Companion of the 2023 International Conference on Management of Data最新文献_第6页

PyNKDV: An Efficient Network Kernel Density Visualization Library for Geospatial Analytic Systems 地理空间分析系统的高效网络核密度可视化库

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589711

Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu

{"title":"PyNKDV: An Efficient Network Kernel Density Visualization Library for Geospatial Analytic Systems","authors":"Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu","doi":"10.1145/3555041.3589711","DOIUrl":"https://doi.org/10.1145/3555041.3589711","url":null,"abstract":"Network kernel density visualization (NKDV) is an important tool for many application domains, including criminology and transportation science. However, all existing software tools, e.g., SANET (a plug-in for QGIS and ArcGIS) and spNetwork (an R package), adopt the naïve implementation of NKDV, which does not scale to large-scale location datasets and high-resolution sizes. To overcome this issue, we develop the first python library, called PyNKDV, which adopts our complexity-reduced solution and its parallel implementation to significantly improve the efficiency for generating NKDV. Moreover, PyNKDV is also user friendly (with four lines of python code) and can support commonly used geospatial analytic systems (e.g., QGIS and ArcGIS). In this demonstration, we will use three large-scale location datasets (up to 7.71 million data points), provide different python scripts (in the Jupyter Notebook), and install existing software tools (i.e., SANET and spNetwork) for participants to (1) explore different functionalities of our PyNKDV library and (2) compare its practical efficiency with existing software tools.","PeriodicalId":161812,"journal":{"name":"Companion of the 2023 International Conference on Management of Data","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123548524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Large-scale Geospatial Analytics: Problems, Challenges, and Opportunities 大规模地理空间分析:问题、挑战和机遇

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589401

Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, R. Cheng

{"title":"Large-scale Geospatial Analytics: Problems, Challenges, and Opportunities","authors":"Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, R. Cheng","doi":"10.1145/3555041.3589401","DOIUrl":"https://doi.org/10.1145/3555041.3589401","url":null,"abstract":"Geospatial analytics is an important field in many communities, including crime science, transportation science, epidemiology, ecology, and urban planning. However, with the rapid growth of big geospatial data, most of the commonly used geospatial analytic tools are not efficient (or even feasible) to support large-scale datasets. As such, domain experts have raised the concerns about the inefficiency issues for using these tools. In this tutorial, we aim to arouse the attention of database researchers for this important, emerging, database-related, and interdisciplinary topic, which consists of four parts. In the first part, we will discuss different problems and highlight the challenges for two types of geospatial analytic tools, which are (1) hotspot detection and (2) correlation analysis. In the second and third parts, we will specifically discuss two geospatial analytic tools, namely kernel density visualization (the representative hotspot detection method) and K-function (the representative correlation analysis method), respectively, and their variants. In the fourth part, we will highlight the future opportunities for this topic.","PeriodicalId":161812,"journal":{"name":"Companion of the 2023 International Conference on Management of Data","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125130712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

SmokedDuck Demonstration: SQLStepper 熏鸭示范:sqlstep

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589731

Haneen Mohammed, Charlie Summers, Sughosh Kaushik, Eugene Wu

引用次数: 0

Future of Database System Architectures 数据库系统架构的未来

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589360

G. Alonso, N. Ailamaki, S. Krishnamurthy, S. Madden, S. Sivasubramanian, R. Ramakrishnan

{"title":"Future of Database System Architectures","authors":"G. Alonso, N. Ailamaki, S. Krishnamurthy, S. Madden, S. Sivasubramanian, R. Ramakrishnan","doi":"10.1145/3555041.3589360","DOIUrl":"https://doi.org/10.1145/3555041.3589360","url":null,"abstract":"Over the past two decades, we have experienced major technology disruptions on multiple fronts, none bigger than the emergence of cloud computing, which has led to fundamental changes in how database software is architected. We are seeing several new trends that are similarly shaping the future of data management. With the demise of Moore's Law, we are now seeing a lot of interest (and start-ups with significant investments) in hardware database accelerators, exploring FPGAs, GPUs, and more. Economies of scale in the cloud make it possible to move to hardware many things that were done in software, the trend will continue and increase. Modern data estates are spread across data located on premises, on the edge and in one or more public clouds, spread across various sources like multiple relational databases, file and storage systems, and no-SQL systems, both operational and analytic. This phenomenon is referred to as data sprawl. We are also seeing the emergence of many novel data workloads. For example, rich data pipelines are an increasingly common workload. And finally, Machine Learning is having a rapidly increasing role in every aspect of the database software lifecycle. This SIGMOD panel will discuss the impact of the above changes and trends on database hardware and software architectures. How will these changes impact DB system design, how will DB systems look like in the near future? Where are the hardest research challenges? What learnings from the past will guide us through these disruptions?","PeriodicalId":161812,"journal":{"name":"Companion of the 2023 International Conference on Management of Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115437289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Demonstration of KAMEL: A Scalable BERT-based System for Trajectory Imputation 基于bert的可扩展轨迹输入系统KAMEL的演示

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589733

Mashaal Musleh, M. Mokbel

引用次数: 4

Demonstrating NaturalMiner: Searching Large Data Sets for Abstract Patterns Described in Natural Language 演示NaturalMiner:搜索用自然语言描述的抽象模式的大数据集

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589694

Immanuel Trummer

引用次数: 1

Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques 优化张量计算:从应用程序到编译和运行时技术

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589407

Matthias Boehm, Matteo Interlandi, Christopher M. Jermaine

{"title":"Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques","authors":"Matthias Boehm, Matteo Interlandi, Christopher M. Jermaine","doi":"10.1145/3555041.3589407","DOIUrl":"https://doi.org/10.1145/3555041.3589407","url":null,"abstract":"Machine learning (ML) training and scoring fundamentally relies on linear algebra programs and more general tensor computations. Most ML systems utilize distributed parameter servers and similar distribution strategies for mini-batch stochastic gradient descent training. However, many more tasks in the data science and engineering lifecycle can benefit from efficient tensor computations. Examples include primitives for data cleaning, data and model debugging, data augmentation, query processing, numerical simulations, as well as a wide variety of training and scoring algorithms. In this survey tutorial, we first make a case for the importance of optimizing more general tensor computations, and then provide an in-depth survey of existing applications, optimizing compilation techniques, and underlying runtime strategies. Interestingly, there are close connections to data-intensive applications, query rewriting and optimization, as well as query processing and physical design. Our goal for the tutorial is to structure existing work, create common terminology, and identify open research challenges.","PeriodicalId":161812,"journal":{"name":"Companion of the 2023 International Conference on Management of Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130860110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personal Data for Personal Use: Vision or Reality? 个人使用的个人数据:愿景还是现实?

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589378

X. Dong, Bo Li, Julia Stoyanovich, A. Tung, G. Weikum, A. Halevy, Wang-Chiew Tan

引用次数: 0

Fairness in Ranking: From Values to Technical Choices and Back 排名的公平性:从价值观到技术选择再回来

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589405

Julia Stoyanovich, Meike Zehlike, Ke Yang

引用次数: 0

SparkSQL+: Next-generation Query Planning over Spark SparkSQL+:基于Spark的下一代查询规划

Companion of the 2023 International Conference on Management of Data Pub Date : 2023-06-04 DOI: 10.1145/3555041.3589715

Binyang Dai, Qichen Wang, K. Yi

引用次数: 0