bluuebunny/crossref_metadata_2025 huggingface dataset:
Early separations: Dropout from online and face-to-face counseling
Authors: Buyruk Genç Arzu , Amanvermez Yağmur , Zeren Şerife Gonca , Erus Seher Merve
Published: September 2019

In this study, dropout from counseling sessions were investigated in order to examine the underlying causes of dropout. Forty-five university students who applied for counseling but dropped out after the preliminary interview or after any of the sessions participated in the study. The data of the research were collected by Counseling Dropout Survey that was developed by researchers. In this study, frequency distribution and percentages were calculated. Content analysis was used in the analysis of quantitative data. At the end of the study, mostly indicated reasons of dropout were busy schedule in university or at work, decrease in motivation and their belief that the methods used in the counseling would not help their improvement. It has also been found that the clients who drop out particularly after the first or second session have a negative attitude towards counseling process in general.
Applied linguistics perspective in developing reading material
Authors: Kaygısız Çağrı
Published: September 2019

Language teaching is a skill-based teaching aiming at the development of language skills and teaching tools are one of the important components of the teaching process in terms of the elements that will enable to increase the knowledge and experience related to the skill intended to be developed as the input source. Therefore, teaching tools should be designed in a way that allows access to the targeted development level, taking into account the nature of the intended skill area and the nature of language processing. As known, reading is a process of mental decoding, where many processes related to each other are performed simultaneously in order to gain access to the deep meaning of the text from the moment the interaction begins and this process is based on the background knowledge of the individual's experiences and the mechanics of the language components. Accordingly, for the development of reading skills, knowledge of phonological, semantic, morphological, and syntactic systems must be fully acquired, and these systems must function in integrated manner. In this respect, the teaching tools prepared for the purpose of developing reading skills should be designed in accordance with the principles of general training material’s development and taking into account the structure of the reading action itselft. In this sense, the aim of the study is to provide a theoretical perspective on the possible qualifications of the teaching tools that will be used for the development of reading skill, based on the studies of the field of applied linguistics related to the reading comprehension process.
Strategi Pembentukan Tambrauw Sebagai Kabupaten Konservasi di Papua
Authors: Marten Sepus Fatem , Awang San Afri , Maryudi Ahmad , Pudyatmoko Satyawan , Marwa Jonni , Manuhua Devi , Lembang Salmon
Published: September 2019

Tambrauw merupakan kabupaten di Papua Barat yang menempatkan kebijakan konservasi sebagai domain pembangunan daerah. Penelitian ini bertujuan merancang strategi bagi pembentukan Tambrauw sebagai Kabupaten Konservasi. Penelitian berlangsung sejak bulan oktober-November 2017, menggunakan pendekatan SWOT untuk analisis kekuatan dan kelemahan (faktor Internal) dan peluang dan ancaman (faktor eksternal).  Hasil penelitian menunjukkan bahwa Tambrauw layak sebagai Kabupaten Konservasi, dimana posisinya berada pada kwadran I. Artinya bahwa terdapat kekuatan dan peluang sebagai faktor kunci pembentukan kabupaten Konservasi. Kelayakan sebagai kabupetan konservasi juga terlihat melalui hasil analisis faktor internal dan eksternal, dimana Tambrauw dimungkinkan menjadi kabupaten konservasi dengan nilai evaluasi faktor internal 3.20 dan ekternal sebesar 2.75. Meskipun faktor kelemahan dan keterancaman memiliki potensi cukup besar, namun melalui 4 pilihan strategi yang ditetapkan diyakini akan membantu pemerintah Kabupaten Tambrauw memperkecil ancaman dan kelemahan dimaksud.
STAR
Authors: Lu Yi , Yu Xiangyao , Madden Samuel
Published: September 2019

In this paper, we present STAR, a new distributed in-memory database with asymmetric replication. By employing a single-node non-partitioned architecture for some replicas and a partitioned architecture for other replicas, STAR is able to efficiently run both highly partitionable workloads and workloads that involve cross-partition transactions. The key idea is a new
phase-switching
algorithm where the execution of single-partition and cross-partition transactions is separated. In the partitioned phase, single-partition transactions are run on multiple machines in parallel to exploit more concurrency. In the single-master phase, mastership for the entire database is switched to a single designated master node, which can execute these transactions without the use of expensive coordination protocols like two-phase commit. Because the master node has a full copy of the database, this phase-switching can be done at negligible cost. Our experiments on two popular benchmarks (YCSB and TPC-C) show that high availability via replication can coexist with fast serializable transaction execution in distributed in-memory databases, with STAR outperforming systems that employ conventional concurrency control and replication algorithms by up to one order of magnitude.
An experimental evaluation of large scale GBDT systems
Authors: Fu Fangeheng , Jiang Jiawei , Shao Yingxia , Cui Bin
Published: September 2019

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we find it surprising that the existing systems manage the training dataset in different ways, but none of them have studied the impact of data management. To that end, this paper aims to study the pros and cons of different data management methods regarding the performance of distributed GBDT.

We first introduce a quadrant categorization of data management policies based on data partitioning and data storage. Then we conduct an in-depth systematic analysis and summarize the advantageous scenarios of the quadrants. Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases. To validate our analysis empirically, we implement different quadrants in the same code base and compare them under extensive workloads, and finally compare Vero with other state-of-the-art systems over a wide range of datasets. Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload.
Crossbow
Authors: Koliousis Alexandros , Watcharapichat Pijika , Weidlich Matthias , Mai Luo , Costa Paolo , Pietzuch Peter
Published: September 2019

Deep learning models are trained on servers with many GPUs, and training must scale with the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel synchronous stochastic gradient descent: they process a batch of training data at a time, partitioned across GPUs, and average the resulting partial gradients to obtain an updated global model. To fully utilise all GPUs, systems must increase the batch size, which hinders statistical efficiency. Users tune hyper-parameters such as the learning rate to compensate for this, which is complex and model-specific.

We describe Crossbow, a new single-server multi-GPU system for training deep learning models that enables users to freely choose their preferred batch size---however small---while scaling to multiple GPUs. Crossbow uses many parallel model replicas and avoids reduced statistical efficiency through a new synchronous training method. We introduce SMA, a synchronous variant of model averaging in which replicas
independently
explore the solution space with gradient descent, but adjust their search
synchronously
based on the trajectory of a globally-consistent average model. Crossbow achieves high hardware efficiency with small batch sizes by potentially training multiple model replicas per GPU, automatically tuning the number of replicas to maximise throughput. our experiments show that Crossbow improves the training time of deep learning models on an 8-GPU server by 1.3--4X compared to TensorFlow.
Intermittent query processing
Authors: Tang Dixin , Shang Zechao , Elmore Aaron J. , Krishnan Sanjay , Franklin Michael J.
Published: September 2019

Many applications ingest data in an intermittent, yet largely predictable, pattern. Existing systems tend to ignore how data arrives when making decisions about how to update (or refresh) an ongoing query. To address this shortcoming we propose a new query processing paradigm, Intermittent Query Processing (IQP), that bridges query execution and policies, to determine when to update results and how much resources to allocate for ensuring fast query updates. Here, for a query the system provides an initial result that is to be refreshed when policy dictates, such as after a defined number of new records arrive or a time interval elapses. In between intermittent data arrivals, IQP inactivates query execution by selectively releasing some resources occupied in normal execution that will be least helpful (for future refreshes) according to the arrival patterns for new records. We present an IQP prototype based on PostgreSQL that selectively persists the state associated with query operators to allow for fast query updates while constraining resource consumption. Our experiments show that for several application scenarios IQP greatly lowers query processing latency compared to batch systems, and largely reduces memory consumption with comparable latency compared to a state-of-the-art incremental view maintenance technique.
Neo
Authors: Marcus Ryan , Negi Parimarjan , Mao Hongzi , Zhang Chi , Alizadeh Mohammad , Kraska Tim , Papaemmanouil Olga , Tatbul Nesime
Published: September 2019

Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce
Neo
(
Neural Optimizer
), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them.
ITAA
Authors: Zhang Yipeng , Bao Zhifeng , Mo Songsong , Li Yuchen , Zhou Yanghao
Published: September 2019

In this paper, we demonstrate an Intelligent Trajectory-driven outdoor Advertising deployment Assistant (ITAA), which assists users to find an optimal strategy for outdoor advertising (ad) deployment. The challenge is how to measure the influence to the moving trajectories of ads, and how to optimize the placement of ads among billboards that maximize the influence has been proven NP-hard. Therefore, we develop a framework based on two trajectory-driven influence models. ITAA is built upon this framework with a user-friendly UI. It serves both ad companies and their customers. We enhance the interpretability to improve the user's understanding of the influence of ads. The interactive function of ITAA is made interpretable and easy to engage.
PRIMAT
Authors: Franke Martin , Sehili Ziad , Rahm Erhard
Published: September 2019

Privacy-preserving record linkage (PPRL) is increasingly demanded in real-world applications, e.g., in the health-care domain, to combine person-related data for data analysis while preserving the privacy of individuals. However, the adoption of PPRL is hampered by the absence of easy-to-use and powerful PPRL tools covering the
entire
PPRL process. We therefore demonstrate Primat, a flexible and scalable tool that enables the definition and application of tailored PPRL workflows as well as the comparative evaluation of different PPRL methods. We introduce the main requirements for PPRL tools and discuss previous tool efforts that do not fully meet the requirements and have not been applied in practice. By contrast, Primat covers the whole PPRL life-cycle and improves applicability by providing various components for data owners and the central linkage to be executed by a trusted linkage unit.
VISE
Authors: Choi Hyewon , Zhu Erkang , Bangash Arsala , Miller Renée J.
Published: September 2019

We present VISE, or Vehicle Image Search Engine, to support the fast search of similar vehicles from low-resolution traffic camera images. VISE can be used to trace and locate vehicles for applications such as police investigations when high-resolution footage is not available. Our system consists of three components: an interactive user-interface for querying and browsing identified vehicles; a scalable search engine for fast similarity search on millions of visual objects; and an image processing pipeline that extracts feature vectors of objects from video frames. We use transfer learning technique to integrate state-of-the-art Convolutional Neural Networks with two different refinement methods to achieve high retrieval accuracy. We also use an efficient high-dimensional nearest neighbor search index to enable fast retrieval speed. In the demo, our system will offer users an interactive experience exploring a large database of traffic camera images that is growing in real time at 200K frames per day.
In-database distributed machine learning
Authors: Sandha Sandeep Singh , Cabrera Wellington , Al-Kateb Mohammed , Nair Sanjay , Srivastava Mani
Published: September 2019

Machine learning has enabled many interesting applications and is extensively being used in big data systems. The popular approach - training machine learning models in frameworks like Tensorflow, Pytorch and Keras - requires movement of data from database engines to analytical engines, which adds an excessive overhead on data scientists and becomes a performance bottleneck for model training. In this demonstration, we give a practical exhibition of a solution for the enablement of distributed machine learning natively inside database engines. During the demo, the audience will interactively use Python APIs in Jupyter Notebooks to train
multiple linear regression
models on synthetic regression datasets and
neural network models
on vision and sensory datasets directly inside Teradata SQL Engine.
SHOAL
Authors: Li Zhao , Chen Xia , Pan Xuming , Zou Pengcheng , Li Yuchen , Yu Guoxian
Published: September 2019

E-commerce taxonomy plays an essential role in online retail business. Existing taxonomy of e-commerce platforms organizes items into an ontology structure. However, the ontology-driven approach is subject to costly manual maintenance and often does not capture user's search intention, particularly when user searches by her personalized needs rather than a universal definition of the items. Observing that search queries can effectively express user's intention, we present a novel large-Scale Hierarchical taxOnomy via grAph based query coaLition (
SHOAL
) to bridge the gap between item taxonomy and user search intention. SHOAL organizes
hundreds of millions of items
into a
hierarchical topic structure
. Each topic that consists of a cluster of items denotes a conceptual shopping scenario, and is tagged with easy-to-interpret descriptions extracted from search queries. Furthermore, SHOAL establishes correlation between categories of ontology-driven taxonomy, and offers opportunities for explainable recommendation. The feedback from domain experts shows that SHOAL achieves a precision of 98% in terms of placing items into the right topics, and the result of an online A/B test demonstrates that SHOAL boosts the Click Through Rate (CTR) by 5%. SHOAL has been deployed in Alibaba and supports millions of searches for online shopping per day.
CAPRIO
Authors: Costa Constantinos , Ge Xiaoyu , Chrysanthis Panos K.
Published: September 2019

Recently, navigation and localization systems have emerged to support queries like the shortest distance in either indoor or outdoor with additional constraints. These systems, however, neither combine the indoor and outdoor information nor consider the external natural conditions like the weather that one may face across an outdoor path.

In this demonstration paper we present
CAPRIO
, which proposes and implements a novel graph representation that integrates indoor and outdoor information to discover paths that personalize outdoor exposure while minimizes the overall path length. We also demonstrate how unifying the graph algorithms for indoor and outdoor navigation enables significant optimizations that would not be possible otherwise.
HERMIT in action
Authors: Wu Yingjun , Yu Jia , Tian Yuanyuan , Sidle Richard , Barber Ronald
Published: September 2019

Database administrators construct secondary indexes on data tables to accelerate query processing in relational database management systems (RDBMSs). These indexes are built on top of the most frequently queried columns according to the data statistics. Unfortunately, maintaining multiple secondary indexes in the same database can be extremely space consuming, causing significant performance degradation due to the potential exhaustion of memory space. However, we find that there indeed exist many opportunities to save storage space by exploiting column correlations. We recently introduced Hermit, a succinct secondary indexing mechanism for modern RDBMSs. Hermit judiciously leverages the rich soft functional dependencies hidden among columns to prune out redundant structures for indexed key access. instead of building a complete index that stores every single entry in the key columns, Hermit navigates any incoming key access queries to an existing index built on the correlated columns. This is achieved through the Tiered Regression Search Tree (TRS-Tree), a succinct, ML-enhanced data structure that performs fast curve fitting to adaptively and dynamically capture both column correlations and outliers. In this demonstration, we showcase Hermit's appealing characteristics. we not only demonstrate that Hermit can significantly reduce space consumption with limited performance overhead in terms of query response time and index maintenance time, but also explain in detail the rationale behind Hermit's high efficiency using interactive online query processing examples.
Demonstration of Krypton
Authors: Ordookhanians Allen , Li Xin , Nakandala Supun , Kumar Arun
Published: September 2019

In this demonstration, we present Krypton, a system for accelerating occlusion-based deep convolution neural network (CNN) explanation workloads. Driven by the success of CNNs in image understanding tasks, there is growing adoption of CNNs in various domains, including high stakes applications such as radiology. However, users of such applications often seek an "explanation" for why a CNN predicted a certain label. One of the most widely used approaches for explaining CNN predictions is the occlusion-based explanation (OBE) method. This approach is computationally expensive due to the large number of re-inference requests produced. Krypton reduces the runtime of OBE by up to 35x by enabling incremental and approximate inference optimizations that are inspired by classical database query optimization techniques. We allow the audience to interactively diagnose CNN predictions from several use cases, including radiology and natural images. A short video of our demonstration can be found here: https://youtu.be/1OWddbd4n6Y
LensXPlain
Authors: Miao Zhengjie , Lee Andrew , Roy Sudeepa
Published: September 2019

In this demonstration, we will present LensXPlain, an interactive system to help users understand answers of aggregate queries by providing meaningful explanations. Given a SQL group-by query and a question from a user "
why output o is high/low
", or "
why output o
1
is higher/lower than o
2
", LensXPlain helps users explore the results and find subsets of tuples captured by predicates that contributed the most toward such observations. The contributions are measured either by
intervention
(if the contributing tuples are removed, the values or the ratios in the user question change in the opposite direction), or by
aggravation
(if the query is restricted to the contributing tuples, the observations change more in the same direction). LensXPlain uses ensemble learning for recommending useful attributes in explanations, and employs a suite of optimizations to enable explanation generation and refinement at an interactive speed. In the demonstration, the audience can run aggregation queries over real world datasets, browse the answers using a graphical user interface, ask questions on unexpected/interesting query results with simple visualizations, and explore and refine explanations returned by LensXPlain.
Spade
Authors: Diao Yanlei , Guzewicz Paweł , Manolescu Ioana , Mazuran Mirjana
Published: September 2019

RDF data is complex; exploring it is hard, and can be done through many different metaphors. We have developed and propose to demonstrate Spade, a tool helping users discover meaningful content of an RDF graph by showing them the results of
aggregation (OLAP-style)
queries automatically identified from the data. Spade chooses aggregates that are
visually interesting
, a property formally based on statistic properties of the aggregation query results.

While well understood for relational data, such exploration raises multiple challenges for RDF: facts, dimensions and measures have to be
identified
(as opposed to known beforehand); as there are more candidate aggregates, assessing their interestingness can be very costly; finally,
ontologies
bring novel specific challenges but also novel opportunities, enabling
ontology-driven exploration
from an aggregate initially proposed by the system.

Spade is a
generic, extensible framework
, which we instantiated with: (
i
) novel methods for enumerating candidate measures and dimensions in the vast space of possibilities provided by an RDF graph; (
ii
) a set of aggregate interestingness functions; (
iii
) ontology-based interactive exploration, and (
iv
) efficient early-stop techniques for estimating the interestingness of an aggregate query.

The demonstration will comprise interactive scenarios on a variety of large, interesting RDF graphs.
Dietcoin
Authors: Frey Davide , Makkes Marc X. , Roman Pierre-Louis , Taïani François , Voulgaris Spyros
Published: September 2019

Distributed ledgers are among the most replicated data repositories in the world. They offer data consistency, immutability, and auditability, based on the assumption that each participating node locally verifies their entire content. Although their content, currently extending up to a few hundred gigabytes, can be accommodated by dedicated commodity hard disks, downloading it, processing it, and storing it in general-purpose desktop and laptop computers can prove largely impractical. Even worse, this becomes a prohibitive restriction for smartphones, mobile devices, and resource-constrained IoT devices.

In this demo, we present an implementation of Dietcoin, a Bitcoin protocol extension that allows nodes to perform secure local verification of Bitcoin transactions with small bandwidth and storage requirements. This demo presents and benchmarks the main features of Dietcoin that are important for today's cryptocurrencies and smart contract systems, but are missing in the current state-of-the-art: (i) allowing resource-constrained devices to verify the correctness of selected blocks locally without having to download the complete ledger; (ii) enabling devices to join a blockchain quickly yet securely, dropping bootstrap time from days down to a matter of seconds; (iii) providing a generic solution that can be applied to other distributed ledgers secured with Proof-of-Work.
Data Civilizer 2.0
Authors: Rezig El Kindi , Cao Lei , Stonebraker Michael , Simonini Giovanni , Tao Wenbo , Madden Samuel , Ouzzani Mourad , Tang Nan , Elmagarmid Ahmed K.
Published: September 2019

Data scientists spend over 80% of their time (1) parameter-tuning machine learning models and (2) iterating between data cleaning and machine learning model execution. While there are existing efforts to support the first requirement, there is currently no integrated workflow system that couples data cleaning and machine learning development. The previous version of Data Civilizer was geared towards data cleaning and discovery using a set of pre-defined tools. In this paper, we introduce Data Civilizer 2.0, an end-to-end workflow system satisfying both requirements. In addition, this system also supports a sophisticated data debugger and a workflow visualization system. In this demo, we will show how we used Data Civilizer 2.0 to help scientists at the Massachusetts General Hospital build their cleaning and machine learning pipeline on their 30TB brain activity dataset.
Ease.ml/ci and Ease.ml/meter in action
Authors: Renggli Cedric , Hubis Frances Ann , Karlaš Bojan , Schawinski Kevin , Wu Wentao , Zhang Ce
Published: September 2019

Developing machine learning (ML) applications is similar to developing traditional software --- it is often an iterative process in which developers navigate within a rich space of
requirements, design decisions, implementations, empirical quality
, and
performance
. In traditional software development, software engineering is the field of study which provides principled guidelines for this iterative process. However, as of today, the counterpart of "software engineering for ML" is largely missing --- developers of ML applications are left with powerful tools (e.g., TensorFlow and PyTorch) but little guidance regarding the development lifecycle itself.

In this paper, we view the management of ML development
life-cycles
from a
data management
perspective. We demonstrate two closely related systems, ease.ml/ci and ease.ml/meter, that provide
some
"principled guidelines" for ML application development: ci is a continuous integration engine for ML models and meter is a "profiler" for controlling
overfitting
of ML models. Both systems focus on managing the "statistical generalization power" of datasets used for assessing the quality of ML applications, namely, the
validation set
and the
test set
. By demonstrating these two systems we hope to spawn further discussions within our community on building this new type of data management systems for statistical generalization.
TextCube
Authors: Meng Yu , Huang Jiaxin , Shang Jingbo , Han Jiawei
Published: September 2019

Today's society is immersed in a wealth of text data, ranging from news articles, to social media, research literature, medical records, and corporate reports. A grand challenge of data science and engineering is to develop effective and scalable methods to extract structures and knowledge from massive text data to satisfy diverse applications, without extensive, corpus-specific human annotations.

In this tutorial, we show that TextCube provides a critical information organization structure that will satisfy such an information need. We overview a set of recently developed data-driven methods that facilitate automated construction of TextCubes from massive, domain-specific text corpora, and show that TextCubes so constructed will enhance text exploration and analysis for various applications. We focus on new TextCube construction methods that are scalable, weakly-supervised, domain-independent, language-agnostic, and effective (i.e., generating quality TextCubes from large corpora of various domains). We will demonstrate with real datasets (including news articles, scientific publications, and product reviews) on how TextCubes can be constructed to assist multidimensional analysis of massive text corpora.
Procella
Authors: Chattopadhyay Biswapesh , Dutta Priyam , Liu Weiran , Tinn Ott , Mccormick Andrew , Mokashi Aniket , Harvey Paul , Gonzalez Hector , Lomax David , Mittal Sagar , Ebenstein Roee , Mikhaylin Nikita , Lee Hung-ching , Zhao Xiaoyan , Xu Tony , Perez Luis , Shahmohammadi Farhad , Bui Tran , McKay Neil , Aya Selcuk , Lychagina Vera , Elliott Brett
Published: September 2019

Large organizations like YouTube are dealing with exploding data volume and increasing demand for data driven applications. Broadly, these can be categorized as: reporting and dashboarding, embedded statistics in pages, time-series monitoring, and ad-hoc analysis. Typically, organizations build specialized infrastructure for each of these use cases. This, however, creates silos of data and processing, and results in a complex, expensive, and harder to maintain infrastructure.

At YouTube, we solved this problem by building a new SQL query engine - Procella. Procella implements a superset of capabilities required to address all of the four use cases above, with high scale and performance, in a single product. Today, Procella serves hundreds of billions of queries per day across all four workloads at YouTube and several other Google product areas.
Native store extension for SAP HANA
Authors: Sherkat Reza , Florendo Colin , Andrei Mihnea , Blanco Rolando , Dragusanu Adrian , Pathak Amit , Khadilkar Pushkar , Kulkarni Neeraj , Lemke Christian , Seifert Sebastian , Iyer Sarika , Gottapu Sasikanth , Schulze Robert , Gottipati Chaitanya , Basak Nirvik , Wang Yanhong , Kandiyanallur Vivek , Pendap Santosh , Gala Dheren , Almeida Rajesh , Ghosh Prasanta
Published: September 2019

We present an overview of SAP HANA's Native Store Extension (NSE). This extension substantially increases database capacity, allowing to scale far beyond available system memory. NSE is based on a hybrid in-memory and paged column store architecture composed from data access primitives. These primitives enable the processing of hybrid columns using the same algorithms optimized for traditional HANA's in-memory columns. Using only three key primitives, we fabricated byte-compatible counterparts for complex memory resident data structures (e.g. dictionary and hash-index), compressed schemes (e.g. sparse and run-length encoding), and exotic data types (e.g. geo-spatial). We developed a new buffer cache which optimizes the management of paged resources by smart strategies sensitive to page type and access patterns. The buffer cache integrates with HANA's new execution engine that issues pipelined prefetch requests to improve disk access patterns. A novel load unit configuration, along with a unified persistence format, allows the hybrid column store to dynamically switch between in-memory and paged data access to balance performance and storage economy according to application demands while reducing Total Cost of Ownership (TCO). A new partitioning scheme supports load unit specification at table, partition, and column level. Finally, a new advisor recommends optimal load unit configurations. Our experiments illustrate the performance and memory footprint improvements on typical customer scenarios.
Integration of large-scale data processing systems and traditional parallel database technology
Authors: Abouzied Azza , Abadi Daniel J. , Bajda-Pawlikowski Kamil , Silberschatz Avi
Published: September 2019

In 2009 we explored the feasibility of building a hybrid SQL data analysis system that takes the best features from two competing technologies: large-scale data processing systems (such as Google MapReduce and Apache Hadoop) and parallel database management systems (such as Greenplum and Vertica). We built a prototype, HadoopDB, and demonstrated that it can deliver the high SQL query performance and efficiency of parallel database management systems while still providing the scalability, fault tolerance, and flexibility of large-scale data processing systems. Subsequently, HadoopDB grew into a commercial product, Hadapt, whose technology was eventually acquired by Teradata. In this paper, we provide an overview of HadoopDB's original design, and its evolution during the subsequent ten years of research and development effort. We describe how the project innovated both in the research lab, and as a commercial product at Hadapt and Teradata. We then discuss the current vibrant ecosystem of software projects (most of which are open source) that continued HadoopDB's legacy of implementing a systems level integration of large-scale data processing systems and parallel database technology.
What I probably did right and what I think I could have done better
Authors: Tan Wang-Chiew
Published: September 2019

They say a lot of good things in life are not free. Success is one of them. Successful research requires an immense amount of hard work and dedication over a long period of time. For better or worse, hard work alone does not guarantee success. In my experience, success is a marathon of hard work and some luck along the way. What is often forgotten is that it is important to enjoy the journey of hard work and appreciate many experiences and relationships along the way. I am deeply honored to receive the 2019 VLDB Women in Database Research Award. In the talk, I will share with you a retrospective of my journey so far, what I probably did right along the way, and perhaps more importantly, the many things I think I could have done better as a computer scientist and especially a female computer scientist.
Enabling data science for the majority
Authors: Parameswaran Aditya
Published: September 2019

Despite great strides in the generation, collection, and processing of data at scale, data science is still extremely inconvenient for the vast majority of the population. The driving goal of our research, over the past half decade, has been to make it easy for individuals and teams---regardless of programming or analysis expertise---manage, analyze, make sense of, and draw insights from large datasets. In this article, we reflect on a comprehensive suite of tools that we've been building to empower everyone to perform data science more efficiently and effortlessly, including DataSpread, a scalable spreadsheet tool that combines the benefits of spreadsheets and databases, and ZenVisage, a visual exploration tool that accelerates the discovery of trends or patterns. Our tools have been developed in collaboration with experts in various disciplines, including neuroscience, battery science, genomics, astrophysics, and ad analytics. We will discuss some of the key technical challenges underlying the development of these tools, and how we addressed them, drawing from ideas in multiple disciplines. in the process, we will outline a research agenda for tool development to empower everyone to tap into the hidden potential in their datasets at scale.
Pathways to institution and resource sustainability: lessons from the Forest Rights Act implementation in India
Authors: DAS S. , TYAGI N.
Published: September 2019

The Scheduled Tribes and Other Forest Dwellers (Recognition of Rights) Act, 2006, marked a momentous change in the institutional contour of forest management in India. The change suggested better tenure security along with a strong community resource regime. However, policy implementation
was marked by weak enforcement of community forest resource rights. Intrigued by variable performance of the Act, this study explains the process of rights negotiation, and analyzes the determinants of institutional and resource sustainability in the post-rights scenario.
 The study,
based on successful cases from Maharashtra, uses a comprehensive set of institutional and resource indicators, developed to suit the cultural and ecological context. The strongest factors that emerged from the two case studies were of community association with the resource, resource centrality
and visionary leadership. However, these factors manifested differently in the two villages. The success was also mediated by a range of intermediate variables in both villages. The findings offer a counter-narrative to the claim that granting forest rights will lead to forest fragmentation
and loss of forest cover. Both the villages had rich forest and robust institutions normatively fuelled by idea of selfgovernance, inclusion, and equity. Such normative foundations of equity and sustainability steered these institutions to successful collective outcomes.
Non-native tree species: strategies for sustainable management in Europe
Authors: KEČA Lj. , MARČETA M. , BOŽIČ G. , PERIĆ S. , TSVETKOV I. , ANDREASSEN K. , STIJOVIĆ A. , MANDŽUKOVSKI D. , ZLOKAPA B. , NICOLESCU V.N.
Published: September 2019

The prospects and challenges for non-native tree species (NNTS) in Southeast Europe (SEE) were analyzed using a combination of SWOT Analysis and the Analytic Hierarchy Process (AHP). Preference data from three groups of opinion leaders with extensive knowledge of the silviculture, ecology
and impact of climate change on NNTS in SEE (researchers, practitioners and decision-makers) were used. Results revealed that strengths and opportunities for all three analyzed elements outweigh their weaknesses and threats. In the review of silviculture, key strengths and opportunities were
identified as high p roductivity, adaptation to afforestation of degraded lands, gap filling in forest ecosystems after the loss of native tree species, and higher volume growth of NNTS compared to native tree species. Strength-Opportunity (SO) elements related to climate change were found
to be adaptive management responsiveness to climate change and increased length of growing period, possibility of better-adapted mixtures with NNTS under climate change, and replacement of tree species that are sensitive to pests and outbreaks resulting from climate change. These results provide
important insights into different segments of strategy approach of sustainable management of NNTS in relation to management, silviculture and climate change practices in SEE.
Statistical convergence of double sequences on product time scales
Authors: Çinar Muhammed , Yilmaz Emrah , Altin Yavuz , Gulsen Tuba
Published: September 2019

In this study, we present the basic concepts of statistical convergence for double sequences on an arbitrary product time scale.
Moreover, we investigate the connection between statistical convergence for double sequences and double Cesàro summability on a product time scale.
Explaining and Predicting Midterm Congressional Election Outcomes: Factoring in Opposition Party Strategy
Authors: King James D.
Published: September 2019

Midterm congressional elections are referenda on the president not by happenstance but by design, as the opposition party and its candidates attack the administration when political conditions provide the opportunity. Models for explaining and predicting the number of seats won by the president’s party incorporating the number of seats held before the election, prior election results, change in status of the economy, and presidential evaluation data conditioned by strategy of the opposition party are presented. These models improve upon traditional models of midterm election explanations and predictions, providing parsimonious, theory-driven, and accurate models of midterm election outcomes.
Dumping Trump and Electoral Bumps: The Causes and Consequences of Republican Officeholders’ Endorsement Decisions
Authors: Mathew Nicole Asmussen
Published: September 2019

Endorsement of the party’s nominee by the vast majority of that party’s top elected officials is a foregone conclusion in most presidential campaigns. But in 2016, Republican lawmakers were slow to endorse Donald Trump, lackluster in their enthusiasm, and a substantial number never endorsed or withdrew their endorsements by the campaign’s end. What explains lawmakers’ decisions to endorse, and the timing and strength of their endorsements? I find that primary endorsements were most likely to come from anti-immigration moderates, but as the campaign wore on, conservatives and members from more Republican districts became more supportive in their endorsements. Women were highly influenced by the release of the Access Hollywood tape, while Ted Cruz’s endorsers were stingy in their support until Cruz himself issued a late September endorsement. To see if these endorsement decisions made a difference in the election, I compare the performance of endorsers and non-endorsers in the 2016 congressional elections, and I compare Trump’s performance in districts in which he was endorsed to those in which he wasn’t. Rather than the traditional presidential coattails, I find evidence of negative coattails and reverse coattails. Endorsers did about 1.7 percentage points worse than non-endorsers, while Trump did 1.4 percentage points better in districts where the incumbent Republican endorsed him.
Using Journal Impact Factor to Assess Scholarly Records: Overcorrecting for the Potter Stewart Approach to Promotion and Tenure
Authors: Oldmixon Elizabeth A. , Grant J. Tobin
Published: September 2019

Promotion and tenure decisions frequently require an assessment of the quality of a candidate’s research record. Without carefully specifying what constitutes a tenurable and promotable record, departments frequently adopt the Potter Stewart approach – they know it when they see it. The benefit of such a system is that it allows for multiple paths to tenure and promotion and encourages holistic review, but the drawback is that it allows for the promotion and tenure process to be more easily manipulated by favoritism and bias. Incorporating transparent metrics such as journal impact factor (JIF) would seem like a good way to standardize the process. We argue, however, that when JIF becomes determinative, conceptual disadvantages and systematic biases are introduced into the process. JIF indicates the visibility or utility of ajournal; it does not and cannot tell us about individual articles published in that journal. Moreover, it creates inequitable paths to tenure on the basis of gender and subfield, given gendered patterns of publications and the variation in journal economies by subfield.
Ready for Hillary?: Explicit and Implicit Sexism in the 2016 Presidential Election
Authors: Barnello Michelle A. , Bitecofer Rachel , Kidd Quentin
Published: September 2019

The 2016 nomination of Hillary Clinton as the first female major party nominee for president created an unprecedented opportunity to test for evidence of explicit sexism in the electorate. Presidential elections normally produce two equally matched nominees with impressive public service resumes who behave similarly on the campaign trail. However, while Democrats were making history nominating the first female nominee, the Republicans also made history by selecting a nominee with no public service experience, a controversial personal background, and conduct that conflicted sharply with traditional norms of presidential candidates. In survey after survey, voters recognized that Clinton held a significant qualification advantage over Donald Trump. Yet, despite the fact that both men and women were more likely to rate Clinton as more qualified than her opponent overall, using an innovative approach via an original survey, we find evidence of implicit sexism in the way that some males evaluated Clinton compared to their female counterparts.
“One Difficulty…of a Serious Nature”: The Overlooked Racial Dynamics of the Electoral College
Authors: Blake William D.
Published: September 2019

This paper explores the racial origins and legacy of the Electoral College through historical and quantitative analyses. At the Constitutional Convention, the Electoral College served the interests of Southern slaveowners by perpetuating the advantage of the Three-Fifths Compromise. Following Reconstruction, Southern states that disenfranchised African Americans received an even larger voice in the Electoral College. Republicans tried to counter these moves by admitting unusually small states into the union. The ironic consequence of this non-racial decision is that, today, these states are heavily White and have citizens with higher levels of racial resentment. A MM-regression analysis of every election from 2000 on indicates the Electoral College has consistently awarded more votes per capita to states with Whiter populations and more racially conservative attitudes. The racially-disparate power exerted by these states makes it more difficult to enact racially-egalitarian policies. This paper adds a new perspective to the normative debate over the Electoral College, which typically focuses on democratic fairness and federalism concerns.
Some remarks on products of sets in the Heisenberg group and in the affine group
Authors: Shkredov Ilya D.
Published: September 2019

We obtain some new results about products of large and small sets in the Heisenberg group as well as in the affine group over the prime field.
We apply these growth results to Freiman’s isomorphism in nonabelian groups.
Confronting the impact of teen pregnancy in Mississippi: the need for after-school programs
Authors: Bogan Danielle R. , Aranmolate Rasaki , Mawson Anthony R.
Published: September 2019

Although rates have decreased 8% since 2014, teen pregnancy remains a substantial public health and social problem in Mississippi and elsewhere in the US. Evidence suggests that, among teenagers, the after-school hours from 3 to 6 pm are peak times of risky sexual activity. This paper reviews recent research and programs concerning sexual risk behavior among adolescents and proposes that after-school programs would increase the daily period of supervised activity and thereby reduce risks not only of pregnancy and sexually transmitted diseases but would also serve to enhance scholastic attainment and hence career prospects for the future. After-school programs from 3 to 6 p.m. should incorporate a multifaceted model that includes academic enhancement and recreational activities as well as curriculum-based sex education, youth development, and service learning.
Sparing the control arm using well-characterized diagnostic approaches – the Gart and Buck prevalence estimator for efficacy estimation in single-arm trials
Authors: Hahn Andreas , Loderstädt Ulrike , Frickmann Hagen , Schwarz Norbert G.
Published: September 2019

Efficacy estimation of medical interventions in clinical trials requires diagnostics to assess the study endpoint. The imperfect nature of diagnostics often leads to biased efficacy estimations, usually underestimation, that could result in failure of clinical trials. Adjustment methods can be used if sensitivity and specificity are known, but they are not regularly applied. Double-arm clinical trials are the standard for demonstrating the superiority of a medical intervention. However, sometimes single-arm trials are the only option: for example, if a parallel group trial design with a control group is regarded as unethical. Based on the Gart and Buck prevalence estimator (aka the Rogan-Gladen estimator), we demonstrate how the concept of diagnostic accuracy-adjusted effect estimation can be used for estimating the efficacy in single-arm trials. The accuracy of the diagnostic measure (sensitivity and specificity) used for endpoint assessment as well as the predictive accuracy of a predictor and its allocation among the study population have to be known to obtain consistent efficacy estimation of a medical intervention. If double-arm trials are not feasible, an approach such as that described here can provide evidence regarding the efficacy of a medical intervention based on a single-arm trial.
Crystal structure of catena-poly[tri(4-chlorophenyl)-(μ2-hydroxido)tin(IV)] – 2-propanol (1/1), C21H21Cl3O2Sn
Authors: Lo Kong Mun , Lee See Mun , Tiekink Edward R.T.
Published: September 2019

C21H21Cl3O2Sn, monoclinic, P21/n (no. 14), a = 13.3360(1) Å, b = 8.2332(1) Å, c = 20.2443(2) Å, β = 104.070(1)°, V = 2156.10(4) Å3, Z = 4, R
gt(F) = 0.0199, wR
ref(F
2) = 0.0544, T = 100(2) K.