Titlebook: Data Management in Machine Learning Systems; Matthias Boehm,Arun Kumar,Jun Yang Book 2019 Springer Nature Switzerland AG 2019

damped

書(shū)目名稱Data Management in Machine Learning Systems影響因子(影響力)




書(shū)目名稱Data Management in Machine Learning Systems影響因子(影響力)學(xué)科排名




書(shū)目名稱Data Management in Machine Learning Systems網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Data Management in Machine Learning Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Data Management in Machine Learning Systems被引頻次




書(shū)目名稱Data Management in Machine Learning Systems被引頻次學(xué)科排名




書(shū)目名稱Data Management in Machine Learning Systems年度引用




書(shū)目名稱Data Management in Machine Learning Systems年度引用學(xué)科排名




書(shū)目名稱Data Management in Machine Learning Systems讀者反饋




書(shū)目名稱Data Management in Machine Learning Systems讀者反饋學(xué)科排名

天然熱噴泉

ascetic

LARK

Peak-Bone-Mass

使增至最大

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

使增至最大

預(yù)感

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

HALO

Ruptured-Disk

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