bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast dataset search with earth mover's distance
Authors: Yang Wenzhe , Wang Sheng , Sun Yuan , Peng Zhiyong
Published: September 2022

The amount of spatial data in open data portals has increased rapidly, raising the demand for spatial dataset search in large data repositories. In this paper, we tackle spatial dataset search by using the Earth Mover's Distance (EMD) to measure the similarity between datasets. EMD is a robust similarity measure between two distributions and has been successfully applied to multiple domains such as image retrieval, document retrieval, multimedia, etc. However, the existing EMD-based studies typically depend on a common filtering framework with a single pruning strategy, which still has a high search cost. To address this issue, we propose a Dual-Bound Filtering (DBF) framework to accelerate the EMD-based spatial dataset search. Specifically, we represent datasets by Z-order histograms and organize them as nodes in a tree structure. During a query, two levels of filtering are conducted based on pooling-based bounds and a TICT bound on EMD to prune dissimilar datasets efficiently. We conduct experiments on four real-world spatial data repositories and the experimental results demonstrate the efficiency and effectiveness of our DBF framework.
Example-based spatial pattern matching
Authors: Chen Yue , Feng Kaiyu , Cong Gao , Kiah Han Mao
Published: September 2022

The prevalence of GPS-enabled mobile devices and location-based services yield massive volume of spatial objects where each object contains information including geographical location, name, address, category and other attributes. This paper introduces a novel type of query termedexample-based spatial pattern matching(EPM) query. It takes as input a set of spatial objects, each of which is associated with one or more keywords and a location. These objects serve as an example that depicts the spatial pattern that users want to retrieve. The EPM query returns all sets of objects that match the spatial pattern. The EPM query can be used for applications like urban planning, scene recognition and similar region search. We propose an efficient algorithm and three pruning techniques to answer EPM queries. Furthermore, we provide an approximation guarantee for intermediate results of the algorithm. Our experimental evaluations on four real-world datasets demonstrate the effectiveness and efficiency of our proposed algorithm and techniques.
AIM
Authors: McKenna Ryan , Mullins Brett , Sheldon Daniel , Miklau Gerome
Published: September 2022

We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally generates synthetic data from the noisy measurements. It uses a set of innovative features to iteratively select the most useful measurements, reflecting both their relevance to the workload and their value in approximating the input data. We also provide analytic expressions to bound per-query error with high probability which can be used to construct confidence intervals and inform users about the accuracy of generated data. We show empirically that AIM consistently outperforms a wide variety of existing mechanisms across a variety of experimental settings.
Troubles with nulls, views from the users
Authors: Toussaint Etienne , Guagliardo Paolo , Libkin Leonid , Sequeda Juan
Published: September 2022

Incomplete data, in the form of null values, has been extensively studied since the inception of the relational model in the 1970s. Anecdotally, one hears that the way in which SQL, the standard language for relational databases, handles nulls creates a myriad of problems in everyday applications of database systems. To the best of our knowledge, however, the actual shortcomings of SQL in this respect, as perceived by database practitioners, have not been systematically documented, and it is not known if existing research results can readily be used to address the practical challenges.

Our goal is to collect and analyze the shortcomings of nulls and their treatment by SQL, and to re-evaluate existing research in this light. To this end, we designed and conducted a survey on the everyday usage of null values among database users. From the analysis of the results we reached two main conclusions. First, null values are ubiquitous and relevant in real-life scenarios, but SQL's features designed to deal with them cause multiple problems. The severity of these problems varies depending on the SQL features used, and they cannot be reduced to a single issue. Second, foundational research on nulls is misdirected and has been addressing problems of limited practical relevance. We urge the community to view the results of this survey as a way to broaden the spectrum of their researches and further bridge the theory-practice gap on null values.
Shortest-path queries on complex networks
Authors: Zhang Junhua , Li Wentao , Yuan Long , Qin Lu , Zhang Ying , Chang Lijun
Published: September 2022

The shortest-path query, which returns the shortest path between two vertices, is a basic operation on complex networks and has numerous applications. To handle shortest-path queries, one option is to use traversal-based methods (e.g., breadth-first search); another option is to use extension-based methods, i.e., extending existing methods that use indexes to handle shortest-distance queries to support shortest-path queries. These two types of methods make different trade-offs in query time and space cost, but comprehensive studies of their performance on real-world graphs are lacking. Moreover, extension-based methods usually use extra attributes to extend the indexes, resulting in high space costs. To address these issues, we thoroughly compare the two types of methods mentioned above. We also propose a new extension-based approach, Monotonic Landmark Labeling (MLL), to reduce the required space cost while still guaranteeing query time. We compare the performance of different methods on ten large real-world graphs with up to 5.5 billion edges. The experimental results reveal the characteristics of various methods, allowing practitioners to select the appropriate method for a specific application.
JENNER
Authors: Ghosh Dhrubajyoti , Gupta Peeyush , Mehrotra Sharad , Yus Roberto , Altowim Yasser
Published: September 2022

Emerging domains, such as sensor-driven smart spaces and social media analytics, require incoming data to beenrichedprior to its use. Enrichment often consists of machine learning (ML) functions that are too expensive/infeasible to execute at ingestion. We develop a strategy entitled Just-in-time ENrichmeNt in quERy Processing (JENNER) to support interactive analytics over data as soon as it arrives for such application context. JENNER exploits the inherent tradeoffs of cost and quality often displayed by the ML functions to progressively improve query answers during query execution. We describe how JENNER works for a large class of SPJ and aggregation queries that form the bulk of data analytics workload. Our experimental results on real datasets (IoT and Tweet) show that JENNER achieves progressive answers performing significantly better than the naive strategies of achieving progressive computation.
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
Authors: Shao Zezhi , Zhang Zhao , Wei Wei , Wang Fei , Xu Yongjun , Cao Xin , Jensen Christian S.
Published: September 2022

We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals entirely as the outcome of the diffusion, while neglecting the inherent signals, which impacts model performance negatively. To improve modeling performance, we propose a novel Decoupled Spatial-Temporal Framework (DSTF) that separates the diffusion and inherent traffic information in a data-driven manner, which encompasses a unique estimation gate and a residual decomposition mechanism. The separated signals can be handled subsequently by the diffusion and inherent modules separately. Further, we propose an instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network (D
2
STGNN), that captures spatial-temporal correlations and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Extensive experiments with four real-world traffic datasets demonstrate that the framework is capable of advancing the state-of-the-art.
On shapley value in data assemblage under independent utility
Authors: Luo Xuan , Pei Jian , Cong Zicun , Xu Cheng
Published: September 2022

In many applications, an organization may want to acquire data from many data owners. Data marketplaces allow data owners to produce data assemblage needed by data buyers through coalition. To encourage coalitions to produce data, it is critical to allocate revenue to data owners in a fair manner according to their contributions. Although in literature Shapley fairness and alternatives have been well explored to facilitate revenue allocation in data assemblage, computing exact Shapley value for many data owners and large assembled data sets through coalition remains challenging due to the combinatoric nature of Shapley value. In this paper, we explore the decomposability of utility in data assemblage by formulating the independent utility assumption. We argue that independent utility enjoys many applications. Moreover, we identify interesting properties of independent utility and develop fast computation techniques for exact Shapley value under independent utility. Our experimental results on a series of benchmark data sets show that our new approach not only guarantees the exactness of Shapley value, but also achieves faster computation by orders of magnitudes.
Memory-optimized multi-version concurrency control for disk-based database systems
Authors: Freitag Michael , Kemper Alfons , Neumann Thomas
Published: September 2022

Pure in-memory database systems offer outstanding performance but degrade heavily if the working set does not fit into DRAM, which is problematic in view of declining main memory growth rates. In contrast, recently proposed memory-optimized disk-based systems such as Umbra leverage large in-memory buffers for query processing but rely on fast solid-state disks for persistent storage. They offer near in-memory performance while the working set is cached, and scale gracefully to arbitrarily large data sets far beyond main memory capacity. Past research has shown that this architecture is indeed feasible for read-heavy analytical workloads.

We continue this line of work in the following paper, and present a novel multi-version concurrency control approach that enables a memory-optimized disk-based system to achieve excellent performance on transactional workloads as well. Our approach exploits that the vast majority of versioning information can be maintained entirely in-memory without ever being persisted to stable storage, which minimizes the overhead of concurrency control. Large write transactions for which this is not possible are extremely rare, and handled transparently by a lightweight fallback mechanism. Our experiments show that the proposed approach achieves transaction throughput up to an order of magnitude higher than competing disk-based systems, confirming its viability in a real-world setting.
SwitchTx
Authors: Li Junru , Lu Youyou , Zhang Yiming , Wang Qing , Cheng Zhuo , Huang Keji , Shu Jiwu
Published: September 2022

Online-transaction-processing (OLTP) applications require the underlying storage system to guarantee consistency and serializability for distributed transactions involving large numbers of servers, which tends to introduce high coordination cost and cause low system performance. In-network coordination is a promising approach to alleviate this problem, which leverages programmable switches to move a piece of coordination functionality into the network. This paper presents a fast and scalable transaction processing system called SwitchTx. At the core of SwitchTx is a decentralized multi-switch in-network coordination mechanism, which leverages modern switches' programmability to reduce coordination cost while avoiding the central-switch-caused problems in the state-of-the-art Eris transaction processing system. SwitchTx abstracts various coordination tasks (e.g., locking, validating, and replicating) as in-switch gather-and-scatter (GaS) operations, and offloads coordination to a tree of switches for each transaction (instead of to a central switch for all transactions) where the client and the participants connect to the leaves. Moreover, to control the transaction traffic intelligently, SwitchTx reorders the coordination messages according to their semantics and redesigns the congestion control combined with admission control. Evaluation shows that SwitchTx outperforms current transaction processing systems in various workloads by up to 2.16X in throughput, 40.4% in latency, and 41.5% in lock time.
CodexDB
Authors: Trummer Immanuel
Published: September 2022

CodexDB enables users to customize SQL query processing via natural language instructions. CodexDB is based on OpenAI's GPT-3 Codex model which translates text into code. It is a framework on top of GPT-3 Codex that decomposes complex SQL queries into a series of simple processing steps, described in natural language. Processing steps are enriched with user-provided instructions and descriptions of database properties. Codex translates the resulting text into query processing code. An early prototype of CodexDB is able to generate correct code for up to 81% of queries for the WikiSQL benchmark and for up to 62% on the SPIDER benchmark.
BABOONS
Authors: Trummer Immanuel
Published: September 2022

BABOONS (BlAck BOx Optimization of Natural language data Summaries) optimizes text data summaries for an arbitrary, user-defined utility function. Primarily, it targets scenarios in which utility is evaluated via large language models. Users describe their utility function in natural language or provide a model, trained to score text summaries in a specific domain.

BABOONS uses reinforcement learning to explore the space of possible descriptions. In each iteration, BABOONS generates summaries and evaluates their utility. To reduce data processing overheads during summary generation, BABOONS uses a proactive processing strategy that dynamically merges current with likely future queries for efficient processing. Also, BABOONS supports scenario-specific sampling and batch processing strategies. These mechanisms allow to scale processing to large data and item sets. The experiments show that BABOONS scales significantly better than baselines. Also, they show that summaries generated by BABOONS receive higher average grades from users in a large survey.
Chimp
Authors: Liakos Panagiotis , Papakonstantinopoulou Katia , Kotidis Yannis
Published: September 2022

Applications in diverse domains such as astronomy, economics and industrial monitoring, increasingly press the need for analyzing massive collections of
time series
data. The sheer size of the latter hinders our ability to efficiently store them and also yields significant storage costs. Applying general purpose compression algorithms would effectively reduce the size of the data, at the expense of introducing significant computational overhead. Time Series Management Systems that have emerged to address the challenge of handling this overwhelming amount of information, cannot suffer the ingestion rate restrictions that such compression algorithms would cause. Data points are usually encoded using faster, streaming compression approaches. However, the techniques that contemporary systems use do not fully utilize the compression potential of time series data, with implications in both storage requirements and access times. In this work, we propose a novel streaming compression algorithm, suitable for floating point time series data. We empirically establish properties exhibited by a diverse set of time series and harness these features in our proposed encodings. Our experimental evaluation demonstrates that our approach
readily outperforms
competing techniques, attaining compression ratios that are competitive with
slower
general purpose algorithms, and on average around 50% of the space required by state-of-the-art streaming approaches. Moreover, our algorithm outperforms all earlier techniques with regards to both
compression and access time
, offering a significantly
improved trade-off
between space and speed. The aforementioned benefits of our approach - in terms of
all
space requirements, compression time and read access - significantly improve the efficiency in which we can store and analyze time series data.
Fine-grained modeling and optimization for intelligent resource management in big data processing
Authors: Lyu Chenghao , Fan Qi , Song Fei , Sinha Arnab , Diao Yanlei , Chen Wei , Ma Li , Feng Yihui , Li Yaliang , Zeng Kai , Zhou Jingren
Published: September 2022

Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute based integrated system to support multi-objective resource optimization via fine-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new fine-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level RO decisions well under a second. Evaluation using production workloads shows that our new RO system could reduce 37--72% latency and 43--78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s.
Diversified top-
k
route planning in road network
Authors: Luo Zihan , Li Lei , Zhang Mengxuan , Hua Wen , Xu Yehong , Zhou Xiaofang
Published: September 2022

Route planning is ubiquitous and has a profound impact on our daily life. However, the existing path algorithms tend to produce similar paths between similar OD (Origin-Destination) pairs because they optimize query results without considering their influence on the whole network, which further introduces congestions. Therefore, we investigate the problem of diversifying the top-k paths between an OD pair such that their similarities are under a threshold while their total length is minimal. However, the current solutions all depend on the expensive graph traversal which is too slow to apply in practice. Therefore, we first propose an edge deviation and concatenation-based method to avoid the expensive graph search in path enumeration. After that, we dive into the path relations and propose a path similarity computation method with constant complexity, and propose a pruning technique to improve efficiency. Finally, we provide the completeness and efficiency-oriented solutions to further accelerate the query answering. Evaluations on the real-life road networks demonstrate the effectiveness and efficiency of our algorithm over the state-of-the-art.
Enabling efficient and general subpopulation analytics in multidimensional data streams
Authors: Manousis Antonis , Cheng Zhuo , Basat Ran Ben , Liu Zaoxing , Sekar Vyas
Published: September 2022

Today's large-scale services (
e.g.
, video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a "sketch of sketches" to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times.
Dynamic spanning trees for connectivity queries on fully-dynamic undirected graphs
Authors: Chen Qing , Lachish Oded , Helmer Sven , Böhlen Michael H.
Published: September 2022

Answering connectivity queries is fundamental to fully dynamic graphs where edges and vertices are inserted and deleted frequently. Existing work proposes data structures and algorithms with worst case guarantees. We propose a new data structure, the
dynamic tree
(D-tree), together with algorithms to construct and maintain it. The D-tree is the first data structure that scales to fully dynamic graphs with millions of vertices and edges and, on average, answers connectivity queries much faster than data structures with worst case guarantees.
ByteHTAP
Authors: Chen Jianjun , Ding Yonghua , Liu Ye , Li Fangshi , Zhang Li , Zhang Mingyi , Wei Kui , Cao Lixun , Zou Dan , Liu Yang , Zhang Lei , Shi Rui , Ding Wei , Wu Kai , Luo Shangyu , Sun Jason , Liang Yuming
Published: September 2022

In recent years, at ByteDance, we see more and more business scenarios that require performing complex analysis over freshly imported data, together with transaction support and strong data consistency. In this paper, we describe our journey of building ByteHTAP, an HTAP system with high data freshness and strong data consistency. It adopts a separate-engine and shared-storage architecture. Its modular system design fully utilizes an existing ByteDance's OLTP system and an open source OLAP system. This choice saves us a lot of resources and development time and allows easy future extensions such as replacing the query processing engine with other alternatives.

ByteHTAP can provide high data freshness with less than one second delay, which enables many new business opportunities for our customers. Customers can also configure different data freshness thresholds based on their business needs. ByteHTAP also provides strong data consistency through global timestamps across its OLTP and OLAP system, which greatly relieves application developers from handling complex data consistency issues by themselves. In addition, we introduce some important performance optimizations to ByteHTAP, such as pushing computations to the storage layer and using delete bitmaps to efficiently handle deletes. Lastly, we will share our lessons and best practices in developing and running ByteHTAP in production.
Ganos
Authors: Xie Jiong , Chen Zhen , Liu Jianwei , Wang Fang , Li Feifei , Chen Zhida , Liu Yinpei , Cai Songlu , Fan Zhenhua , Xiao Fei , Chen Yue
Published: September 2022

Recently, the trend of developing digital twins for smart cities has driven a need for managing large-scale multidimensional, dynamic, and scene-oriented spatial data. Due to larger data scale and more complex data structure, queries over such data are more complicated and expensive than those on traditional spatial data, which poses challenges to the system efficiency and deployment costs. The existing spatial databases have limited support in both data types and operations. Therefore, a new-generation spatial database with excellent performance and effective deployment costs is needed.

This paper presents Ganos, a cloud-native spatial database engine of PolarDB for PostgreSQL that is developed by Alibaba Cloud, to efficiently manage multidimensional, dynamic, and scene-oriented spatial data. Ganos models 3D space and spatio-temporal dynamics as first-class citizens. Also, it natively supports spatial/spatio-temporal data types such as 3DMesh, Trajectory, Raster, PointCloud, etc. Besides, it implements a novel extended-storage mechanism that utilizes cloud-native object storage to reduce storage costs and enable uniform operations on the data in different storages. To facilitate processing "big" queries, Ganos extends PolarDB and provides spatial-oriented multi-level parallelism under the architecture of decoupling compute from storage in cloud-native databases, which achieves elasticity and excellent query performance. We demonstrate Ganos in real-life case studies. The performance of Ganos is evaluated using real datasets, and promising results are obtained. Finally, based on the extensive deployment and application of Ganos, the lessons learned from our customers and the expectations of modern cloud applications for new spatial database features are discussed.
Doppler
Authors: Cahoon Joyce , Wang Wenjing , Zhu Yiwen , Lin Katherine , Liu Sean , Truong Raymond , Singh Neetu , Wan Chengcheng , Ciortea Alexandra , Narasimhan Sreraman , Krishnan Subru
Published: September 2022

Selecting the optimal cloud target to migrate SQL estates from on-premises to the cloud remains a challenge. Current solutions are not only time-consuming and error-prone, requiring significant user input, but also fail to provide appropriate recommendations. We present Doppler, a scalable recommendation engine that provides right-sized Azure SQL Platform-as-a-Service (PaaS) recommendations without requiring access to sensitive customer data and queries. Doppler introduces a novel price-performance methodology that allows customers to get a personalized rank of relevant cloud targets solely based on low-level resource statistics, such as latency and memory usage. Doppler supplements this rank with internal knowledge of Azure customer behavior to help guide new migration customers towards one optimal target. Experimental results over a 9-month period from prospective and existing customers indicate that Doppler can identify optimal targets and adapt to changes in customer workloads. It has also found cost-saving opportunities among over-provisioned cloud customers, without compromising on capacity or other requirements. Doppler has been integrated and released in the Azure Data Migration Assistant v5.5, which receives hundreds of assessment requests daily.
Manu
Authors: Guo Rentong , Luan Xiaofan , Xiang Long , Yan Xiao , Yi Xiaomeng , Luo Jigao , Cheng Qianya , Xu Weizhi , Luo Jiarui , Liu Frank , Cao Zhenshan , Qiao Yanliang , Wang Ting , Tang Bo , Xie Charles
Published: September 2022

With the development of learning-based embedding models, embedding vectors are widely used for analyzing and searching unstructured data. As vector collections exceed billion-scale, fully managed and horizontally scalable vector databases are necessary. In the past three years, through interaction with our 1200+ industry users, we have sketched a vision for the features that next-generation vector databases should have, which include long-term evolvability, tunable consistency, good elasticity, and high performance.

We present Manu, a cloud native vector database that implements these features. It is difficult to integrate all these features if we follow traditional DBMS design rules. As most vector data applications do not require complex data models and strong data consistency, our design philosophy is to relax the data model and consistency constraints in exchange for the aforementioned features. Specifically, Manu firstly exposes the write-ahead log (WAL) and binlog as backbone services. Secondly, write components are designed as log publishers while all read-only analytic and search components are designed as independent subscribers to the log services. Finally, we utilize multi-version concurrency control (MVCC) and a delta consistency model to simplify the communication and cooperation among the system components. These designs achieve a low coupling among the system components, which is essential for elasticity and evolution. We also extensively optimize Manu for performance and usability with hardware-aware implementations and support for complex search semantics. Manu has been used for many applications, including, but not limited to, recommendation, multimedia, language, medicine and security. We evaluated Manu in three typical application scenarios to demonstrate its efficiency, elasticity, and scalability.
Kelpie
Authors: Rossi Andrea , Firmani Donatella , Merialdo Paolo , Teofili Tommaso
Published: September 2022

The latest generations of Link Prediction (LP) models rely on embeddings to tackle incompleteness in Knowledge Graphs, achieving great performance at the cost of interpretability. Their opaqueness limits the trust that users can place in them, hindering their adoption in real-world applications. We have recently introduced Kelpie, an explainability framework tailored specifically for embedding-based LP models. Kelpie can be applied to any embedding-based LP model, and supports two explanation scenarios that we have called
necessary
and
sufficient.
In this demonstration we showcase Kelpie's capability to explain the predictions of models based on vastly different architectures on the 5 major datasets in literature.
Share the tensor tea
Authors: Asada Yuki , Fu Victor , Gandhi Apurva , Gemawat Advitya , Zhang Lihao , He Dong , Gupta Vivek , Nosakhare Ehi , Banda Dalitso , Sen Rathijit , Interlandi Matteo
Published: September 2022

We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to supports the TPC-H benchmark, and it provides performance that are comparable to, and often better than, that of specialized CPU and GPU query processors.
ActivePDB
Authors: Drien Osnat , Freiman Matanya , Amsterdamer Yael
Published: September 2022

We present a novel framework for uncertain data management, called ActivePDB. We are given a relational probabilistic database, where each tuple is correct with some probability; e.g., a database constructed from textual data using information extraction. We are now given a query and we want to determine the correctness of its results. Unlike probabilistic databases, we have an oracle that can resolve the uncertainty, such as a domain expert that can verify data against their sources. Since verification may be costly, our goal is to determine the correct output of the query, while asking the oracle to verify as few tuples as possible. ActivePDB provides an end-to-end solution to this problem. In a nutshell, we first track provenance to identify which input tuples contribute to the derivation of each output tuple, and in what ways. We then design an active learning solution to iteratively choose tuples to be verified based on the provenance structure and on an evolving estimation of the probability of the tuples correctness. We will demonstrate ActivePDB in the context of the NELL database of extracted facts, allowing participants to both pose queries and play the role of oracles.
Satellite image search in AgoraEO
Authors: Aksoy Ahmet Kerem , Dushev Pavel , Zacharatou Eleni Tzirita , Hemsen Holmer , Charfuelan Marcela , Quiané-Ruiz Jorge-Arnulfo , Demir Begüm , Markl Volker
Published: September 2022

The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives. MiLaN is a deep hashing network based on metric learning that encodes high-dimensional image features into compact binary hash codes. We use these codes as keys in a hash table to enable real-time nearest neighbor search and highly accurate retrieval. In this demonstration, we showcase the efficiency of MiLaN by integrating it with EarthQube, a browser and search engine within AgoraEO. EarthQube supports interactive visual exploration and Query-by-Example over satellite image repositories. Demo visitors will interact with EarthQube playing the role of different users that search images in a large-scale remote sensing archive by their semantic content and apply other filters.
SmartBench
Authors: Orogat Abdelghny , El-Roby Ahmed
Published: September 2022

In recent years, a significant number of question answering (QA) systems that retrieve answers to natural language questions from knowledge graphs (KG) have been introduced. However, finding a benchmark that accurately evaluates the quality of a question answering system is a difficult task because of (1) the high degree of variations with respect to the fine-grained properties among the available benchmarks, (2) the static nature of the available benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real deployment over KGs that are different from those which the QA system was evaluated using. In this demonstration, we introduce SmartBench, an automatic benchmark generating system for QA over any KG. The benchmark generated by SmartBench is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties.
AMRAS
Authors: Wu Qingshun , Li Yafei , Li Huiling , Zhang Di , Zhu Guanglei
Published: September 2022

The wide adoption of GPS-enabled smart devices has greatly promoted spatial crowdsourcing, where the core issue is how to assign tasks to workers efficiently and with high quality. In this paper, we build a novel visual analysis system for spatial crowdsourcing, namely AMRAS, which can not only intuitively present the task allocation for workers under different time window scales to users (e.g., data analysts and managers) in real-time, but also help users analyze task assignment decision model and its learning process. AMRAS has the following novel features. First, AMRAS provides two user-friendly interfaces that allow users to employ simple and easy-to-use console to perform statistical analysis. Secondly, AMRAS provides three powerful visualization tools, such as the visualization of assignment results, assignment process, and assignment decision model, which not only allow users to intuitively analyze the whole process of task assignment, but also help users discover the computational bottleneck of their task assignment solution. Finally, AMRAS enables online access to real-time data, providing users with instant assignment and instant analysis. We have implemented and deployed AMRAS on Alibaba Cloud and demonstrated its usability and efficiency in real-world datasets. The demonstration video of AMRAS has been uploaded to Google Drive.
AvantGraph query processing engine
Authors: Leeuwen Wilco v. , Mulder Thomas , van de Wall Bram , Fletcher George , Yakovets Nikolay
Published: September 2022

We demonstrate AvantGraph, a graph query processing engine developed by the Database group at TU Eindhoven. Designed for efficient processing of
both
subgraph matching and navigational graph queries, AvantGraph encompasses innovation in three key areas: the planner, the cardinality estimator, and the execution engine. We present demonstration scenarios covering a wide range of workloads across diverse domains which (1) provides deep insights into the core challenges of complex graph query processing and (2) showcases corresponding critical optimizations via "under-the-hood" operational insights of AvantGraph's key components.
YeSQL
Authors: Foufoulas Yannis , Simitsis Alkis , Ioannidis Yannis
Published: September 2022

The diversity and complexity of modern data management applications led to the extension of the relational paradigm with syntactic and semantic support for User-Defined Functions (UDFs). Although well-established in traditional DBMS settings, UDFs have become even more central in many applications spanning data science, data analytics, etc. Still, a critical limitation of UDFs, which to some extent has turned data scientists towards NoSQL systems, is the impedance mismatch between their evaluation and relational processing. We present YeSQL, an SQL extension with rich UDF support along with a pluggable architecture to easily integrate it with either server-based or embedded database engines. We currently support UDFs written in Python, which are fully integrated with relational queries as scalar functions, aggregators, or table returning functions. Key novel characteristics of YeSQL include easy implementation of complex algorithms, tracing JIT compilation of Python UDFs, and seamless integration with a database engine. Our demonstration will showcase (a) the usability and expressiveness of our approach, and (b) that our techniques of minimizing context switching between the relational engine and the Python VM are very effective and achieve significant speedups in common, practical use cases.
Densest subgraph discovery on large graphs
Authors: Fang Yixiang , Luo Wensheng , Ma Chenhao
Published: September 2022

As one of the most fundamental problems in graph data mining, the
densest subgraph discovery
(DSD) problem has found a broad spectrum of real applications, such as social network community detection, graph index construction, regulatory motif discovery in DNA, fake follower detection, and so on. Theoretically, DSD closely relates to other fundamental graph problems, such as network flow and bipartite matching. Triggered by these applications and connections, DSD has garnered much attention from the database, data mining, theory, and network communities.

In this tutorial, we first highlight the importance of DSD in various applications and the unique challenges that need to be addressed. Subsequently, we classify existing DSD solutions into several groups, which cover around 50 research papers published in many well-known venues (e.g., SIGMOD, PVLDB, TODS, WWW), and conduct a thorough review of these solutions in each group. Afterwards, we analyze and compare the models and solutions in these works. Finally, we point out a list of promising future research directions. We believe that this tutorial not only helps researchers have a better understanding of existing densest subgraph models and solutions, but also provides them insights for future study.
Unified data analytics
Authors: Kaoudi Zoi , Quiané-Ruiz Jorge-Arnulfo
Published: September 2022

There is an urgent need for unifying data analytics as more and more application tasks become more complex: Nowadays, it is normal to see tasks performing data preparation, analytical processing, and machine learning operations in a single pipeline. Despite this need, achieving this is still a dreadful process where developers have to get familiar with many data processing platforms and write ad hoc scripts for integrating them. This tutorial is motivated by this need from both academia and industry. We will discuss the importance of unifying data processing as well as the current efforts to achieve it. In particular, we will introduce a classification of the different cases where an application needs or benefits from data analytics unification and discuss the challenges in each case. Along with this classification, we will also present current efforts known up to date that aim at unifying data processing, such as Apache Beam and Apache Wayang, and emphasize their differences. We will conclude with open problems and their challenges.
Managing forests for culturally significant plants in traditional Cherokee homelands: emerging platforms
Authors: Baumflek M. , Cabe T. , Schelhas J. , Dunlavey M.
Published: September 2022

The Eastern Band of Cherokee Indians (EBCI), and the USDA Forest Service are engaged in integrated research and action to manage forests for culturally significant plants within portions of traditional Cherokee homelands. The effort seeks to support EBCI arts, food sovereignty, and
cultural practices while promoting coordinated forest stewardship guided by Cherokee knowledge. The focus area includes the Qualla Boundary (EBCI tribal lands); the Pisgah, Nantahala, and Cherokee National Forests; and the Great Smoky Mountains National Park. Examination of synergistic efforts
to date suggests an emerging platform for access, sustainable harvesting and improved ecological conditions. Here, a platform is conceived of as long-term institutionalized collaboration across policies, practices, and governance at different scales. Building on approaches to adaptive collaborative
management with Indigenous peoples at the landscape level indicates that key features of platform building in this context include responding to Indigenous priorities, fostering cross-boundary relationships, community engagement, and co-producing knowledge.
Low loss flexibility and high efficiency of radio per fiber system for modern wireless communication system
Authors: Smirani Lassaad K. , Abd El-Aziz Ismail A. , Ahammad SK Hasane , Hossain Md. Amzad , Daher Malek G. , Rashed Ahmed Nabih Zaki
Published: September 2022

This article outlined intermediate/radio frequencies over fiber system performance efficiency measurement for the designed circuit frequencies. Output power, max. Q factor, and optical and electrical signal per noise ratio are also reported against radio and intermediate frequencies for the suggested system with the previous system. The suggested system has reported better efficiency and low loss flexibility than the previous system in the mentioned performance parameters. This study has clarified the low loss flexibility and high efficiency of radio per fiber system for modern wireless optical systems. We have clarified intermediate/radio frequencies over fiber system performance efficiency measurement in the presence of s and z parameters for the designed circuit frequencies. The comparison of the performance simulation parameters based on the previous/proposed models is clarified in the range of radio frequency and intermediate frequency range.
Intra-amniotic inflammation in the mid-trimester of pregnancy is a risk factor for neuropsychological disorders in childhood
Authors: Gervasi Maria Teresa , Romero Roberto , Cainelli Elisa , Veronese Paola , Tran Maria Rosa , Jung Eunjung , Suksai Manaphat , Bosco Mariachiara , Gotsch Francesca
Published: September 2022

Objectives

Intra-amniotic inflammation is a subclinical condition frequently caused by either microbial invasion of the amniotic cavity or sterile inflammatory stimuli, e.g., alarmins. An accumulating body of evidence supports a role for maternal immune activation in the genesis of fetal neuroinflammation and the occurrence of neurodevelopmental disorders such as cerebral palsy, schizophrenia, and autism. The objective of this study was to determine whether fetal exposure to mid-trimester intra-amniotic inflammation is associated with neurodevelopmental disorders in children eight to 12 years of age.

Methods

This is a retrospective case-control study comprising 20 children with evidence of prenatal exposure to intra-amniotic inflammation in the mid-trimester and 20 controls matched for gestational age at amniocentesis and at delivery. Amniotic fluid samples were tested for concentrations of interleukin-6 and C-X-C motif chemokine ligand 10, for bacteria by culture and molecular microbiologic methods as well as by polymerase chain reaction for eight viruses. Neuropsychological testing of children, performed by two experienced psychologists, assessed cognitive and behavioral domains. Neuropsychological dysfunction was defined as the presence of an abnormal score (<2 standard deviations) on at least two cognitive tasks.

Results

Neuropsychological dysfunction was present in 45% (9/20) of children exposed to intra-amniotic inflammation but in only 10% (2/20) of those in the control group (p=0.03). The relative risk (RR) of neuropsychological dysfunction conferred by amniotic fluid inflammation remained significant after adjusting for gestational age at delivery [aRR=4.5 (1.07–16.7)]. Of the 11 children diagnosed with neuropsychological dysfunction, nine were delivered at term and eight of them had mothers with intra-amniotic inflammation. Children exposed to intra-amniotic inflammation were found to have abnormalities in neuropsychological tasks evaluating complex skills, e.g., auditory attention, executive functions, and social skills, whereas the domains of reasoning, language, and memory were not affected in the cases and controls.

Conclusions

Asymptomatic sterile intra-amniotic inflammation in the mid-trimester of pregnancy, followed by a term birth, can still confer to the offspring a substantial risk for neurodevelopmental disorders in childhood. Early recognition and treatment of maternal immune activation in pregnancy may be a strategy for the prevention of subsequent neurodevelopmental disorders in offspring.
Rethinking of identity under war: Pryazovia renaissance and regional centre ambitions in Mariupol before 2022
Authors: Gnatiuk Oleksiy , Mezentsev Kostyantyn , Pidgrushnyi Grygorii
Published: September 2022

The article addresses identity transformation in geopolitical fault-line city under a semi-frozen military conflict. Until 2014, the Donbas, a region in the eastern Ukraine, had a strong identity cultivated by the local industrial and financial groups. The Russian-backed military conflict induced rethinking of Donbas identity, giving a chance for revival of silenced regional identities. Our case study is Mariupol, the second most populous city in Donetsk oblast and the informal capital of Pryazovia that stepped out from the shadow of Donetsk. The research is based on the survey data (n = 1,251) collected in 2020 through personal interviews, analysed using descriptive statistics and binary logistic regression. The hypothesis that emerging Pryazovia identity should qualitatively differ from presumably stigmatized Donbas identity was confirmed only partially. The identity rethinking seems to be neither rapid no straightforward. Donbas identity appears quite persistent, while Pryazovia identity functions mainly as a complementary one. Instead of escape from the stigmatized Donbas identity, we observe rather its redefinition, including on local-centric (“Mariupolocentrism”) and Ukraine-centric bases.