bluuebunny/crossref_metadata_2025 huggingface dataset:
Dutch Colonial Infrastructure Development in Takengon, 1904-1942
Authors: Iswanto Sufandi , Nurasiah Nurasiah , Kesuma T.  Bahagia
Published: October 2021

The events of the Gayo-Alas War that occurred between the Gayo-Alas people against the Dutch colonial marked the end of the Aceh War. This bloody conflict also marked the Gayo Plateau region, especially Takengon, which was occupied by the Dutch colonial authorities. Since then, Takengon, which was originally just an inland region of Aceh, has begun to grow, especially its infrastructure. Therefore, this study aims to examine the infrastructure that was built in the colonial era (1904-1942). The method used in this study is the historical method, which consisted of five analytical stages, namely topic selection, heuristics, leverage, interpretation, and historiography. The results of the study show that since the Dutch colonial came to the Gayo Takengon Plateau in 1904, Takengon was more developed comparing to previous period, all of which was inseparable from infrastructure development by the Dutch colonial. Some of the infrastructures were built successfully, there were the main road between Bireuen and Takengon, central government, Dutch and Tionghoa society, market, and Dutch school.  The development of infrastructure also has had an impact for Takengon's area, such as the new society along Bireuen-Takengon highway, and the construction of Takengon-Gayo Lues' road. In addition, the opening of the road was aimed at opening the Dutch plantations and the private sector has  made the emergence of the society's settlements, like in Redelong, Pondok Baru (Janarata), Lampahan and Isaq.
KDV-explorer
Authors: Chan Tsz Nam , Ip Pak Lon , U Leong Hou , Tong Weng Hou , Mittal Shivansh , Li Ye , Cheng Reynold
Published: October 2021

Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.
DeFiHap
Authors: Mao Yuetian , Yuan Shuai , Cui Nan , Du Tianjiao , Shen Beijun , Chen Yuting
Published: October 2021

The emergence of Hive greatly facilitates the management of massive data stored in various places. Meanwhile, data scientists face challenges during HiveQL programming - they may not use correct and/or efficient HiveQL statements in their programs; developers may also introduce anti-patterns indeliberately into HiveQL programs, leading to poor performance, low maintainability, and/or program crashes. This paper presents an empirical study on HiveQL programming, in which 38 HiveQL anti-patterns are revealed. We then design and implement DeFiHap, the first tool for automatically detecting and fixing HiveQL anti-patterns. DeFiHap detects HiveQL anti-patterns via analyzing the abstract syntax trees of HiveQL statements and Hive configurations, and generates fix suggestions by rule-based rewriting and performance tuning techniques. The experimental results show that DeFiHap is effective. In particular, DeFiHap detects 25 anti-patterns and generates fix suggestions for 17 of them.
Debugging missing answers for spark queries over nested data with breadcrumb
Authors: Diestelkämper Ralf , Lee Seokki , Glavic Boris , Herschel Melanie
Published: October 2021

We present Breadcrumb, a system that aids developers in debugging queries through query-based explanations for missing answers. Given as input a query and an expected, but missing, query result, Breadcrumb identifies operators in the input query that are responsible for the failure to derive the missing answer. These operators form explanations that guide developers who can then focus their debugging efforts on fixing these parts of the query. Breadcrumb is implemented on top of Apache Spark. Our approach is the first that scales to big data dimensions and is capable of finding explanations for common errors in queries over nested and de-normalized data, e.g., errors based on misinterpreting schema semantics.
Demonstration of dealer
Authors: Lin Qiongqiong , Zhang Jiayao , Liu Jinfei , Ren Kui , Lou Jian , Liu Junxu , Xiong Li , Pei Jian , Sun Jimeng
Published: October 2021

Data-driven machine learning (ML) has witnessed great success across a variety of application domains. Since ML model training relies on a large amount of data, there is a growing demand for high-quality data to be collected for ML model training. Data markets can be employed to significantly facilitate data collection. In this work, we demonstrate Dealer, an en
D
-to-end model m
a
rketp
l
ace with diff
e
rential p
r
ivacy. Dealer consists of three entities, data owners, the broker, and model buyers. Data owners receive compensation for their data usages allocated by the broker; The broker collects data from data owners, builds and sells models to model buyers; Model buyers buy their target models from the broker. We demonstrate the functionalities of the three participating entities and the abbreviated interactions between them. The demonstration allows the audience to understand and experience interactively the process of model trading. The audience can act as a data owner to control what and how the data would be compensated, can act as a broker to price machine learning models with maximum revenue, as well as can act as a model buyer to purchase target models that meet expectations.
From papers to practice
Authors: Müller Heiko , Castelo Sonia , Qazi Munaf , Freire Juliana
Published: October 2021

Data preparation is still a major bottleneck for many data science projects. Even though many sophisticated algorithms and tools have been proposed in the research literature, it is difficult for practitioners to integrate them into their data wrangling efforts. We present openclean, a open-source Python library for data cleaning and profiling, openclean integrates data profiling and cleaning tools in a single environment that is easy and intuitive to use. We designed openclean to be extensible and make it easy to add new functionality. By doing so, it will not only become easier for users to access state-of-the-art algorithms for their data wrangling efforts, but also allow researchers to integrate their work and evaluate its effectiveness in practice. We envision openclean as a first step to build a community of practitioners and researchers in the field. In our demo, we outline the main components and design decisions in the development of openclean and demonstrate the current functionality of the library on real-world use cases.
EPICGen
Authors: Anastasiou Chrysovalantis , Costa Constantinos , Chrysanthis Panos K. , Shahabi Cyrus
Published: October 2021

Effectively and accurately forecasting the congestion in indoor spaces has become particularly important during the pandemic in order to reduce the risk of exposure to airborne viruses. However, there is a lack of readily available indoor congestion data to train such models. Therefore, in this demo paper we propose
EPICGen
, an experimental platform for indoor congestion generation to support congestion forecasting in indoor spaces.
EPICGen
consists of two components: (i)
Grid Overlayer
, which models the floor plans of buildings; and (ii)
Congestion Generator
, a realistic indoor congestion generator. We demonstrate
EPICGen
through an intuitive map-based user interface that enables end-users to customize the parameters of the system and visualize generated datasets.
AnyOLAP
Authors: Schuhknecht Felix , Priesterroth Aaron , Henneberg Justus , Salkhordeh Reza
Published: October 2021

The volume of data that is processed and produced by modern data-intensive applications is constantly increasing. Of course, along with the volume, the interest in analyzing and interpreting this data increases as well. As a consequence, more and more DBMSs and processing frameworks are specialized towards the efficient execution of long-running, read-only analytical queries. Unfortunately, to enable analysis, the data first has to be moved from the source application to the analytics tool via a lengthy ETL process, which increases the runtime and complexity of the analysis pipeline.

In this work, we advocate to simply skip ETL altogether. With AnyOLAP, we can perform online analysis of data directly
within
the source application and
while
it is running. In the proposed demonstration, the audience will get the chance to put AnyOLAP to the test on a set of data-intensive applications that are supposed to be analyzed while they are up and running. As the entire analysis pipeline of AnyOLAP will be exposed to the audience in form of live and interactive visualizations, users will be able to experience the benefits of true online analysis firsthand.
How divergent is your data?
Authors: Pastor Eliana , Gavgavian Andrew , Baralis Elena , de Alfaro Luca
Published: October 2021

We present DivExplorer, a tool that enables users to explore datasets and find subgroups of data for which a classifier behaves in an anomalous manner. These subgroups, denoted as divergent subgroups, may exhibit, for example, higher-than-normal false positive or negative rates. DivExplorer can be used to analyze and debug classifiers. If the data has ethical or social implications, DivExplorer can be also used to identify bias in classifiers.
An extensible and reusable pipeline for automated utterance paraphrases
Authors: Berro Auday , Fard Mohammad-Ali Yaghub Zade , Baez Marcos , Benatallah Boualem , Benabdeslem Khalid
Published: October 2021

In this demonstration paper we showcase an extensible and reusable pipeline for automatic
paraphrase generation
, i.e., reformulating sentences using different words. Capturing the nuances of human language is fundamental to the effectiveness of Conversational AI systems, as it allows them to deal with the different ways users can utter their requests in natural language. Traditional approaches to utterance paraphrasing acquisition, such as hiring experts or crowd-sourcing, involve processes that are often costly or time consuming, and with their own trade-offs in terms of quality. Automatic paraphrasing is emerging as an attractive alternative that promises a fast, scalable and cost-effective process. In this paper we showcase how our extensible and reusable pipeline for automated utterance paraphrasing can support the development of Conversational AI systems by integrating and extending existing techniques under an unified and configurable framework.
Catch a blowfish alive
Authors: Liu Jiaxiang , Knopf Karl , Tan Yiqing , Ding Bolin , He Xi
Published: October 2021

Policy-aware differential privacy (DP) frameworks such as Blowfish privacy enable more accurate query answers than standard DP. In this work, we build the first policy-aware DP system for interactive data exploration, BlowfishDB, that aims to (i) provide bounded and flexible privacy guarantees to the data curators of sensitive data and (ii) support accurate and efficient data exploration by data analysts. However, the specification and processing of customized privacy policies incur additional performance cost, especially for datasets with a large domain. To address this challenge, we propose dynamic Blowfish privacy which allows for the dynamic generation of smaller privacy policies and their data representations at query time. BlowfishDB ensures same levels of accuracy and privacy as one would get working on the static privacy policy. In this demonstration of BlowfishDB, we show how a data curator can fine-tune privacy policies for a sensitive dataset and how a data analyst can retrieve accuracy-bounded query answers efficiently without being a privacy expert.
SAND in action
Authors: Boniol Paul , Paparrizos John , Palpanas Themis , Franklin Michael J.
Published: October 2021

Subsequence anomaly detection in long data series is a significant problem. While the demand for real-time analytics and decision making increases, anomaly detection methods have to operate over streams and handle drifts in data distribution. Nevertheless, existing approaches either require prior domain knowledge or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. Moreover, subsequence anomaly detection methods usually require access to the entire dataset and are not able to learn and detect anomalies in streaming settings. To address these limitations, we propose SAND, a novel online system suitable for domain-agnostic anomaly detection. SAND relies on a novel steaming methodology to incrementally update a model that adapts to distribution drifts and omits obsolete data. We demonstrate our system over different streaming scenarios and compare SAND with other subsequence anomaly detection methods.
GraphScope
Authors: Fan Wenfei , He Tao , Lai Longbin , Li Xue , Li Yong , Li Zhao , Qian Zhengping , Tian Chao , Wang Lei , Xu Jingbo , Yao Youyang , Yin Qiang , Yu Wenyuan , Zhou Jingren , Zhu Diwen , Zhu Rong
Published: October 2021

GraphScope is a system and a set of language extensions that enable a new programming interface for large-scale distributed graph computing. It generalizes previous graph processing frameworks
(e.g.
, Pregel, GraphX) and distributed graph databases (
e.g
., Janus-Graph, Neptune) in two important ways: by exposing a unified programming interface to a wide variety of graph computations such as graph traversal, pattern matching, iterative algorithms and graph neural networks within a high-level programming language; and by supporting the seamless integration of a highly optimized graph engine in a general purpose data-parallel computing system.

A GraphScope program is a sequential program composed of declarative data-parallel operators, and can be written using standard Python development tools. The system automatically handles the parallelization and distributed execution of programs on a cluster of machines. It outperforms current state-of-the-art systems by enabling a separate optimization (or family of optimizations) for each graph operation in one carefully designed coherent framework. We describe the design and implementation of GraphScope and evaluate system performance using several real-world applications.
Not black-box anymore!
Authors: Eltabakh Mohamed , Subramanian Anantha , Al-Omari Awny , Al-Kateb Mohammed , Nair Sanjay , Hasan Mahbub , Cabrera Wellington , Zhang Charles , Kishore Amit , Prasad Snigdha
Published: October 2021

Teradata Vantage is a platform for integrating a broad range of analytical functions and capabilities with the Teradata's SQL engine. One of the main challenges in optimizing the execution of these analytical functions is that many of them are not only black boxes, but also have polymorphic nature, i.e., their behavior and properties may change depending on the invocation context. In this paper, we first demonstrate the inherent complexity in optimizing polymorphic functions, and then present the Vantage's
Collaborative Optimizer
, which is a cross-platform optimizer designed for optimizing the analytical functions invoked from within the SQL engine. The Collaborative Optimizer is the industry-first effort towards enabling analytics-aware optimizations over polymorphic analytical functions. We present a novel markup language-based approach for expressing the functions' polymorphic properties via a set of well-defined instructions. The Collaborative Optimizer uses these instructions at query time to infer the corresponding properties, and then decide on the applicable optimizations. From several possible optimizations, we showcase two core optimizations, namely
"projection push"
and
"predicate push"
, which aim at optimizing the data movement to and from the analytical functions. The experiments using the Teradata-MLE analytical system demonstrate the expressiveness power and flexibility of the proposed markup language. Moreover, benchmark and real-world customer queries show the significant performance gain that the Collaborative Optimizer brings to the Vantage system.
Napa
Authors: Agiwal Ankur , Lai Kevin , Manoharan Gokul Nath Babu , Roy Indrajit , Sankaranarayanan Jagan , Zhang Hao , Zou Tao , Chen Min , Chen Zongchang (Jim) , Dai Ming , Do Thanh , Gao Haoyu , Geng Haoyan , Grover Raman , Huang Bo , Huang Yanlai , Li Zhi (Adam) , Liang Jianyi , Lin Tao , Liu Li , Liu Yao , Mao Xi , Meng Yalan (Maya) , Mishra Prashant , Patel Jay , R. Rajesh S. , Raman Vijayshankar , Roy Sourashis , Shishodia Mayank Singh , Sun Tianhang , Tang Ye (Justin) , Tatemura Junichi , Trehan Sagar , Vadali Ramkumar , Venkatasubramanian Prasanna , Zhang Gensheng , Zhang Kefei , Zhang Yupu , Zhuang Zeleng , Graefe Goetz , Agrawal Divyakant , Naughton Jeff , Kosalge Sujata , Hacıgümüş Hakan
Published: October 2021

Google services continuously generate vast amounts of application data. This data provides valuable insights to business users. We need to store and serve these planet-scale data sets under the extremely demanding requirements of scalability, sub-second query response times, availability, and strong consistency; all this while ingesting a massive stream of updates from applications used around the globe. We have developed and deployed in production an analytical data management system, Napa, to meet these requirements. Napa is the backend for numerous clients in Google. These clients have a strong expectation of variance-free, robust query performance. At its core, Napa's principal technologies for robust query performance include the aggressive use of materialized views, which are maintained consistently as new data is ingested across multiple data centers. Our clients also demand flexibility in being able to adjust their query performance, data freshness, and costs to suit their unique needs. Robust query processing and flexible configuration of client databases are the hallmark of Napa design.

Most of the related work in this area takes advantage of full flexibility to design the whole system without the need to support a diverse set of preexisting use cases. In comparison, a particular challenge we faced is that Napa needs to deal with hard constraints from existing applications and infrastructure, so we could not do a "green field" system, but rather had to satisfy existing constraints. These constraints led us to make particular design decisions and also devise new techniques to meet the challenges. In this paper, we share our experiences in designing, implementing, deploying, and running Napa in production with some of Google's most demanding applications.
openGauss
Authors: Li Guoliang , Zhou Xuanhe , Sun Ji , Yu Xiang , Han Yue , Jin Lianyuan , Li Wenbo , Wang Tianqing , Li Shifu
Published: October 2021

Although learning-based database optimization techniques have been studied from academia in recent years, they have not been widely deployed in commercial database systems. In this work, we build an autonomous database framework and integrate our proposed learning-based database techniques into an open-source database system openGauss. We propose effective learning-based models to build learned optimizers (including learned query rewrite, learned cost/cardinality estimation, learned join order selection and physical operator selection) and learned database advisors (including self-monitoring, self-diagnosis, self-configuration, and self-optimization). We devise an effective validation model to validate the effectiveness of learned models. We build effective training data management and model management platforms to easily deploy learned models. We have evaluated our techniques on real-world datasets and the experimental results validated the effectiveness of our techniques. We also provide our learnings of deploying learning-based techniques.
Hyperspace
Authors: Potharaju Rahul , Kim Terry , Song Eunjin , Wu Wentao , Novik Lev , Dave Apoorve , Fogarty Andrew , Pirzadeh Pouria , Acharya Vidip , Dhody Gurleen , Li Jiying , Ramanujam Sinduja , Bruno Nicolas , Galindo-Legaria César A. , Narasayya Vivek , Chaudhuri Surajit , Nori Anil K. , Talius Tomas , Ramakrishnan Raghu
Published: October 2021

Microsoft recently introduced Azure Synapse Analytics, which offers an integrated experience across data ingestion, storage, and querying in Apache Spark and T-SQL over data in the lake, including files and warehouse tables. In this paper, we present our experiences with designing and implementing Hyperspace, the indexing subsystem underlying Synapse. Hyperspace enables users to build multiple types of secondary indexes on their data, maintain them through a multi-user concurrency model, and leverage them automatically---without any change to their application code---for query/workload acceleration. Many requirements of Hyperspace are based on feedback from several enterprise customers. We present the details of Hyperspace's underlying design, the user-facing APIs, its concurrency control protocol for index access, its index-aware query processing techniques, and its maintenance mechanisms for handling index updates. Evaluations over standard industry benchmarks and real customer workloads show that Hyperspace can accelerate query execution by up to 10x and in certain real-world workloads, even up to two orders of magnitude.
SpeakNav
Authors: Zheng Bolong , Bi Lei , Cao Juan , Chai Hua , Fang Jun , Chen Lu , Gao Yunjun , Zhou Xiaofang , Jensen Christian S.
Published: October 2021

Many navigation applications take natural language speech as input, which avoids users typing in words and thus improves traffic safety. However, navigation applications often fail to understand a user's free-form description of a route. In addition, they only support input of a specific source or destination, which does not enable users to specify additional route requirements. We propose a SpeakNav framework that enables users to describe intended routes via speech and then recommends appropriate routes. Specifically, we propose a novel Route Template based Bidirectional Encoder Representation from Transformers (RT-BERT) model that supports the understanding of natural language route descriptions. The model enables extraction of information of intended POI keywords and related distances. Then we formalize a template-driven path query that uses the extracted information. To enable efficient query processing, we develop a hybrid label index for computing network distances between POIs, and we propose a branch-and-bound algorithm along with a pivot reverse B-tree (PB-tree) index. Experiments with real and synthetic data indicate that RT-BERT offers high accuracy and that the proposed algorithm is capable of outperforming baseline algorithms.
Hazelcast jet
Authors: Gencer Can , Topolnik Marko , Ďurina Viliam , Demirci Emin , Kahveci Ensar B. , Gürbüz Ali , Lukáš Ondřej , Bartók József , Gierlach Grzegorz , Hartman František , Yılmaz Ufuk , Doğan Mehmet , Mandouh Mohamed , Fragkoulis Marios , Katsifodimos Asterios
Published: October 2021

Jet is an open source, high performance, distributed stream processor built at Hazelcast during the last five years. Jet was engineered with millisecond latency on the 99.99th percentile as its primary design goal. Originally Jet's purpose was to be an execution engine that performs complex business logic on top of streams generated by Hazelcast's In-memory Data Grid (IMDG): a set of in-memory, partitioned and replicated data structures. With time, Jet evolved into a full-fledged, scale-out stream processor that can handle out-of-order streams and provide exactly-once processing guarantees. Jet's end-to-end latency lies in the order of milliseconds, and its throughput in the order of millions of events per CPU-core. This paper presents the main design decisions we made in order to maximize the performance per CPU-core, alongside lessons learned, and an empirical performance evaluation.
Watermarks in stream processing systems
Authors: Akidau Tyler , Begoli Edmon , Chernyak Slava , Hueske Fabian , Knight Kathryn , Knowles Kenneth , Mills Daniel , Sotolongo Dan
Published: October 2021

Streaming data processing is an exercise in taming disorder: from oftentimes huge torrents of information, we hope to extract powerful and timely analyses. But when dealing with streaming data, the unbounded and temporally disordered nature of real-world streams introduces a critical challenge: how does one reason about the completeness of a stream that never ends? In this paper, we present a comprehensive definition and analysis of
watermarks
, a key tool for reasoning about temporal completeness in infinite streams.

First, we describe what watermarks are and why they are important, highlighting how they address a suite of stream processing needs that are poorly served by eventually-consistent approaches:

• Computing a
single
correct answer, as in notifications.

• Reasoning about a
lack
of data, as in dip detection.

• Performing
non-incremental
processing over temporal subsets of an infinite stream, as in statistical anomaly detection with cubic spline models.

• Safely and punctually
garbage collecting
obsolete inputs and intermediate state.

• Surfacing a reliable signal of overall
pipeline health
.

Second, we describe, evaluate, and compare the semantically equivalent, but starkly different, watermark implementations in two modern stream processing engines: Apache Flink and Google Cloud Dataflow.
The cosmos big data platform at Microsoft
Authors: Power Conor , Patel Hiren , Jindal Alekh , Leeka Jyoti , Jenkins Bob , Rys Michael , Triou Ed , Zhu Dexin , Katahanas Lucky , Talapady Chakrapani Bhat , Rowe Joshua , Zhang Fan , Draves Rich , Friedman Marc , Filho Ivan Santa Maria , Kumar Amrish
Published: October 2021

The twenty-first century has been dominated by the need for large scale data processing, marking the birth of big data platforms such as Cosmos. This paper describes the evolution of the exabyte-scale Cosmos big data platform at Microsoft; our journey right from scale and reliability all the way to efficiency and usability, and our next steps towards improving security, compliance, and support for heterogeneous analytics scenarios. We discuss how the evolution of Cosmos parallels the evolution of the big data field, and how the changes in the Cosmos workloads over time parallel the changing requirements of users across industry.
On the limits of machine knowledge
Authors: Razniewski Simon , Arnaout Hiba , Ghosh Shrestha , Suchanek Fabian
Published: October 2021

General-purpose knowledge bases (KBs) are an important component of several data-driven applications. Pragmatically constructed from available web sources, these KBs are far from complete, which poses a set of challenges in curation as well as consumption.

In this tutorial we discuss how completeness, recall and negation in DBs and KBs can be represented, extracted, and inferred. We proceed in 5 parts: (i) We introduce the logical foundations of knowledge representation and querying under partial closed-world semantics. (ii) We show how information about recall can be identified in KBs and in text, and (iii) how it can be estimated via statistical patterns. (iv) We show how interesting negative statements can be identified, and (v) how recall can be targeted in a comparative notion.
Machine learning for cloud data systems
Authors: Jindal Alekh , Interlandi Matteo
Published: October 2021

The goal of this tutorial is to educate the audience about the state of the art in ML for cloud data systems, both in research and in practice. The tutorial is divided in two parts: the progress, and the path forward.

Part I covers the recent successes in deploying machine learning solutions for cloud data systems. We will discuss the practical considerations taken into account and the progress made at various levels. The goal is to compare and contrast the promise of ML for systems with the ground actually covered in industry.

Finally, Part II discusses practical issues of machine learning in the enterprise covering the generation of explanations, model debugging, model deployment, model management, constraints on eyes-on data usage and anonymization, and a discussion of the technical debt that can accrue through machine learning and models in the enterprise.
Evolution of a compiling query engine
Authors: Neumann Thomas
Published: October 2021

In 2011 we showed how to use dynamic code generation to process queries in a data-centric manner. This execution model can produce compact and efficient code and was successfully used by both our own systems and systems of other groups. As the systems become used in practice, additional techniques were developed for shortcomings that did arrive, including low-latency compilation, multi-threading support, and others. This paper gives an overview of the evolution of our query engine within in the last ten years, and points out which problem have to be tackled to bring a compiling system into production usage.
Computing how-provenance for SPARQL queries via query rewriting
Authors: Hernández Daniel , Galárraga Luis , Hose Katja
Published: October 2021

Over the past few years, we have witnessed the emergence of large knowledge graphs built by extracting and combining information from multiple sources. This has propelled many advances in query processing over knowledge graphs, however the aspect of providing provenance explanations for query results has so far been mostly neglected. We therefore propose a novel method, SPARQLprov, based on query rewriting, to compute how-provenance polynomials for SPARQL queries over knowledge graphs. Contrary to existing works, SPARQLprov is system-agnostic and can be applied to standard and already deployed SPARQL engines without the need of customized extensions. We rely on spm-semirings to compute polynomial annotations that respect the property of commutation with homomorphisms on monotonic and non-monotonic SPARQL queries without aggregate functions. Our evaluation on real and synthetic data shows that SPARQLprov over standard engines incurs an acceptable runtime overhead w.r.t. the original query, competing with state-of-the-art solutions for how-provenance computation.
Alchemical lexica in Syriac: planetary signs, code names and medicines
Authors: Martelli Matteo
Published: October 2021

Technical terminology and Decknamen represent key hallmarks of the alchemical literature in different traditions. The opacity of this vocabulary makes the reading of alchemical texts difficult and, in order to cope with similar challenges, Byzantine, Syriac and Arabic scholars soon started compiling technical vocabularies. In my paper I shall investigate two (partially overlapping) lexica, which open the BL Syriac alchemical MSS Egerton 709 and Oriental 1593. On the one hand, I will explore the variety of sources used by the anonymous compiler(s) to assemble these useful tools (Byzantine alchemists as well as the Greek medical tradition; Syro-Arabic lexicography). On the other, particular attention will be given to the structure and mise en page of the two lexica, which will be compared with analogous alchemical dictionaries in the Byzantine (e.g. MS Marcianus gr. 299) and Arabic (e.g. MS Gotha 1261) traditions.
The role of anticorrosive polymer coatings for the protection of metallic surface
Authors: Boga Karteek , Pothu Ramyakrishna , Arukula Ravi , Boddula Rajender , Gaddam Sashivinay Kumar
Published: October 2021

Research on corrosion-resistant polymer coatings has attracted scientists in both academia and industry to prevent corrosion-related damage in modern industrial equipment and to extend the working life of industrial metallic surfaces. To achieve realistic applications, diverse methodologies have been designed to magnify the anticorrosion efficiency of these coatings. The current review describes the conventional and advanced methods utilized for the synthesis of corrosion-resistant polymer coatings. The main intent of this review article is to provide an overview of the design and preparation of anticorrosive polymer coatings with important examples. The promising anticorrosion applications of self-healing polymer coatings for metallic materials and alloys are highlighted. The advantage of a superhydrophobic surface to prevent the corrosion of the materials is discussed. A brief discussion over the anticorrosion mechanism of conducting polymer coatings is also provided. Finally, the current challenges and future perspectives to prolong the anticorrosion performance of these coatings are also addressed.
Infinitely many solutions for nonlocal elliptic systems in Orlicz–Sobolev spaces
Authors: Heidari Samira , Razani Abdolrahman
Published: October 2021

Recently, the existence of at least two weak solutions for a Kirchhoff–type problem has been studied in [M. Makvand Chaharlang and A. Razani,
Two weak solutions for some Kirchhoff-type problem with Neumann boundary condition,
Georgian Math. J. 28 2021, 3, 429–438]. Here, the existence of infinitely many solutions for nonlocal Kirchhoff-type systems including Dirichlet boundary conditions in Orlicz–Sobolev spaces is studied by using variational methods and critical point theory.
Dealing with Not-Knowing in Inbound Open Innovation: A High-Tech Innovation Case
Authors: Tauraitė-Kavai Erika
Published: October 2021

Knowledge and innovation are two inseparable topics in the literature on knowledge management or innovation. Most often, when scholars write about knowledge management, they refer to innovation as the key objective. Some argue that using the knowledge available beyond a firm’s boundaries (open innovation) leads to increased innovativeness, while others talk of the knowledge-creating firm. However, current literature provides us with limited insights on how the innovation team deals with externally developed knowledge or how it comes into the innovation creation process. Managing teamwork innovation endeavours in technology development is challenging since the outcome is often uncertain as well as inputs along that path. This paper attempts to open the backbox of open innovation and suggests that innovating teams entwine externally developed knowledge through the process of dealing with not-knowing.
Stoichiometric modulation on optical nonlinearity of 2D MoS
x
Se2−x
alloys for photonic applications
Authors: Sun Xiaoli , Wang Yuanyuan , Yan Bingzheng , Yang Kejian , Wei Wei , Dai Ying , Chen Feng , Zhang Baitao , He Jingliang
Published: October 2021

The composition-engineered band structures of two-dimensional (2D) ternary transition-metal dichalcogenides (TMDCs) semiconductor alloys directly dominate their electronic and optical properties. Herein, in this paper, a detailed theoretical and experimental study on the composition-dependent nonlinear optical properties of 2D MoS
x
Se2−x
alloys was carried out. The first-principles calculations were performed to investigate the compositionally modulated properties of monolayer 2D MoS
x
Se2−x
(x = 0.25, 0.5, 1.0, 1.5, and 1.75) in terms of the carrier effective mass, carrier density and mobility, as well as band-gaps. Furthermore, high-quality few-layered MoS
x
Se2−x
(x = 0.2, 0.5, 1.0, 1.5, and 1.8) nanosheets were fabricated by using liquid phase exfoliation method. The third-order nonlinear optical response was investigated by open-aperture Z-scan technique, revealing composition-dependent saturable absorption, and light modulation properties, which were correlated to the theoretical calculations and further confirmed by using MoS
x
Se2−x
nanosheets as saturable absorbers (SAs) for all-solid-state pulsed lasers. In particular, a mode-locked solid-state laser with pulse width of 227 fs was realized with MoS0.2Se1.8 as SA, for the first time to our best knowledge. Our work not only provides a comprehensive understanding of the compositionally and defectively modulated nonlinear optical responses of ternary TMDCs alloys, but also paves a way for the development of 2D materials-based novel optoelectronic devices.
Austerity Effects on National Output and Income Inequality: A Systematic Literature Review
Authors: Okeke Angela , Alexiou Constantinos , Nellis Joseph
Published: October 2021

Fiscal sustainability issues over rising national debt concerns and the consequent expansionary fiscal retrenchment hypothesis has fuelled the contentious austerity vis-à-vis stimulus debate which has spawned a large empirical literature of conflicting findings on the economic effects of austerity – with particular emphasis revolving around equity and distributional issues. In this paper we attempt to summarise the growing literature on the recent developments regarding the theoretical as well as empirical approaches on national output and distributional aspects of austerity. By exploring the existing evidence in the literature on the effect of consolidation programs, we offer a more holistic overview of the subject matter through a synthesis of the extant literature and, by so doing, propose directions for future research pertinent to both academic researchers and policymakers.
Chiral bis(pyrazolyl)methane copper(I) complexes and their application in nitrene transfer reactions
Authors: Thomas Fabian , Steden Dominik , Eith Alexander , Hoffmann Alexander , Herres-Pawlis Sonja
Published: October 2021

In this study, chiral bis(pyrazolyl)methane copper(I) acetonitrile complexes were applied to generate two novel terminal copper tosyl nitrene complexes with the nitrene generating agent SPhINTs in dichloromethane at low temperatures. The syntheses of the chiral bis(pyrazolyl)methane ligands are based on pulegone and camphor, members of the natural chiral pool. The chiral copper(I) acetonitrile complexes were applied as catalysts in the copper nitrene mediated aziridination reaction of different styrene derivatives and the C–H amination of various substrates. The reactions afforded good yields, but low enantiomeric excess under mild conditions. The nitrene species have been characterized with UV/Vis and EPR spectroscopy and the products of the decay by ESI mass spectrometry.
Ksylitol – rola technologiczna i żywieniowa
Authors: Baranowski Damian , Rutkowska Jarosława , Antoniewska Agata
Published: October 2021

W pracy przedstawiono aktualny stan wiedzy dotyczącej ksylitolu jako substancji słodzącej, stanowiącej alternatywę dla powszechnie stosowanych syropów fruktozowo-glukozowych i sacharozy w produktach spożywczych. Ksylitol jest pięciowęglowym alkoholem cukrowym, występującym naturalnie głównie w produktach pochodzenia roślinnego. Na skalę przemysłową otrzymywany jest w procesie uwodornienia ksylozy. Jest najsłodszym z polioli o względnej słodkości porównywalnej z sacharozą. Ksylitol (E 967) został dopuszczony w krajach Unii Europejskiej jako dodatek do żywności na poziomie quantum satis w większości produktów spożywczych. Organy ds. bezpieczeństwa żywności, takie jak JECFA (Joint Expert Committee on Food Additives), SCF (Scientific Committee on Food) czy FDA (Food and Drug Administration) uznały ksylitol za bezpieczny środek słodzący. Ksylitol może być stosowany nie tylko ze względu na kształtowanie słodkości w produktach spożywczych, ale również z uwagi na liczne właściwości prozdrowotne. Zaledwie 25 ÷ 50 % ksylitolu jest absorbowane w jelicie cienkim, a metabolizm ksylitolu zachodzi przy niewielkim udziale insuliny i prowadzi do nieznacznego podwyższenia poziomu glukozy we krwi, dlatego zalecany jest diabetykom, jak również osobom otyłym. Ksylitol jest również ceniony ze względu na udokumentowane właściwości przeciwpróchnicze (nie stanowi substratu do procesów energetycznych drobnoustrojów próchnicotwórczych, wpływa korzystnie na remineralizację szkliwa oraz obniża zdolność patogenów do adhezji do powierzchni zęba). Podawany w odpowiedniej dawce może wpływać również na obniżenie ryzyka wystąpienia ostrego zapalenia ucha środkowego u dzieci.
Porównanie jakości muffin bezglutenowych z mąki jaglanej i gryczanej
Authors: Kaszuba Joanna , Posadzka Zuzanna , Kogut Barbara , Jaworska Grażyna , Kuźniar Piotr , Łukaszek Agnieszka
Published: October 2021

Jedynym sposobem leczenia celiakii, glutenozależnej choroby trzewnej lub enteropatii glutenowej, jest całkowite wyeliminowanie białek glutenowych z pożywienia, co wiąże się z bezwzględnym przestrzeganiu diety bezglutenowej. Celem pracy było określenie receptury oraz porównanie jakości muffin bezglutenowych gryczanych, jaglanych oraz gryczano-jaglanych wyprodukowanych w dwóch wariantach smakowych: słodkim oraz wytrawnym. Badania jakości muffin obejmowały ocenę ich wyglądu zewnętrznego i miękiszu, a także pomiar parametrów barwy, analizę parametrów profilu tekstury (test TPA) i ocenę organoleptyczną. W ocenie organoleptycznej słodkich wyrobów najwyżej oceniono muffiny gryczane oraz gryczano-jaglane, zaś wśród muffin wytrawnych – gryczane i zaliczono je do bardzo dobrej klasy jakości. Jakość pozostałych muffin sklasyfikowano jako dobrą. Barwa badanych muffin bezglutenowych była w różnym stopniu zależna od rodzaju użytej mąki i pozostałych składników receptury. Miękisz muffin był podobnej elastyczności, zaś pozostałe parametry ich tekstury były istotnie zróżnicowane. Na podstawie uzyskanych wyników stwierdzono, że badane bezglutenowe mąki jednogatunkowe, jak i ich mieszanki, można stosować do produkcji muffin o dobrej jakości organoleptycznej, mogących wzbudzić zainteresowanie konsumentów poszukujących wartościowych i smacznych, a przede wszystkim dietetycznych produktów zbożowych.
An assessment of meat products with added selected bioactive substances
Authors: Przybylski Wiesław , Jaworska Danuta , Kajak-Siemaszko Katarzyna , Sałek Piotr , Lubański Mateusz
Published: October 2021

Implementation of bioactive antioxidant compounds into the product can enhance the product quality and extend the shelf-life of meat and meat products. The aim of the study was to evaluate the effect of fish oil, lyophilisate of tomato concentrate and thyme extract additives on the oxidation stability, colour parameters and sensory quality of the meat product during its storage. This study was conducted on a model meat product in the form of meatballs made from minced: chicken breast muscles, turkey drumstick muscles, pork meat, with addition of eggs, wheat bread, salt, black pepper and herbal pepper. After heat treatment, the samples were cooled and were stored under vacuum at 4 ºC for 14 days. It was shown that addition of 2 % freeze-dried tomato concentrate to meatballs resulted in cooking yield increase during heat treatment, decrease of pH and changes in colour parameters as well as stabilization of colour during storage. The addition of tomato concentrate also improved the sensory quality and also influenced the maintenance of the overall quality during the initial storage period. The addition of 4 % thyme extract to meatballs stopped oxidative changes and did not significantly affect the technological and sensory quality. Meatballs with the addition of cod liver oil and thyme extract were characterized by the highest overall quality after heat treatment. Based on sensory evaluation and oxidative status, meatballs can be stored for up to 7 days.
Identyfikacja fałszowania frakcji tłuszczowej wybranych czekolad
Authors: Suwała Grzegorz
Published: October 2021

Oszustwa w branży spożywczej nie są zjawiskiem nowym, jednak w opracowaniach z ostatnich lat podkreśla się skalę tego problemu i towarzyszące mu straty gospodarcze. Według danych z 2018 roku oszustwa na rynku żywności w skali świata szacuje się na ok. 129 mld złotych. Czekolada należy do grupy wyrobów kakaowych i czekoladowych. Z uwagi na rodzaj surowca, z jakiego jest wytwarzana, musi spełniać wymagania określone w odpowiednich przepisach. Określono w nich m.in., pod jakimi warunkami prawodawca dopuszcza dodanie do omawianych produktów tłuszczu roślinnego innego niż kakaowy. Celem pracy była identyfikacja zafałszowań czekolad z dodatkiem zamiennika tłuszczu kakaowego na podstawie udziału kwasów tłuszczowych oraz ocena wartości wskaźnika proporcji kwasu stearynowego do palmitynowego. Materiał doświadczalny stanowiło osiem czekolad różnych marek, zapakowanych w opakowania jednostkowe o masie netto 100 g, zakupionych w handlu detalicznym, pochodzących z różnych partii produkcyjnych, nabywanych w interwale rocznym w latach 2014 - 2018. Łącznie w podanym okresie przebadano 40 czekolad. Stwierdzono, że zróżnicowana jakość dostępnego na rynku tłuszczu kakaowego decyduje o jakości wytworzonych z użyciem tego surowca produktów. Zidentyfikowano dużą zmienność udziału zafałszowanych produktów w okresie realizacji badań. Wskazano istotne różnice wartości proporcji kwasów tłuszczowych – stearynowego do palmitynowego pomiędzy analizowanymi próbkami czekolad, a także ustalono, że wysokiej jakości tłuszcz kakaowy pozwala na uzyskanie odpowiednio wysokiego wskaźnika proporcji nawet z dodatkiem tańszego zamiennika tłuszczu kakaowego.
Zmiany nomenklatury taksonomicznej ważnych gatunków probiotycznych bakterii Lactobacillus
Authors: Zielińska Dorota , Kołożyn-Krajewska Danuta
Published: October 2021

Gatunki Lactobacillus mają ogromne znaczenie w życiu człowieka, począwszy od ich kluczowej roli w fermentacji żywności, a skończywszy na korzystnym dla zdrowia wpływie na organizmy ludzi i zwierząt. Celem niniejszej pracy było przedstawienie, z uwzględnieniem historii rodzaju Lactobacillus, obecnej rewolucyjnej zmiany nomenklatury taksonomicznej, a także przedyskutowanie znaczenia tej zmiany dla konsumentów, producentów żywności probiotycznej i innych zainteresowanych stron. Piętnastu naukowców z dwunastu różnych instytucji i siedmiu różnych krajów przeanalizowało cały genom każdego znanego gatunku Lactobacillus i wskazało propozycję zmian. Propozycja ta została przyjęta do druku w oficjalnym dzienniku rejestrów nazw bakterii. W analizie autorzy przyjęli wielofazowe podejście i na tej podstawie dokonali reklasyfikacji rodzaju Lactobacillus na 25 rodzajów. Ta bezprecedensowa skala zmian może być myląca dla zainteresowanych stron. Jednakże wprowadza także szereg korzyści związanych ze zgrupowaniem blisko spokrewnionych bakterii w bardziej jednolite i stabilne ewolucyjnie taksony, a także ze stworzeniem możliwości lepszego projektowania ukierunkowanych grup żywności, cechujących się funkcjonalnością i biologiczną skutecznością.