標(biāo)題: Titlebook: Data Management in Machine Learning Systems; Matthias Boehm,Arun Kumar,Jun Yang Book 2019 Springer Nature Switzerland AG 2019 [打印本頁(yè)] 作者: damped 時(shí)間: 2025-3-21 16:26
書目名稱Data Management in Machine Learning Systems影響因子(影響力)
書目名稱Data Management in Machine Learning Systems影響因子(影響力)學(xué)科排名
書目名稱Data Management in Machine Learning Systems網(wǎng)絡(luò)公開(kāi)度
書目名稱Data Management in Machine Learning Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Data Management in Machine Learning Systems被引頻次
書目名稱Data Management in Machine Learning Systems被引頻次學(xué)科排名
書目名稱Data Management in Machine Learning Systems年度引用
書目名稱Data Management in Machine Learning Systems年度引用學(xué)科排名
書目名稱Data Management in Machine Learning Systems讀者反饋
書目名稱Data Management in Machine Learning Systems讀者反饋學(xué)科排名
作者: 天然熱噴泉 時(shí)間: 2025-3-21 23:30 作者: ascetic 時(shí)間: 2025-3-22 03:57 作者: LARK 時(shí)間: 2025-3-22 06:04 作者: Peak-Bone-Mass 時(shí)間: 2025-3-22 12:23 作者: 使增至最大 時(shí)間: 2025-3-22 15:26
Marisol J. Voncken,Susan M. B?gelsnal model and query language seem to be a poor fit with ML, as most ML algorithms look very different from and oftentimes far more complicated than database queries. Thus, database systems have traditionally served as a data store for ML; the ML algorithm would pull the data out from the database, t作者: 使增至最大 時(shí)間: 2025-3-22 18:14 作者: 預(yù)感 時(shí)間: 2025-3-22 22:46
Cognitive Psychology: What Is In The Box?tems apply a broad range of rewrites and optimization techniques to improve the efficiency of ML programs. In this chapter, we first categorize existing systems according to their optimization scope, survey important classes of logical and physical rewrites, and also discuss means of adapting execut作者: HALO 時(shí)間: 2025-3-23 03:19 作者: Ruptured-Disk 時(shí)間: 2025-3-23 08:54
Evolution from Linear to Systems Thinkingent data access in ML systems. These techniques bear strong similarity with corresponding data access methods in database systems, with the difference of focusing on dense and sparse matrices or tensors, as well as specific access patterns of ML workloads. In this chapter, we survey existing techniq作者: Essential 時(shí)間: 2025-3-23 11:17 作者: languor 時(shí)間: 2025-3-23 15:07 作者: sterilization 時(shí)間: 2025-3-23 21:49 作者: 代替 時(shí)間: 2025-3-24 00:42 作者: 宏偉 時(shí)間: 2025-3-24 04:54
Data Management in Machine Learning Systems978-3-031-01869-5Series ISSN 2153-5418 Series E-ISSN 2153-5426 作者: 有節(jié)制 時(shí)間: 2025-3-24 08:40 作者: Minikin 時(shí)間: 2025-3-24 14:20 作者: 毛細(xì)血管 時(shí)間: 2025-3-24 16:58
ML Through Database Queries and UDFs,nal model and query language seem to be a poor fit with ML, as most ML algorithms look very different from and oftentimes far more complicated than database queries. Thus, database systems have traditionally served as a data store for ML; the ML algorithm would pull the data out from the database, t作者: 脫落 時(shí)間: 2025-3-24 22:19
Multi-Table ML and Deep Systems Integration,search expanded such integration along various dimensions. The first set focuses on making ML algorithm implementations more aware of the underlying data model of RDBMSs: the multi-table relational model. The second set focuses on deeper systems modifications to RDBMSs that are tailored toward ML wo作者: Outmoded 時(shí)間: 2025-3-25 02:00 作者: 贊成你 時(shí)間: 2025-3-25 06:58 作者: refine 時(shí)間: 2025-3-25 09:21 作者: insurgent 時(shí)間: 2025-3-25 15:43
Resource Heterogeneity and Elasticity,ve made it easier than ever to access computing resources. For example, a public cloud such as Amazon EC2 allows users to acquire a cluster on demand and pay only for its actual usage. There is a blossoming ecosystem of tools, libraries, and platforms for ML in the cloud and cluster computing settin作者: Torrid 時(shí)間: 2025-3-25 18:17 作者: Wallow 時(shí)間: 2025-3-25 22:33
Conclusions,ed systems, high-performance computing, programming languages, and more. Motivated by (1) data-driven applications, (2) data-intensive workload characteristics, and (3) data systems support for ML, we took a data-centric view and reviewed approaches for integrating ML in data systems as well as data作者: insightful 時(shí)間: 2025-3-26 03:35 作者: AER 時(shí)間: 2025-3-26 06:17
Execution Strategies, discuss interesting runtime techniques, including special runtime optimizers. Finally, in the context of deep learning, there is a trend toward exploiting accelerators such as GPUs, FPGAs, and custom ASICs for training and scoring, which is another specific hybrid execution strategy.作者: FLIT 時(shí)間: 2025-3-26 10:05 作者: 雪白 時(shí)間: 2025-3-26 14:37 作者: defendant 時(shí)間: 2025-3-26 20:05 作者: HATCH 時(shí)間: 2025-3-26 23:46
Introduction,ring, classification, regression, time series analysis, recommendations, and reinforcement learning, together with (2) application-specific pipelines that connect these algorithms with steps for preparing data, incorporating domain knowledge, interpreting results, and applying insights.作者: 下級(jí) 時(shí)間: 2025-3-27 02:19
Multi-Table ML and Deep Systems Integration,ata model of RDBMSs: the multi-table relational model. The second set focuses on deeper systems modifications to RDBMSs that are tailored toward ML workloads or creating new systems designed for specific ML workloads. In this chapter, we dive into both of these sets of systems that deepened the integration of ML with relational data management.作者: 抑制 時(shí)間: 2025-3-27 08:16 作者: 運(yùn)動(dòng)性 時(shí)間: 2025-3-27 12:26
Marisol J. Voncken,Susan M. B?gelsransform it into the appropriate format (e.g., matrices, tensors, or dataframes), and then analyze it using programs written in a different programming language. On the other hand, there are a number of compelling arguments for doing ML inside a database system.作者: A保存的 時(shí)間: 2025-3-27 16:35 作者: 減去 時(shí)間: 2025-3-27 20:29 作者: 蔑視 時(shí)間: 2025-3-27 22:01
The Problem of Epistemology [1936] of ML models into production. Tackling these challenges requires ideas and techniques that combine not just ML and data management, but also other fields of computing, including human-computer interaction and operating and distributed systems. We now dive into these auxiliary steps in the ML lifecycle in depth.作者: 無(wú)聊的人 時(shí)間: 2025-3-28 05:30
Cognitive Psychology: What Is In The Box?e database research community have contributed to their solutions. With this overview, we hope to illustrate the various optimization possibilities enabled by different levels of abstraction, and in particular, opportunities that become possible by having declarative specifications in the spirit of database systems.作者: antidote 時(shí)間: 2025-3-28 07:24
Resource Heterogeneity and Elasticity,e database research community have contributed to their solutions. With this overview, we hope to illustrate the various optimization possibilities enabled by different levels of abstraction, and in particular, opportunities that become possible by having declarative specifications in the spirit of database systems.作者: adjacent 時(shí)間: 2025-3-28 14:11
2153-5418 ng and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques...In this book,作者: 極端的正確性 時(shí)間: 2025-3-28 16:54
Marisol J. Voncken,Susan M. B?gelsring, classification, regression, time series analysis, recommendations, and reinforcement learning, together with (2) application-specific pipelines that connect these algorithms with steps for preparing data, incorporating domain knowledge, interpreting results, and applying insights.作者: deactivate 時(shí)間: 2025-3-28 19:10 作者: 擴(kuò)張 時(shí)間: 2025-3-29 02:03
Robert S. Cohen,Thomas Schnelleteristics, and (3) data systems support for ML, we took a data-centric view and reviewed approaches for integrating ML in data systems as well as data management techniques in ML systems. In the following, we draw several conclusions regarding the existing state of the art, as well as major open problems and directions for future work.作者: 新娘 時(shí)間: 2025-3-29 05:12
