bluuebunny/crossref_metadata_2025 huggingface dataset:
Importance of establishment of corporate culture in libraries in the development of information services
Authors: Bayter Mustafa
Published: September 2019

Institutions carry the reflections of the culture in which they are formed. Therefore, each institution is influenced by its cultural structure in order to create its own culture. This corporate culture consists of values, beliefs and norms that hold the members of the institution together. Libraries are the institutions with the most important sources of information. The longer the corporate culture is established within this organization, the more effective the access to information and communication with the user will be. In this context, the subject of our study, the definition and history of corporate culture, the formation of corporate culture, the place and importance of culture in the institution, the elements of corporate culture, corporate culture and corporate change, corporate culture and management, the effect of managers in the formation of corporate culture, corporate culture and human resources, corporate culture culture and institutional commitment, the formation of institutional culture in the library institution, library and institutional behavior, library and institutional culture, the effect of the library on the formation of corporate culture. ​Extended English summary is in the end of Full Text PDF (TURKISH) file. Özet Kurumlar, içinde oluştukları kültürün yansımalarını taşırlar. Bu yüzden her kurum kendi kültürünü oluşturabilmek için içinde bulunduğu kültür yapısından etkilenmektedir. Kurumsal kültür, kurum üyelerini bir arada tutan değerler, inanç, normlardan meydana gelmektedir. Kütüphaneler bilgi aktarımını sağlayan en önemli bilgi kaynaklarına sahip kurumlardır. Kütüphane kurumu içerisinde kurum kültürü ne kadar iyi oluşursa bilgiye ulaşım ve kullanıcı ile olan iletişim o kadar etkili olacaktır. Bu  çalışmada bilgi hizmetlerinin gelişmesinde kütüphanelerde kurum kültürünün oluşmasının önemi, kurum kültürünün tanımı ve tarihçesi, kurumsal kültürün oluşması, kültürün kurumdaki yeri ve önemi, kurum kültürünü oluşturan unsurlar,  kurum kültürü ve kurumsal değişim, kurum kültürü ve yönetim, kurum kültürünün oluşmasında yöneticilerin etkisi, kurum kültürü ve insan kaynakları, kurum kültürü ve kurumsal bağlılık, kütüphane kurumunda kurumsal kültürün oluşumu, kütüphane ve kurumsal davranış, kütüphane ve kurum kültürü, kütüphanenin kurum kültürü oluşturmasındaki etkisi başlıkları altında incelenmeye çalışılmıştır.
DimmStore
Authors: Karyakin Alexey , Salem Kenneth
Published: September 2019

Memory can consume a substantial amount of power in database servers, yet memory power has received considerably less attention than CPU power. Memory power consumption is also highly non-proportional. Thus, memory power becomes even more significant in the common case in which a database server is either not completely busy or not completely full. In this paper, we study the application of two memory power optimization techniques - rank-aware allocation and rate-based layout - to database systems. By concentrating memory load, rather than spreading it out evenly, these techniques create and exploit memory idleness to achieve power savings. We have implemented these techniques in a prototype database system called DimmStore. DimmStore is part of a memory power testbed which includes customized hardware with direct power measurement capabilities, allowing us to measure the techniques' effectiveness. We use the testbed to empirically characterize the power saving opportunities provided by these techniques, as well as their performance impact, under YCSB and TPC-C workloads. Under simple YCSB workloads, power savings ranged up to 50%, depending on load and space utilization, with little performance impact. Savings were smaller, but still significant, for TPC-C, which has more complex data locality characteristics.
Querying shortest paths on time dependent road networks
Authors: Wang Yong , Li Guoliang , Tang Nan
Published: September 2019

For real-world
time dependent road networks
(TDRNs), answering shortest path-based route queries and plans in real-time is highly desirable by many industrial applications. Unfortunately, traditional (
Dijkstra
- or
A
*-like) algorithms are computationally expensive for such tasks on TDRNs.

Naturally, indexes are needed to meet the real-time constraint required by real applications. In this paper, we propose a novel height-balanced tree-structured index, called TD-G-tree, which supports fast route queries over TDRNs. The key idea is to use hierarchical graph partitioning to split a road network into hierarchical partitions. This will produce a balanced tree, where each tree node corresponds to a partition and each parent-child relationship corresponds to a partition and its sub-partition. We then compute and index
time dependent shortest paths
(TDSPs) only for
borders
(
i.e
., vertices whose edges are cut by a partition). Based on TD-G-tree, we devise efficient algorithms to support TDSP queries, as well as time-interval based route planning, for computing optimal solutions through dynamic programming and chronological divide-and-conquer. Extensive experiments on real-world datasets show that our method significantly outperforms existing approaches.
PrivateSQL
Authors: Kotsogiannis Ios , Tao Yuchao , He Xi , Fanaeepour Maryam , Machanavajjhala Ashwin , Hay Michael , Miklau Gerome
Published: September 2019

Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific types of relational data (e.g. graphs), there isn't a general privacy definition for multi-relational schemas with constraints, and no system that permits accurate differentially private answering of SQL queries while imposing a fixed privacy budget across all queries posed by the analyst.

This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. PrivateSQL allows an analyst to query data stored in a standard database management system using a rich class of SQL counting queries. PrivateSQL adopts a novel generalization of differential privacy to multi-relational data that takes into account constraints in the schema like foreign keys, and allows the data owner to flexibly specify entities in the schema that need privacy. PrivateSQL ensures a fixed privacy loss across all the queries posed by the analyst by answering queries on private synopses generated from several views over the base relation that are tuned to have low error on a representative query workload. We experimentally evaluate PrivateSQL on a real-world dataset and a workload of more than 3, 600 queries. We show that for 50% of the queries PrivateSQL offers at least 1, 000x better error rates than solutions adapted from prior work.
CAPER
Authors: Amiri Mohammad Javad , Agrawal Divyakant , Abbadi Amr El
Published: September 2019

Despite recent intensive research, existing blockchain systems do not adequately address all the characteristics of distributed applications. In particular, distributed applications collaborate with each other following service level agreements (SLAs) to provide different services. While collaboration between applications, e.g., cross-application transactions, should be
visible
to all applications, the internal data of each application, e.g, internal transactions, might be
confidential
. In this paper, we introduce
CAPER
, a permissioned blockchain system to support both internal and cross-application transactions of collaborating distributed applications. In CAPER, the blockchain ledger is formed as a
directed acyclic graph
where each application accesses and maintains only its own
view
of the ledger including its internal and all cross-application transactions. CAPER also introduces three consensus protocols to globally order cross-application transactions between applications with different internal consensus protocols. The experimental results reveal the efficiency of CAPER in terms of performance and scalability.
Rethinking database high availability with RDMA networks
Authors: Zamanian Erfan , Yu Xiangyao , Stonebraker Michael , Kraska Tim
Published: September 2019

Highly available database systems rely on data replication to tolerate machine failures. Both classes of existing replication algorithms, active-passive and active-active, were designed in a time when network was the dominant performance bottleneck. In essence, these techniques aim to minimize network communication between replicas at the cost of incurring more processing redundancy; a trade-off that suitably fitted the conventional wisdom of distributed database design. However, the emergence of next-generation networks with high throughput and low latency calls for revisiting these assumptions.

In this paper, we first make the case that in modern RDMA-enabled networks, the bottleneck has shifted to CPUs, and therefore the existing network-optimized replication techniques are no longer optimal. We present
Active-Memory Replication
, a new high availability scheme that efficiently leverages RDMA to completely eliminate the processing redundancy in replication. Using Active-Memory, all replicas dedicate their processing power to executing new transactions, as opposed to performing redundant computation. Active-Memory maintains high availability and correctness in the presence of failures through an efficient RDMA-based undo-logging scheme. Our evaluation against active-passive and active-active schemes shows that Active-Memory is up to a factor of 2 faster than the second-best protocol on RDMA-based networks.
Efficient knowledge graph accuracy evaluation
Authors: Gao Junyang , Li Xian , Xu Yifan Ethan , Sisman Bunyamin , Dong Xin Luna , Yang Jun
Published: September 2019

Estimation of the accuracy of a large-scale knowledge graph (KG) often requires humans to annotate samples from the graph. How to obtain statistically meaningful estimates for accuracy evaluation while keeping human annotation costs low is a problem critical to the development cycle of a KG and its practical applications. Surprisingly, this challenging problem has largely been ignored in prior research. To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts. Motivated by the properties of the annotation cost function observed in practice, we propose the use of cluster sampling to reduce the overall cost. We further apply weighted and two-stage sampling as well as stratification for better sampling designs. We also extend our framework to enable efficient incremental evaluation on evolving KG, introducing two solutions based on stratified sampling and a weighted variant of reservoir sampling. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed solution. Compared to baseline approaches, our best solutions can provide up to 60% cost reduction on static KG evaluation and up to 80% cost reduction on evolving KG evaluation, without loss of evaluation quality.
Efficient algorithms for densest subgraph discovery
Authors: Fang Yixiang , Yu Kaiqiang , Cheng Reynold , Lakshmanan Laks V. S. , Lin Xuemin
Published: September 2019

Densest subgraph discovery
(DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph
G
, DSD aims to find a subgraph
D
of
G
with the highest density (e.g., the number of edges over the number of vertices in
D
). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that the densest subgraph can be accurately found through a
k
-core (a kind of dense subgraph of
G
), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based- and general pattern-based density. We have performed extensive experimental evaluation on both real and synthetic datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.
Plan-structured deep neural network models for query performance prediction
Authors: Marcus Ryan , Papaemmanouil Olga
Published: September 2019

Query performance prediction, the task of predicting a query's latency prior to execution, is a challenging problem in database management systems. Existing approaches rely on features and performance models engineered by human experts, but often fail to capture the complex interactions between query operators and input relations, and generally do not adapt naturally to workload characteristics and patterns in query execution plans. In this paper, we argue that
deep learning
can be applied to the query performance prediction problem, and we introduce a novel neural network architecture for the task: a
plan-structured neural network
. Our neural network architecture matches the structure of any optimizer-selected query execution plan and predict its latency with high accuracy, while eliminating the need for human-crafted input features. A number of optimizations are also proposed to reduce training overhead without sacrificing effectiveness. We evaluated our techniques on various workloads and we demonstrate that our approach can out-perform the state-of-the-art in query performance prediction.
GALO
Authors: Damasio Guilherme , Bryson Spencer , Corvinelli Vincent , Godfrey Parke , Mierzejewski Piotr , Szlichta Jaroslaw , Zuzarte Calisto
Published: September 2019

Query performance problem determination is usually performed manually in consultation with experts through the analysis of query plans. However, this is an excessively time consuming, human error-prone, and costly process. GALO is a novel system that automates this process. The tool automatically learns recurring problem patterns in query plans over workloads in an offline learning phase to build a knowledge base of plan rewrite remedies. GALO's knowledge base is built on RDF and SPARQL, which is well-suited for manipulating and querying over SQL query plans, which are graphs themselves. It then uses the knowledge base online to re-optimize queries queued for execution to improve performance, often quite dramatically.
Synergistic graph and SQL analytics inside IBM Db2
Authors: Tian Yuanyuan , Sun Wen , Tong Sui Jun , Xu En Liang , Pirahesh Mir Hamid , Zhao Wei
Published: September 2019

To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of specialized graph databases have emerged, such as Neo4j, JanusGraph, and Sqlg. At the same time, RDBMSs and SQL continue to support mission-critical business analytics. However, real-life analytical applications seldom contain only one type of analytics. They are often made of
heterogeneous
workloads, including SQL, machine learning, graph, and other analytics. In particular, SQL and graph analytics are usually accompanied together in one analytical workload. This means that graph and SQL analytics need to be
synergistic
with each other. Unfortunately, most existing graph databases are standalone and cannot easily integrate with relational databases. In addition, as a matter of fact, many graph data (data about relationships between objects or people) are already prevalent in relational databases, although they are not explicitly stored as graphs. Performing graph analytics on these
relational
graph data today requires exporting large amount of data to the specialized graph databases. A natural question arises: can SQL and graph analytics be performed synergistically in a same system? In this demo, we present such a working system called IBM Db2 Graph. Db2 Graph is an in-DBMS graph query approach. It is implemented as a layer inside an experimental IBM Db2™, and thus can support synergistic graph and SQL analytics efficiently. Db2 Graph employs a
graph overlay
approach to expose a
graph view
of the relational data. This approach flexibly retrofits graph queries to existing graph data stored in relational tables. We use an example scenario on health insurance claim analysis to demonstrate how Db2 Graph is used to support synergistic graph and SQL analytics inside Db2.
Stateful functions as a service in action
Authors: Akhter Adil , Fragkoulis Marios , Katsifodimos Asterios
Published: September 2019

In the
serverless
model, users upload application code to a cloud platform and the cloud provider undertakes the deployment, execution and scaling of the application, relieving users from all operational aspects. Although very popular, current serverless offerings offer poor support for the management of local application
state
, the main reason being that managing state and keeping it consistent at large scale is very challenging. As a result, the serverless model is inadequate for executing stateful, latency-sensitive applications. In this paper we present a high-level programming model for developing stateful functions and deploying them in the cloud. Our programming model allows functions to retain state as well as call other functions. In order to deploy stateful functions in a cloud infrastructure, we translate functions and their data exchanges into a stateful dataflow graph. With this paper we aim at demonstrating that using a modified version of an open-source dataflow engine as a runtime for stateful functions, we can deploy scalable and stateful services in the cloud with surprisingly low latency and high throughput.
Data lake management
Authors: Nargesian Fatemeh , Zhu Erkang , Miller Renée J. , Pu Ken Q. , Arocena Patricia C.
Published: September 2019

The ubiquity of data lakes has created fascinating new challenges for data management research. In this tutorial, we review the state-of-the-art in data management for data lakes. We consider how data lakes are introducing new problems including dataset discovery and how they are changing the requirements for classic problems including data extraction, data cleaning, data integration, data versioning, and metadata management.
Guided automated learning for query workload re-optimization
Authors: Damasio Guilherme , Corvinelli Vincent , Godfrey Parke , Mierzejewski Piotr , Mihaylov Alex , Szlichta Jaroslaw , Zuzarte Calisto
Published: September 2019

Query optimization is a hallmark of database systems. When an SQL query runs more expensively than is viable or warranted, determination of the performance issues is usually performed manually in consultation with experts through the analysis of query's execution plan (QEP). However, this is an excessively time consuming, human error-prone, and costly process. GALO is a novel system that automates this process. The tool automatically learns recurring problem patterns in query plans over workloads in an offline learning phase, to build a knowledge base of plan-rewrite remedies. It then uses the knowledge base online to re-optimize queries often quite drastically.

GALO's knowledge base is built on RDF and SPARQL, W3C graph database standards, which is well suited for manipulating and querying over SQL query plans, which are graphs themselves. GALO acts as a third-tier of re-optimization, after query rewrite and cost-based optimization, as a
query plan rewrite
. For generality, the context of knowledge base problem patterns, including table and column names, is abstracted with canonical symbol labels. Since the knowledge base is not tied to the context of supplied QEPs, table and column names are matched automatically during the re-optimization phase. Thus, problem patterns learned over a particular query workload can be applied in other query workloads. GALO's knowledge base is also an invaluable tool for database experts to debug query performance issues by tracking to known issues and solutions as well as refining the optimizer with new tuned techniques by the development team. We demonstrate an experimental study of the effectiveness of our techniques over synthetic TPC-DS and real IBM client query workloads.
AnalyticDB
Authors: Zhan Chaoqun , Su Maomeng , Wei Chuangxian , Peng Xiaoqiang , Lin Liang , Wang Sheng , Chen Zhe , Li Feifei , Pan Yue , Zheng Fang , Chai Chengliang
Published: September 2019

With data explosion in scale and variety, OLAP databases play an increasingly important role in serving real-time analysis with low latency (e.g., hundreds of milliseconds), especially when incoming queries are complex and ad hoc in nature. Moreover, these systems are expected to provide high query concurrency and write throughput, and support queries over structured and complex data types (e.g., JSON, vector and texts).

In this paper, we introduce AnalyticDB, a real-time OLAP database system developed at Alibaba. AnalyticDB maintains all-column indexes in an asynchronous manner with acceptable overhead, which provides low latency for complex ad-hoc queries. Its storage engine extends hybrid row-column layout for fast retrieval of both structured data and data of complex types. To handle large-scale data with high query concurrency and write throughput, AnalyticDB decouples read and write access paths. To further reduce query latency, novel storage-aware SQL optimizer and execution engine are developed to fully utilize the advantages of the underlying storage and indexes. AnalyticDB has been successfully deployed on Alibaba Cloud to serve numerous customers (both large and small). It is capable of holding 100 trillion rows of records, i.e., 10PB+ in size. At the same time, it is able to serve 10m+ writes and 100k+ queries per second, while completing complex queries within hundreds of milliseconds.
Tunable consistency in MongoDB
Authors: Schultz William , Avitabile Tess , Cabral Alyson
Published: September 2019

Distributed databases offer high availability but can impose high costs on client applications in order to maintain strong consistency at all times. MongoDB is a document oriented database whose replication system provides a variety of consistency levels allowing client applications to select the trade-offs they want to make when it comes to consistency and latency, at a per operation level. In this paper we discuss the tunable consistency models in MongoDB replication and their utility for application developers. We discuss how the MongoDB replication system's speculative execution model and data rollback protocol help make this spectrum of consistency levels possible. We also present case studies of how these consistency levels are used in real world applications, along with a characterization of their performance benefits and trade-offs.
AliGraph
Authors: Zhu Rong , Zhao Kun , Yang Hongxia , Lin Wei , Zhou Chang , Ai Baole , Li Yong , Zhou Jingren
Published: September 2019

An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements. Graph Neural Network (GNN) becomes an effective way to address the graph learning problem by converting the graph data into a low dimensional space while keeping both the structural and property information to the maximum extent and constructing a neural network for training and referencing. However, it is challenging to provide an efficient graph storage and computation capabilities to facilitate GNN training and enable development of new GNN algorithms. In this paper, we present a comprehensive graph neural network system, namely
AliGraph
, which consists of distributed graph storage, optimized sampling operators and runtime to efficiently support not only existing popular GNNs but also a series of in-house developed ones for different scenarios. The system is currently deployed at Alibaba to support a variety of business scenarios, including product recommendation and personalized search at Alibaba's E-Commerce platform. By conducting extensive experiments on a real-world dataset with 492.90 million vertices, 6.82 billion edges and rich attributes,
AliGraph
performs an order of magnitude faster in terms of graph building (5 minutes vs hours reported from the state-of-the-art PowerGraph platform). At training,
AliGraph
runs 40%-50% faster with the novel caching strategy and demonstrates around 12 times speed up with the improved runtime. In addition, our in-house developed GNN models all showcase their statistically significant superiorities in terms of both effectiveness and efficiency (e.g., 4.12%--17.19% lift by F1 scores).
QTune
Authors: Li Guoliang , Zhou Xuanhe , Li Shifu , Gao Bo
Published: September 2019

Database knob tuning is important to achieve high performance (e.g., high throughput and low latency). However, knob tuning is an NP-hard problem and existing methods have several limitations. First, DBAs cannot tune a lot of database instances on different environments (e.g., different database vendors). Second, traditional machine-learning methods either cannot find good configurations or rely on a lot of high-quality training examples which are rather hard to obtain. Third, they only support coarse-grained tuning (e.g., workload-level tuning) but cannot provide fine-grained tuning (e.g., query-level tuning).

To address these problems, we propose a query-aware database tuning system QTune with a deep reinforcement learning (DRL) model, which can efficiently and effectively tune the database configurations. QTune first featurizes the SQL queries by considering rich features of the SQL queries. Then QTune feeds the query features into the DRL model to choose suitable configurations. We propose a Double-State Deep Deterministic Policy Gradient (DS-DDPG) model to enable query-aware database configuration tuning, which utilizes the actor-critic networks to tune the database configurations based on both the query vector and database states. QTune provides three database tuning granularities: query-level, workload-level, and cluster-level tuning. We deployed our techniques onto three real database systems, and experimental results show that QTune achieves high performance and outperforms the state-of-the-art tuning methods.
S3
Authors: Zhang Jingtian , Wu Sai , Tan Zeyuan , Chen Gang , Cheng Zhushi , Cao Wei , Gao Yusong , Feng Xiaojie
Published: September 2019

Many new memory indexing structures have been proposed and outperform current in-memory skip-list index adopted by LevelDB, RocksDB and other key-value systems. However, those new indexes cannot be easily intergrated with key-value systems, because most of them do not consider how the data can be efficiently flushed to disk. Some assumptions, such as fixed size key and value, are unrealistic for real applications. In this paper, we present S3, a scalable in-memory skip-list index for the customized version of RocksDB in Alibaba Cloud. S3 adopts a two-layer structure. In the top layer, a cache-sensitive structure is used to maintain a few guard entries to facilitate the search over the skip-list. In the bottom layer, a semi-ordered skip-list index is built to support highly concurrent insertions and fast lookup and range query. To further improve the performance, we train a neural model to select guard entries intelligently according to the data distribution and query distribution. Experiments on multiple datasets show that S3 achieves a comparable performance to other new memory indexing schemes, and can replace current in-memory skip-list of LevelDB and RocksDB to support huge volume of data.
Updating graph databases with Cypher
Authors: Green Alastair , Guagliardo Paolo , Libkin Leonid , Lindaaker Tobias , Marsault Victor , Plantikow Stefan , Schuster Martin , Selmer Petra , Voigt Hannes
Published: September 2019

The paper describes the present and the future of graph updates in Cypher, the language of the Neo4j property graph database and several other products. Update features include those with clear analogs in relational databases, as well as those that do not correspond to any relational operators. Moreover, unlike SQL, Cypher updates can be arbitrarily intertwined with querying clauses. After presenting the current state of update features, we point out their shortcomings, most notably violations of atomicity and non-deterministic behavior of updates. These have not been previously known in the Cypher community. We then describe the industry-academia collaboration on designing a revised set of Cypher update operations. Based on discovered shortcomings of update features, a number of possible solutions were devised. They were presented to key Cypher users, who were given the opportunity to comment on how update features are used in real life, and on their preferences for proposed fixes. As the result of the consultation, a new set of update operations for Cypher were designed. Those led to a streamlined syntax, and eliminated the unexpected and problematic behavior that original Cypher updates exhibited.
Cloud-native database systems at Alibaba
Authors: Li Feifei
Published: September 2019

Cloud-native databases become increasingly important for the era of cloud computing, due to the needs for elasticity and on-demand usage by various applications. These challenges from cloud applications present new opportunities for cloud-native databases that cannot be fully addressed by traditional on-premise enterprise database systems. A cloud-native database leverages software-hardware co-design to explore accelerations offered by new hardware such as RDMA, NVM, kernel bypassing protocols such as DPDK. Meanwhile, new design architectures, such as shared storage, enable a cloud-native database to decouple computation from storage and provide excellent elasticity. For highly concurrent workloads that require horizontal scalability, a cloud-native database can leverage a shared-nothing layer to provide distributed query and transaction processing. Applications also require cloud-native databases to offer high availability through distributed consensus protocols.

At Alibaba, we have explored a suite of technologies to design cloud-native database systems. Our storage engine, X-Engine and PolarFS, improves both write and read throughputs by using a LSM-tree design and self-adapted separation of hot and cold data records. Based on these efforts, we have designed and implemented POLARDB and its distributed version POLARDB-X, which has successfully supported the extreme transaction workloads during the 2018 Global Shopping Festival on November 11, 2018, and achieved commercial success on Alibaba Cloud. We have also designed an OLAP system called AnalyticDB (ADB in short) for enabling real-time interactive data analytics for big data. We have explored a self-driving database platform to achieve autoscaling and intelligent database management. We will report key technologies and lessons learned to highlight the technical challenges and opportunities for cloud-native database systems at Alibaba.
Opportunities for data management research in the era of horizontal AI/ML
Authors: Rekatsinas Theodoras , Roy Sudeepa , Vartak Manasi , Zhang Ce , Polyzotis Neoklis
Published: September 2019

AI/ML is becoming a horizontal technology: its application is expanding to more domains, and its integration touches more parts of the technology stack. Given the strong dependence of ML on data, this expansion creates a new space for applying data management techniques. At the same time, the deeper integration of ML in the technology stack provides more touch points where ML can be used in data management systems and vice versa.

In this panel, we invite researchers working in this domain to discuss this emerging world and its implications on data-management research. Among other topics, the discussion will touch on the opportunities for interesting research, how we can interact with other communities, what is the core expertise we bring to the table, and how we can conduct and evaluate this research effectively within our own community. The goal of the panel is to nudge the community to appreciate the opportunities in this new world of horizontal AI/ML and to spur a discussion on how we can shape an effective research agenda.
University Program Characteristics and Education-Job Mismatch
Authors: Kucel Aleksander , Vilalta-Bufí Montserrat
Published: September 2019

Over-education has been demonstrated to be frequent and persistent across countries. It often goes together with working in a job not related to the field of study (horizontal mismatch) or in a job that requires lower skills than acquired (skill mismatch). We study which program characteristics help university graduates to obtain a good job match. We do the analysis for Spain since the presence of over-education is strong in this country. We analyze the three types of mismatch: over-education, horizontal mismatch, and over-skills. We focus on the role of program characteristics in avoiding over-education in the first job after graduation, and in exiting over-education in the early career. We find that those programs that are academically prestigious and those that promote entrepreneurial skills help avoid being mismatched in the first job and, in case of being mismatched in the first job, they help exit this situation. Overall, our results give support to policies promoting the development of entrepreneurial skills in the Spanish education system.
On the description of multidimensional normal Hausdorff operators on Lebesgue spaces
Authors: Mirotin Adolf R.
Published: September 2019

Hausdorff operators originated from some classical summation methods.
Now this is an active research field.
In the present article, a spectral representation for multidimensional normal Hausdorff operator is given.
We show that normal Hausdorff operator in

L
2

⁢

(

ℝ
n

)

{L^{2}(\mathbb{R}^{n})}

is unitary equivalent to the operator of multiplication by some matrix-valued function (its matrix symbol) in the space

L
2

⁢

(

ℝ
n

;

ℂ

2
n

)

{L^{2}(\mathbb{R}^{n};\mathbb{C}^{2^{n}})}

.
Several corollaries that show that properties of a Hausdorff operator are closely related to the properties of its symbol are considered.
In particular, the norm and the spectrum of such operators are described in terms of the symbol.
Phytotherapy in endometriosis: an up-to-date review
Authors: Della Corte Luigi , Noventa Marco , Ciebiera Michal , Magliarditi Maria , Sleiman Zaki , Karaman Erbil , Catena Ursula , Salvaggio Calogero , Falzone Giovanni , Garzon Simone
Published: September 2019

Endometriosis is a benign gynecological disease which symptoms can provide a severe impact on patient’s quality of life with subsequent impact on psychological well-being. Different therapeutic strategies are available to treat this disease, such as surgery, hormonal therapies, and nonsteroidal anti-inflammatory drugs. Nevertheless, the efficacy of conventional medical treatments is limited or intermittent in most of the patients due to the associated side effects. Therefore, a woman with endometriosis often search for additional and alternative options, and phytotherapy might be a promising alternative and complementary strategy. Different medicinal plants, multicomponent herbal preparations, and phytochemicals were investigated for pharmacological proprieties in endometriosis therapy. In most of the cases, the effect on endometriosis was related to phenolic compounds, such as flavonoids and phenolic acids reporting anti-inflammatory, proapoptotic, antioxidant, and immunomodulatory functions. Moreover, some phytochemicals have been related to a strong phytoestrogenic effect modulating the estrogen activity. Although promising, available evidence is based on in vitro and animal models of endometriosis with a limited number of well-performed clinical studies. There are almost none randomized control trials in this area. Therefore, properly constructed clinical trials are mandatory to achieve more conclusive results about the promising role of phytotherapy in the management of endometriosis.
Stochastic Novikov Engine with Fourier Heat Transport
Authors: Schwalbe Karsten , Hoffmann Karl Heinz
Published: September 2019

The Stochastic Novikov engine is an endoreversible model for heat engines where the heat supply takes place at a fluctuating temperature. These fluctuations can be observed for example at solar thermal power plants. While recently the influence of the temperature fluctuations on the engine’s performance has been studied for Newtonian heat transport, the relation between the used heat transport type and the performance measures remained open. Therefore, we here consider a Stochastic Novikov engine with Fourier heat transport. Based on a short summary of the concept of a Stochastic Novikov engine and the corresponding different control types, the maximum work output and the corresponding efficiency are derived. In particular, we discuss the influence of the distribution’s parameters on the engine’s performance assuming a uniform temperature distribution. We find that the heat transport type has a significant effect on some of the engine’s fundamental properties.
Temporal aggregation of random walk processes and implications for economic analysis
Authors: Ahmad Yamin S , Paya Ivan
Published: September 2019

This paper examines the impact of time averaging and interval sampling data assuming that the data generating process for a given series follows a random walk with iid errors. We provide exact expressions for the corresponding variances, and covariances, for both levels and higher order differences of the aggregated series, as well as that for the variance ratio, demonstrating exactly how the degree of temporal aggregation impacts these properties. We empirically investigate this issue on exchange rates and find that the values of the variance ratios and autocorrelation coefficients at different frequencies are consistent with our theoretical results. We also conduct a simulation exercise that illustrates the potential effect that conditional heteroskedasticity and fat tails may have on the temporal aggregation of a random walk and of a highly persistent autoregressive process.
Pregnancy in Patients With Low Back Pain
Authors: Seddighi Afsoun , Seddighi Amir Saied , Jamshidi Shiva , Rahimi Baghdashti Hesam
Published: September 2019

Pregnancy-related lumbopelvic pain has been a serious and common problem since ancient time. The present review article focuses on terminology, types, clinical presentation, and management of these problems. There are two entities regarding pregnancy-related lumbopelvic pain: pelvic girdle pain (PGP), and pregnancy-related low back pain (LBP). There are multiple mechanisms behind these disabilities. Age, multiparty, heavy weight lifting, hard physical activity, previous LBP, and low education increase the prevalence. About one-half of women with pregnancy-related lumbopelvic pain have PGP, one-third LBP, and one-sixth have both conditions. Overall, the literature reveals that PGP deserves serious attention from the clinical and economic standpoints.
Transcranial Direct Current Stimulation Combining Mindfulness-Based Relapse Prevention for Smoking Cessation: A Case Report
Authors: Khayyer Zahra , Saberi Azad Razieh , Dadjoo Mohsen , Shojaei Azam
Published: September 2019

Previous electroencephalography (EEG) researches have shown significant differences in terms of abnormal brain activity among smokers and non-smokers. Typically, EEG analysis showed decreased slow waves and increased fast waves in people with nicotine consumption. The present study tested a transcranial direct current stimulation (tDCS) technique combined with Mindfulness-based relapse prevention (MBRP) to reduce nicotine cravings. Specifically, cathodal tDCS over left prefrontal cortex (PFC), as well as anodal stimulation over the right central zone, tested. An 18-year-old right-handed male with almost 3 years of smoking participated. He had no anti-tobacco medical prescription. The participant received twelve sessions of tDCS treatment (2 times a week). Anodal stimulation (1.5 mA, 15 minutes) performed on C3 (LORETA source localization) and cathodal stimulation on FP2. Also, the participant received 30-minute MBRP-based psychotherapy sessions in order to ameliorate aggression, stress, and craving. Clinical evaluations, Buss-Perry Aggression Questionnaire (AGQ), Fagerstrom Test for Nicotine Dependence (FTND), Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1994), and daily checklist for the number of smoked cigarettes administered along with carbon monoxide (CO) levels. Also, subjective measures of craving intensity between zero and ten measured after treatment and 3-month follow-up. After treatment, the participant showed a substantial reduction in all clinical tests, and the number of cigarettes decreased from 30 to 2 a day. From a psycho neurotherapeutic perspective, the present study provides preliminary indications for a novel and efficient treatment of smoking during adolescence. Further studies are needed to determine generalizability and overall efficiency.
Multidimensional Neural Selectivity in the Primate Amygdala
Authors: Putnam Philip T. , Gothard Katalin M.
Published: September 2019

The amygdala contributes to multiple functions including attention allocation, sensory processing, decision-making, and the elaboration of emotional behaviors. The diversity of functions attributed to the amygdala is reflected in the response selectivity of its component neurons. Previous work claimed that subsets of neurons differentiate between broad categories of stimuli (e.g., objects vs faces, rewards vs punishment), while other subsets are narrowly specialized to respond to individual faces or facial features (e.g., eyes). Here we explored the extent to which the same neurons contribute to more than one neural subpopulation in a task that activated multiple functions of the amygdala. The subjects (Macaca mulatta) watched videos depicting conspecifics or inanimate objects, and learned by trial and error to choose the individuals or objects associated with the highest rewards. We found that the same neurons responded selectively to two or more of the following task events or stimulus features: (1) alerting, task-related stimuli (fixation icon, video start, and video end); (2) reward magnitude; (3) stimulus categories (social vs nonsocial); and (4) stimulus-unique features (faces, eyes). A disproportionate number of neurons showed selectivity for all of the examined stimulus features and task events. These results suggest that neurons that appear specialized and uniquely tuned to specific stimuli (e.g., face cells, eye cells) are likely to respond to multiple other types of stimuli or behavioral events, if/when these become behaviorally relevant in the context of a complex task. This multidimensional selectivity supports a flexible, context-dependent evaluation of inputs and subsequent decision making based on the activity of the same neural ensemble.
An Open-Source, Automated Home-Cage Sipper Device for Monitoring Liquid Ingestive Behavior in Rodents
Authors: Godynyuk Elizabeth , Bluitt Maya N. , Tooley Jessica R. , Kravitz Alexxai V. , Creed Meaghan C.
Published: September 2019

Measuring ingestive behavior of liquids in rodents is commonly used in studies of reward, metabolism, and circadian biology. Common approaches for measuring liquid intake in real time include computer-tethered lickometers or video-based systems. Additionally, liquids can be measured or weighed to determine the amount consumed without real-time sensing. Here, we built a photobeam-based sipper device that has the following advantages over traditional methods: (1) it is battery powered and fits in vivarium caging to allow home-cage measurements; (2) it quantifies the intake of two different liquids simultaneously for preference studies; (3) it is low cost and easily constructed, enabling high-throughput experiments; and (4) it is open source so that others can modify it to fit their experimental needs. We validated the performance of this device in three experiments. First, we calibrated our device using time-lapse video-based measurements of liquid intake and correlated sipper interactions with liquid intake. Second, we used the sipper device to measure preference for water versus chocolate milk, demonstrating its utility for two-bottle choice tasks. Third, we integrated the device with fiber photometry, establishing its utility for measuring neural activity in studies of ingestive behavior. This device requires no special equipment or caging, and is small, battery powered, and wireless, allowing it to be placed directly in rodent home cages. The total cost of fabrication is less than $100, and all design files and code are open source. Together, these factors greatly increase scalability and utility for a variety of behavioral neuroscience applications.
Coordination of Human Hippocampal Sharpwave Ripples during NREM Sleep with Cortical Theta Bursts, Spindles, Downstates, and Upstates
Authors: Jiang Xi , Gonzalez-Martinez Jorge , Halgren Eric
Published: September 2019

In rodents, waking firing patterns replay in NREM sleep during hippocampal sharpwave ripples (HC-SWRs), correlated with neocortical graphoelements (NC-GEs). NC-GEs include theta bursts, spindles, downstates, and upstates. In humans, consolidation during sleep is correlated with scalp-recorded spindles and downstates/upstates, but HC-SWRs cannot be recorded noninvasively. Here we show in humans of both sexes that HC-SWRs are highly correlated with NC-GEs during NREM, with significantly more related HC-SWRs/NC-GEs for downstates or upstates than theta bursts or spindles, in N2 than N3, in posterior than anterior HC, in frontal than occipital cortex, and ipsilaterally than contralaterally. The preferences interacted (e.g., frontal spindles co-occurred frequently with posterior HC-SWRs in N2). These preferred GEs, stages, and locations for HC-SWR/NC-GE interactions may index selective consolidation activity, although that was not tested in this study. SWR recorded in different HC regions seldom co-occurred, and were related to GE in different cortical areas, showing that HC-NC interact in multiple transient, widespread but discrete, networks. NC-GEs tend to occur with consistent temporal relationships to HC-SWRs, and to each other. Cortical theta bursts usually precede HC-SWRs, where they may help define cortical input triggering HC-SWR firing. HC-SWRs often follow cortical downstate onsets, surrounded by locally decreased broadband power, suggesting a mechanism synchronizing cortical, thalamic, and hippocampal activities. Widespread cortical upstates and spindles follow HC-SWRs, consistent with the hypothesized contribution by hippocampal firing during HC-SWRs to cortical firing-patterns during upstates and spindles. Overall, our results describe how hippocampal and cortical oscillations are coordinated in humans during events that are critical for memory consolidation in rodents.

SIGNIFICANCE STATEMENTHippocampal sharpwave ripples, essential for memory consolidation, mark when hippocampal neurons replay waking firing patterns. In rodents, cortical sleep waves coordinate the transfer of temporary hippocampal to permanent cortical memories, but their relationship with human hippocampal sharpwave ripples remains unclear. We show that human hippocampal sharpwave ripples co-occur with all varieties of cortical sleep waves, in all cortical regions, and in all stages of NREM sleep, but with overall preferences for each of these. We found that sharpwave ripples in different parts of the hippocampus usually occurred independently of each other, and preferentially interacted with different cortical areas. We found that sharpwave ripples typically occur after certain types of cortical waves, and before others, suggesting how the cortico-hippocampo-cortical interaction may be organized in time and space.