scholarly journals ServiceX A Distributed, Caching, Columnar Data Delivery Service

2020 ◽  
Vol 245 ◽  
pp. 04043
Author(s):  
B. Galewsky ◽  
R. Gardner ◽  
L. Gray ◽  
M. Neubauer ◽  
J. Pivarski ◽  
...  
Keyword(s):  
Data Delivery ◽  
Event Selection ◽  
Delivery Service ◽  
On Demand ◽  
Distributed Caching ◽  
Data Engineering ◽  
Analysis System

We will describe a component of the Intelligent Data Delivery Service being developed in collaboration with IRIS-HEP and the LHC experiments. ServiceX is an experiment-agnostic service to enable on-demand data delivery specifically tailored for nearly-interactive vectorized analysis. This work is motivated by the data engineering challenges posed by HL-LHC data volumes and the increasing popularity of python and Spark-based analysis workflows. ServiceX gives analyzers the ability to query events by dataset metadata. It uses containerized transformations to extract just the data required for the analysis. This operation is colocated with the data to avoid transferring unnecessary branches over the WAN. Simple filtering operations are supported to further reduce the amount of data transferred. Transformed events are cached in a columnar datastore to accelerate delivery of subsequent similar requests. ServiceX will learn commonly related columns and automatically include them in the transformation to increase the potential for cache hits by other users. Selected events are streamed to the analysis system using an efficient wire protocol that can be readily consumed by a variety of computational frameworks. This reduces time-to-insight for physics analysis by delegating to ServiceX the complexity of event selection, slimming, reformatting, and streaming.

scholarly journals A Systematic Approach to Order Fulfillment of On-demand Delivery Service for Bento Industry

2013 ◽  
Vol 17 ◽  
pp. 96-103
Author(s):  
Rong-Chang Chen ◽  
Chih-Hui Shieh ◽  
Kai-Ting Chan ◽  
Shin-Yi Chiu ◽  
Jyun-You Fan ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Order Fulfillment ◽  
Delivery Service ◽  
On Demand

Accelerating Data Delivery Service with Collaborated Parallel Traffic Scheduling for Distributed Datacenters

2021 ◽  
Author(s):  
Xiaoyuan Cao ◽  
Kai Luan ◽  
Xiang Luo ◽  
Qiang Bian ◽  
Zhi Li ◽  
...  
Keyword(s):  
Data Delivery ◽  
Traffic Scheduling ◽  
Delivery Service

scholarly journals An intelligent approach for analyzing the impacts of the COVID-19 pandemic on marketing mix elements (7Ps) of the on-demand grocery delivery service

2021 ◽  
Author(s):  
Burak Can Altay ◽  
Abdullah Okumuş ◽  
Burcu Adıgüzel Mercangöz
Keyword(s):  
Marketing Mix ◽  
Decision Makers ◽  
Comparative Approach ◽  
Grocery Shopping ◽  
Delivery Service ◽  
On Demand ◽  
Self Service ◽  
Intelligent Approach ◽  
The Impact ◽  
Hierarchy Process

AbstractDue to the impact of the COVID-19 pandemic, on-demand grocery delivery service that combines mobile technology and city logistics has gained tremendous popularity among grocery shoppers as a substitute to self-service grocery shopping in the store. This paper proposes an intelligent comparative approach where fuzzy logic and the analytical hierarchy process (AHP) method are combined to determine the importance weights of the criteria for marketing mix elements (7Ps) of the on-demand grocery delivery service for the period before COVID-19 and during COVID-19. In addition to its comprehensive theoretical insight, this paper provides a practical contribution to decision makers who create a marketing mix for the on-demand grocery delivery service and other similar online grocery businesses in terms of efficient allocation of resources to the development of marketing mix elements. The study’s findings can also provide clues for the decision makers in times of similar pandemics and crises that are likely to be seen in the future.

scholarly journals Towards an Intelligent Data Delivery Service

2020 ◽  
Vol 245 ◽  
pp. 04015
Author(s):  
Wen Guan ◽  
Tadashi Maeno ◽  
Gancho Dimitrov ◽  
Brian Paul Bockelman ◽  
Torre Wenaus ◽  
...  
Keyword(s):  
Data Processing ◽  
Production Systems ◽  
Event Processing ◽  
Data Delivery ◽  
Data Traffic ◽  
Delivery Service ◽  
Fine Grained ◽  
Ess Model ◽  
Streaming Service ◽  
Model View

The ATLAS Event Streaming Service (ESS) at the LHC is an approach to preprocess and deliver data for Event Service (ES) that has implemented a fine-grained approach for ATLAS event processing. The ESS allows one to asynchronously deliver only the input events required by ES processing, with the aim to decrease data traffic over WAN and improve overall data processing throughput. A prototype of ESS was developed to deliver streaming events to fine-grained ES jobs. Based on it, an intelligent Data Delivery Service (iDDS) is under development to decouple the “cold format” and the processing format of the data, which also opens the opportunity to include the production systems of other HEP experiments. Here we will at first present the ESS model view and its motivations for iDDS system. Then we will also present the iDDS schema, architecture and the applications of iDDS.

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