bluuebunny/crossref_metadata_2025_split huggingface dataset:
A problem peculiar to women: Mental health in menopause<p>Kadına özgü bir sorun: Menopozda ruh sağlığı
Authors: Ertekin Pınar Şükran , Yıldırım Gülay , Duran Özlem , Cesur Büşra 
Published: September 2015

<p>Menopause ranked among reproductive health is one of the issues that requires special consideration because of the position of women. In terms of the elimination of inequalities in gender-related health, it is important to investigate this issue in a holistic approach. Today with the increasing length of life, the time that spent in menopause period is increasing too. Accepted physiological event menopause leads to changes in physiological, mental, social and sexual ways. Changes in the levels of reproductive hormones lead to menstrual cycle disruption, vasomotor symptoms, sleep disorders and mood changes. As menopause period develops with pathological events which threaten life, impair quality of life and affect marriage relationship, it is an important period needs to be addressed by health professionals. In this context, identifying mental problems gone through in menopause period is important in terms of holistic approach. Women in menopause periods should be addressed in the biopsychosocial and cultural integrity and health professionals should determine women who are at risk of mental problems at an early stage and should provide support in a mental aspect.</p><p> </p><p><strong>Özet</strong></p><p>Üreme sağlığı içinde yer alan menopoz, kadınların konumlarından dolayı özel yaklaşım gerektiren konulardan biridir. Cinsiyete bağlı sağlıkta eşitsizlik durumunun ortadan kaldırması açısından bütüncül yaklaşım içerisinde bu konunun araştırılmış olması önemlidir. Günümüzde yaşam süresinin uzaması ile birlikte menopoz döneminde geçirilen süre de artmaktadır. Fizyolojik bir olay olarak kabul edilen menopoz kadında fizyolojik, mental, sosyal ve cinsel yönden birçok değişimler yaşanmasına neden olmaktadır. Üreme hormonlarının düzeylerindeki değişiklikler sonucu adet döngüsünde bozulma, vazomotor belirtiler, uyku bozuklukları, duygu durum değişiklikleri görülebilmektedir. Menopoz dönemi yaşamı tehdit edici, yaşam kalitesini bozucu ve evlilik ilişkisini etkileyen patolojik olaylarla birlikte seyrettiği için sağlık profesyonelleri tarafından ele alınması gereken önemli bir dönemdir. Bu bağlamda menopoz döneminde yaşanan ruhsal sorunların belirlenmesi bütüncül yaklaşım açısından önem taşımaktadır. Menopoz döneminde olan kadınlar biyopsikososyal ve kültürel bütünlük içinde ele alınmalı, sağlık profesyonelleri, ruhsal sorunlar yönünden risk altında olan kadınları erken dönemde belirlemeli, ruhsal yönden destek almalarını sağlamalıdır.</p>
A study on fourth grade students’ social emotional learning skills in terms of various variables<p>4. Sınıf öğrencilerinin sosyal duygusal öğrenme becerilerinin çeşitli değişkenlere göre incelenmesi
Authors: Şara Perihan , Hasanoğlu Gülcihan 
Published: September 2015

<p>Children who have outstanding social and emotional abilities develop more positive relations with their peers and adults; they maintain more positive emotional harmony and mental health; as a consequence, they put in better performance at school. Development of these skills takes place in the early stages of childhood, and continues throughout childhood and adolescence being affected by relations and social environment. Survey research has been adopted as quantitative research methods. The population of the questionnaire consists of 4th graders students who receive education in districts of Bursa and Uşak. Research samples have been attained by making use of criterion sampling which is one of the purposeful sampling methods. Questionnaire has been responded to by some 258 pupils in Bursa and some 200 in Uşak. The objective of this research is to discover if the socio-emotional learning abilities of these 4th graders show any differences in some variables such as gender, socio-economic level and residence in a greate or a little town. In the research, social-emotional learning scale, which was developed by Coryn, Spybrook, Evergreen and Blinkiewicz (2009) and adapted by Akın, Arslan and Demir (2012), and personal information questionnaire have been utilized as a means of data collection.</p><p> </p><p><strong>Özet</strong></p><p>Güçlü sosyal ve duygusal becerilere sahip çocuklar; akranlarıyla ve yetişkinlerle daha olumlu ilişkilere ve daha olumlu duygusal uyum ve ruh sağlığına sahip olmakta, okullarda daha iyi performans göstermektedirler. Bu becerilerin gelişimi çocukluğun ilk yıllarında başlar, çocukluk ve ergenlik boyunca, ilişkiler ve sosyal çevre tarafından etkilenerek devam eder. Okullarda, sosyal ve duygusal gelişim ile akademik gelişim birbirine bağlı olarak sağlanabilir. Araştırma nicel araştırma yöntemlerinden tarama modeli kullanılmıştır. Araştırmanın evrenini Bursa ve Uşak illerindeki 4. sınıfa devam eden öğrenciler oluşturmaktadır. Araştırmanın örneklemi ise amaçlı örneklem yöntemlerinden ölçüt örneklem yönteminden yararlanılarak elde edilmiştir. Araştırmaya Bursa’dan 258, Uşak’tan 200 öğrenci katılmıştır. Bu çalışmada 4. sınıf öğrencilerinin sosyal duygusal öğrenme becerilerinin cinsiyete, sosyo-ekonomik düzeye ve büyük şehirde ya da küçük şehirde yaşama durumlarına göre farklılık gösterip göstermediği araştırılmıştır. Araştırmada 4.sınıftaki öğrencilerin Sosyal‐Duygusal Öğrenme becerilerini belirlemek üzere Coryn, Spybrook, Evergreen ve Blinkiewicz (2009) tarafından geliştirilen ve Akın, Arslan ve Demir (2012) tarafından uyarlanan Sosyal Duygusal Öğrenme Ölçeği (SDÖÖ) kullanılmıştır.</p>
SEMA-JOIN
Authors: He Yeye , Ganjam Kris , Chu Xu 
Published: September 2015

Join is a powerful operator that combines records from two or more tables, which is of fundamental importance in the field of relational database. However, traditional join processing mostly relies on string equality comparisons. Given the growing demand for ad-hoc data analysis, we have seen an increasing number of scenarios where the desired join relationship is not equi-join. For example, in a spreadsheet environment, a user may want to join one table with a subject column country-name, with another table with a subject column country-code. Traditional equi-join cannot handle such joins automatically, and the user typically has to manually find an intermediate mapping table in order to perform the desired join.
We develop a SEMA-JOIN approach that is a first step toward allowing users to perform semantic join automatically, with a click of the button. Our main idea is to utilize a data-driven method that leverages a big table corpus with over 100 million tables to determine statistical correlation between cell values at both row-level and column-level. We use the intuition that the correct join mapping is the one that maximizes aggregate pairwise correlation, to formulate the join prediction problem as an optimization problem. We develop a linear program relaxation and a rounding argument to obtain a 2-approximation algorithm in polynomial time. Our evaluation using both public tables from the Web and proprietary Enterprise tables from a large company shows that the proposed approach can perform automatic semantic joins with high precision for a variety of common join scenarios.
A scalable distributed graph partitioner
Authors: Margo Daniel , Seltzer Margo 
Published: September 2015

We present Scalable Host-tree Embeddings for Efficient Partitioning (Sheep), a distributed graph partitioning algorithm capable of handling graphs that far exceed main memory. Sheep produces high quality edge partitions an order of magnitude faster than both state of the art offline (e.g., METIS) and streaming partitioners (e.g., Fennel). Sheep's partitions are independent of the input graph distribution, which means that graph elements can be assigned to processing nodes arbitrarily without affecting the partition quality.
Sheep transforms the input graph into a strictly smaller elimination tree via a distributed map-reduce operation. By partitioning this tree, Sheep finds an upper-bounded communication volume partitioning of the original graph.
We describe the Sheep algorithm and analyze its space-time requirements, partition quality, and intuitive characteristics and limitations. We compare Sheep to contemporary partitioners and demonstrate that Sheep creates competitive partitions, scales to larger graphs, and has better runtime.
Distributed architecture of Oracle database in-memory
Authors: Mukherjee Niloy , Chavan Shasank , Colgan Maria , Das Dinesh , Gleeson Mike , Hase Sanket , Holloway Allison , Jin Hui , Kamp Jesse , Kulkarni Kartik , Lahiri Tirthankar , Loaiza Juan , Macnaughton Neil , Marwah Vineet , Mullick Atrayee , Witkowski Andy , Yan Jiaqi , Zait Mohamed 
Published: September 2015

Over the last few years, the information technology industry has witnessed revolutions in multiple dimensions. Increasing ubiquitous sources of data have posed two connected challenges to data management solutions -- processing unprecedented volumes of data, and providing ad-hoc real-time analysis in mainstream production data stores without compromising regular transactional workload performance. In parallel, computer hardware systems are scaling out elastically, scaling up in the number of processors and cores, and increasing main memory capacity extensively. The data processing challenges combined with the rapid advancement of hardware systems has necessitated the evolution of a new breed of main-memory databases optimized for mixed OLTAP environments and designed to scale.
The Oracle RDBMS In-memory Option (DBIM) is an industry-first distributed dual format architecture that allows a database object to be stored in columnar format in main memory highly optimized to break performance barriers in analytic query workloads, simultaneously maintaining transactional consistency with the corresponding OLTP optimized row-major format persisted in storage and accessed through database buffer cache. In this paper, we present the distributed, highly-available, and fault-tolerant architecture of the Oracle DBIM that enables the RDBMS to transparently scale out in a database cluster, both in terms of memory capacity and query processing throughput. We believe that the architecture is unique among all mainstream in-memory databases. It allows complete application-transparent, extremely scalable and automated distribution of Oracle RDBMS objects in-memory across a cluster, as well as across multiple NUMA nodes within a single server. It seamlessly provides distribution awareness to the Oracle SQL execution framework through affinitized fault-tolerant parallel execution within and across servers without explicit optimizer plan changes or query rewrites.
JetScope
Authors: Boutin Eric , Brett Paul , Chen Xiaoyu , Ekanayake Jaliya , Guan Tao , Korsun Anna , Yin Zhicheng , Zhang Nan , Zhou Jingren 
Published: September 2015

Interactive, reliable, and rich data analytics at cloud scale is a key capability to support low latency data exploration and experimentation over terabytes of data for a wide range of business scenarios. Besides the challenges in massive scalability and low latency distributed query processing, it is imperative to achieve all these requirements with effective fault tolerance and efficient recovery, as failures and fluctuations are the norm in such a distributed environment.
We present a cloud scale interactive query processing system, called JetScope, developed at Microsoft. The system has a SQL-like declarative scripting language and delivers massive scalability and high performance through advanced optimizations. In order to achieve low latency, the system leverages various access methods, optimizes delivering first rows, and maximizes network and scheduling efficiency. The system also provides a fine-grained fault tolerance mechanism which is able to efficiently detect and mitigate failures without significantly impacting the query latency and user experience. JetScope has been deployed to hundreds of servers in production at Microsoft, serving a few million queries every day.
FIT to monitor feed quality
Authors: Dasu Tamraparni , Shkapenyuk Vladislav , Srivastava Divesh , Swayne Deborah F. 
Published: September 2015

While there has been significant focus on collecting and managing data feeds, it is only now that attention is turning to their quality. In this paper, we propose a principled approach to online data quality monitoring in a dynamic feed environment. Our goal is to alert quickly when feed behavior deviates from expectations.We make contributions in two distinct directions. First, we propose novel enhancements to permit a publish-subscribe approach to incorporate data quality modules into the DFMS architecture. Second, we propose novel temporal extensions to standard statistical techniques to adapt them to online feed monitoring for outlier detection and alert generation atmultiple scalesalong three dimensions: aggregation at multiple time intervals to detect at varying levels of sensitivity; multiple lengths of data history for varying the speed at which models adapt to change; and multiple levels of monitoring delay to address lagged data arrival.FIT, or Feed Inspection Tool, is the result of a successful implementation of our approach. We present several case studies outlining the effective deployment of FIT in real applications along with user testimonials.
Efficient evaluation of object-centric exploration queries for visualization
Authors: Wu You , Harb Boulos , Yang Jun , Yu Cong 
Published: September 2015

The most effective way to explore data is through visualizing the results of exploration queries. For example, an exploration query could be an aggregate of some measures over time intervals, and a pattern or abnormality can be discovered through a time series plot of the query results. In this paper, we examine a special kind of exploration query, namely object-centric exploration query. Common examples include claims made about athletes in sports databases, such as "it is newsworthy that LeBron James has scored 35 or more points in nine consecutive games."
We focus on one common type of visualization, i.e., 2d scatter plot with heatmap. Namely, we consider exploration queries whose results can be plotted on a two-dimensional space, possibly with colors indicating object densities in regions. While we model results as pairs of numbers, the types of the queries are limited only by the users' imagination. In the LeBron James example above, the two dimensions are minimum points scored per game and number of consecutive games, respectively. It is easy to find other equally interesting dimensions, such as minimum rebounds per game or number of playoff games.
We formalize this problem and propose an efficient, interactive-speed algorithm that takes a user-provided exploration query (which can be a blackbox function) and produces an approximate visualization that preserves the two most important visual properties: the outliers and the overall distribution of all result points.
Query optimization in Oracle 12c database in-memory
Authors: Das Dinesh , Yan Jiaqi , Zait Mohamed , Valluri Satyanarayana R. , Vyas Nirav , Krishnamachari Ramarajan , Gaharwar Prashant , Kamp Jesse , Mukherjee Niloy 
Published: September 2015

Traditional on-disk row major tables have been the dominant storage mechanism in relational databases for decades. Over the last decade, however, with explosive growth in data volume and demand for faster analytics, has come the recognition that a different data representation is needed. There is widespread agreement that in-memory column-oriented databases are best suited to meet the realities of this new world.
Oracle 12c Database In-memory, the industry's first dual-format database, allows existing row major on-disk tables to have complementary in-memory columnar representations. The new storage format brings new data processing techniques and query execution algorithms and thus new challenges for the query optimizer. Execution plans that are optimal for one format may be sub-optimal for the other.
In this paper, we describe the changes made in the query optimizer to generate execution plans optimized for the specific format -- row major or columnar -- that will be scanned during query execution. With enhancements in several areas -- statistics, cost model, query transformation, access path and join optimization, parallelism, and cluster-awareness -- the query optimizer plays a significant role in unlocking the full promise and performance of Oracle Database In-Memory.
Live programming in the LogicBlox system
Authors: Green Todd J. , Olteanu Dan , Washburn Geoffrey 
Published: September 2015

The emerging category of self-service enterprise applications motivates support for "live programming" in the database, where the user's iterative data exploration triggers changes to installed application code and its output in real time.This paper discusses the technical challenges in supporting live programming in the database and presents the solution implemented in the LogicBlox commercial system. The workhorse architectural component is a "meta-engine" that incrementally maintains metadata representing application code, guides its compilation into an internal representation in the database kernel, and orchestrates maintenance of materialized views based on those changes. Our approach mirrors LogicBlox's declarative programming model and describes the maintenance of application code using declarative meta-rules; the meta-engine is essentially a "bootstrap" version of the database engine proper.Beyond live programming, the meta-engine turns out effective for a range of static analysis and optimization tasks. Outside of the database context, we speculate that our design may even provide a novel means of building incremental compilers for general-purpose programming languages.
The dataflow model
Authors: Akidau Tyler , Bradshaw Robert , Chambers Craig , Chernyak Slava , Fernández-Moctezuma Rafael J. , Lax Reuven , McVeety Sam , Mills Daniel , Perry Frances , Schmidt Eric , Whittle Sam 
Published: September 2015

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems.
We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost.
In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development.
GIS navigation boosted by column stores
Authors: Alvanaki Foteini , Goncalves Romulo , Ivanova Milena , Kersten Martin , Kyzirakos Kostis 
Published: September 2015

Earth observation sciences, astronomy, and seismology have large data sets which have inherently rich spatial and geospatial information. In combination with large collections of semantically rich objects which have a large number of thematic properties, they form a new source of knowledge for urban planning, smart cities and natural resource management.
Modeling and storing these properties indicating the relationships between them is best handled in a relational database. Furthermore, the scalability requirements posed by the latest 26-attribute light detection and ranging (LIDAR) data sets are a challenge for file-based solutions.
In this demo we show how to query a 640 billion point data set using a column store enriched with GIS functionality. Through a lightweight and cache conscious secondary index called Imprints, spatial queries performance on a flat table storage is comparable to traditional file-based solutions. All the results are visualised in real time using QGIS.
DBSeer
Authors: Yoon Dong Young , Mozafari Barzan , Brown Douglas P. 
Published: September 2015

The pressing need for achieving and maintaining high performance in database systems has made database administration one of the most stressful jobs in information technology. On the other hand, the increasing complexity of database systems has made qualified database administrators (DBAs) a scarce resource. DBAs are now responsible for an array of demanding tasks; they need to (i) provision and tune their database according to their application requirements, (ii) constantly monitor their database for any performance failures or slowdowns, (iii) diagnose the root cause of the performance problem in an accurate and timely fashion, and (iv) take prompt actions that can restore acceptable database performance.
However, much of the research in the past years has focused on improving the raw performance of the database systems, rather than improving their manageability. Besides sophisticated consoles for monitoring performance and a few auto-tuning wizards, DBAs are not provided with any help other than their own many years of experience. Typically, their only resort is trial-and-error, which is a tedious, ad-hoc and often sub-optimal solution.
In this demonstration, we present DBSeer, a workload intelligence framework that exploits advanced machine learning and causality techniques to aid DBAs in their various responsibilities. DBSeer analyzes large volumes of statistics and telemetry data collected from various log files to provide the DBA with a suite of rich functionalities including performance prediction, performance diagnosis, bottleneck explanation, workload insight, optimal admission control, and what-if analysis. In this demo, we showcase various features of DBSeer by predicting and analyzing the performance of a live database system. Will also reproduce a number of realistic performance problems in the system, and allow the audience to use DBSeer to quickly diagnose and resolve their root cause.