and its implementation, ModelarDB, as a portable library, using Apache Spark for query processing and Apache Cassandra for storage. An experimental evaluation shows that, unlike current systems, ModelarDB hits a sweet spot and offers fast ingestion, good compression, and fast, scalable online aggregate query processing at the same time. This is achieved by dynamically adapting to data sets using multiple models. The system degrades gracefully as more outliers occur and the actual errors are much lower than the bounds.
Efficient searchable encryption through compression
Authors: Demertzis Ioannis , Talapatra Rajdeep , Papamanthou Charalampos
Published: September 2018

In this work we design new searchable encryption schemes whose goal is to minimize the
number of cryptographic operations
required to retrieve the result---a dimension mostly overlooked by previous works, yet very important in practice. Our main idea is to utilize compression so as to reduce the size of the plaintext indexes before producing the encrypted searchable indices. Our solution can use any existing Searchable Encryption (SE) scheme as a black-box and any combination of lossless compression algorithms, without compromising security.

The efficiency of our schemes varies based on the leakage exposed by the underlying application. For instance, for private keyword search (more leakage), we demonstrate up to 188× savings in search time, while for database search (less leakage) our savings are up to 62×.

The power of our approach is better manifested when combined with more secure, yet less practical, cryptographic tools, such as Oblivious Random Access Memory (ORAM). In particular while ORAM is known to be prohibitively expensive for large-scale applications, we show that our compress-first-ORAM-next approach allows significant more efficient index search time, reducing the time for executing a query with result of size more than one million tuples from approximately 21 hours to 20 minutes.
Locke and the Methodology of Newton’s Principia
Authors: Connolly Patrick J.
Published: September 2018

A number of commentators have recently suggested that there is a puzzle surrounding Locke’s acceptance of Newton’s Principia. On their view, Locke understood natural history as the primary methodology for natural philosophy and this commitment was at odds with an embrace of mathematical physics. This article considers various attempts to address this puzzle and finds them wanting. It then proposes a more synoptic view of Locke’s attitude towards natural philosophy. Features of Locke’s biography show that he was deeply interested in mathematical physics long before the publication of the Principia. This interest was in line with important developments in the Royal Society. It is argued that Locke endorsed a two-stage approach to natural philosophy which was consistent with an embrace of both natural history and mathematical physics. The Principia can be understood as consistent with this approach.
Branches of Forced Oscillations Induced by a Delayed Periodic Force
Authors: Calamai Alessandro , Pera Maria Patrizia , Spadini Marco
Published: September 2018

We study global continuation properties of the set of T-periodic solutions of parameterized second order delay differential equations with constant time lag on smooth manifolds. We apply our results to get multiplicity of T-periodic solutions. Our topological approach is mainly based on the notion of degree of a tangent vector field.
Language acquisition and linguistic theory: When linguistic theory meets empirical data
Authors: Guijarro-Fuentes Pedro
Published: September 2018

This Special Issue brings together the current work of well-established and well-known researchers in the field of language acquisition from a formal approach across several languages and of bilingual acquisition (2L1 and adult simultaneous and successive bilinguals), focusing on the syntax, semantics and pragmatics of different linguistic phenomena. Specifically, the four papers that will encompass this Special Issue together with an afterword paper written by a leading researcher in the field, Itziar Laka, discuss two main issues for current linguistic theory, both related, in this discussion, to Spanish: on the one hand, how do data and phenomena from the acquisition of different Romance languages inform and shape generative linguistic theory? And, on the other, how does generative linguistic theory frame and constrain research on the acquisition of Romance languages? To that end, divergent bilingual populations are used in these studies, which present longitudinal or cross-sectional data using a diverse range of methodologies (more on this within the individual summaries).
Improving diagnosis by improving education: a policy brief on education in healthcare professions
Authors: Graber Mark L. , Rencic Joseph , Rusz Diana , Papa Frank , Croskerry Pat , Zierler Brenda , Harkless Gene , Giuliano Michael , Schoenbaum Stephen , Colford Cristin , Cahill Maureen , Olson Andrew P.J.
Published: September 2018

Diagnostic error is increasingly recognized as a major patient safety concern. Efforts to improve diagnosis have largely focused on safety and quality improvement initiatives that patients, providers, and health care organizations can take to improve the diagnostic process and its outcomes. This educational policy brief presents an alternative strategy for improving diagnosis, centered on future healthcare providers, to improve the education and training of clinicians in every health care profession. The hypothesis is that we can improve diagnosis by improving education. A literature search was first conducted to understand the relationship of education and training to diagnosis and diagnostic error in different health care professions. Based on the findings from this search we present the justification for focusing on education and training, recommendations for specific content that should be incorporated to improve diagnosis, and recommendations on educational approaches that should be used. Using an iterative, consensus-based process, we then developed a driver diagram that categorizes the key content into five areas. Learners should: 1) Acquire and effectively use a relevant knowledge base, 2) Optimize clinical reasoning to reduce cognitive error, 3) Understand system-related aspects of care, 4) Effectively engage patients and the diagnostic team, and 5) Acquire appropriate perspectives and attitudes about diagnosis. These domains echo recommendations in the National Academy of Medicine’s report Improving Diagnosis in Health Care. The National Academy report suggests that true interprofessional education and training, incorporating recent advances in understanding diagnostic error, and improving clinical reasoning and other aspects of education, can ultimately improve diagnosis by improving the knowledge, skills, and attitudes of all health care professionals.
Numerical limitations of the attainment of the orientation of geological planes
Authors: Michalak Michał
Published: September 2018

The paper discusses limitations of analytical attainment of the attitude of a geological plane by using three non-collinear points. We present problems that arise during computing the orientation of a plane generated by almost collinear points. We referred these errors to floating-point arithmetic inaccuracies. To demonstrate the problem, we examined a surface of constant orientation. We used Delaunay triangulation to calculate its local orientation parameters. We introduced a new measure of collinearity applicable for collecting attitude of planar triangles. Using this measure we showed that certain planes generated by the triangulation cannot be treated as a reliable source of measurement. To examine the relationship between collinearity and orientation, we used a combinatorial algorithm to obtain all possible planes from the given set of points. A statistical criterion of rejecting almost collinear planes was suggested.