A container-based solution to generate HTCondor Batch Systems on demand exploiting heterogeneous Clouds for data analysis

The proliferation of free on-demand music streaming services (e.g., Spotify) is offsetting the traditional revenue sources (e.g., purchases of downloads or CDs) of the music industry. In order to increase revenue and sustain business, the music industry is directing its efforts toward increasing paid subscriptions by converting free listeners into paying subscribers. However, most companies are struggling with these attempts because they lack a clear understanding of the psychological and social purchase motivations of consumers. This study compares and contrasts the two different phases of Millennial generation consumer behaviors: the alluring phase and the hooking phase. A survey was conducted with 73 paying users and 163 non-paying users of on-demand music streaming services. The authors' data analysis shows two separate behavioral dynamics seen between these groups of users. While social influence and attitude are primary drivers for the non-paying users in the alluring phase, facilitating conditions and communication control capacity play critical roles for the paying users in the hooking phase. These results imply that the music industry should apply different approaches to prospective and current customers of music streaming services.

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The economics of inpatient on-demand treatment for haemophilia with high-responding inhibitors: a US retrospective data analysis

Haemophilia ◽

10.1111/j.1365-2516.2011.02623.x ◽

2011 ◽

Vol 18 (2) ◽

pp. 284-290 ◽

Cited By ~ 8

Author(s):

S. M. POKRAS ◽

A. A. PETRILLA ◽

J. WEATHERALL ◽

W. C. LEE

Keyword(s):

Data Analysis ◽

Retrospective Data ◽

On Demand ◽

Retrospective Data Analysis

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Use of reorderable matrices and heatmaps to support data analysis of students transcripts

10.5753/sibgrapi.est.2019.8334 ◽

2019 ◽

Author(s):

Rafael Tavares Carvalho Barros ◽

Thiago Gonçalves Mendes ◽

Celmar Guimarães Da Silva

Keyword(s):

Data Analysis ◽

Visual Representations ◽

Numerical Approach ◽

Specific Subject ◽

On Demand ◽

Color Scale ◽

Student Grades ◽

Order Algorithm

For a course coordinator, the analysis of several students’ transcripts to identify the situation of subjects or students is often an old-fashioned process executed through a textual and numerical approach. This work is part of a larger project aimed at choosing appropriate visual representations to help course coordinators to analyze sets of students transcripts. In this work, we developed a system that allows the visualization of student transcripts through a heatmap of student grades per subject. The heatmap represent grades based on a user-defined color scale. To assist in the analysis, it is possible to reorder subjects and students using the optimal leaf order algorithm, or even to reorder according to the grades of a specific subject or student. In addition, some features have been developed to meet visual guidelines, such as overview, zoom, filter and details-on-demand.

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Dynamic integration of distributed, Cloud-based HPC and HTC resources using JSON Web Tokens and the INDIGO IAM Service

EPJ Web of Conferences ◽

10.1051/epjconf/202024507020 ◽

2020 ◽

Vol 245 ◽

pp. 07020

Author(s):

Danele Spiga ◽

Stefano Dal Pra ◽

Davide Salomoni ◽

Andrea Ceccanti ◽

Roberto Alfieri

Keyword(s):

Enabling Technology ◽

On Demand ◽

The Past ◽

Batch Systems ◽

Batch System ◽

Opportunistic Computing ◽

User Communities ◽

Increasing Demand ◽

Cloud Infrastructures ◽

Distributed Cloud

In the past couple of years, we have been actively developing the Dynamic On-Demand Analysis Service (DODAS) as an enabling technology to deploy container-based clusters over hybrid, private or public, Cloud infrastructures with almost zero effort. DODAS is particularly suitable for harvesting opportunistic computing resources; this is why several scientific communities already integrated their computing use cases into DODAS-instantiated clusters, automating the instantiation, management and federation of HTCondor batch systems. The increasing demand, availability and utilization of HPC resources by and for multidisciplinary user communities, often mandates the possibility to transparently integrate, manage and mix HTC and HPC resources. In this paper, we discuss our experience extending and using DODAS to connect HPC and HTC resources in the context of a distributed Italian regional infrastructure involving multiple sites and communities. In this use case, DODAS automatically generates HTCondor batch system on-demand. Moreover it dynamically and transparently federates sites that may also include HPC resources managed by SLURM; DODAS allows user workloads to make opportunistic and automated use of both HPC and HTC resources, thus effectively maximizing and optimizing resource utilization. We also report on our experience of using and federating HTCondor batch systems exploiting the JSON Web Token capabilities introduced in recent HTCondor versions, replacing the traditional X509 certificates in the whole chain of workload authorization. In this respect we also report on how we integrated HTCondor using OAuth with the INDIGO IAM service.

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Coffea-casa: an analysis facility prototype

EPJ Web of Conferences ◽

10.1051/epjconf/202125102061 ◽

2021 ◽

Vol 251 ◽

pp. 02061

Author(s):

Matous Adamec ◽

Garhan Attebury ◽

Kenneth Bloom ◽

Brian Bockelman ◽

Carl Lundstedt ◽

...

Keyword(s):

Data Analysis ◽

Data Access ◽

End Users ◽

Command Line ◽

Web Interface ◽

Command Line Interface ◽

Interactive Interface ◽

Batch Systems ◽

Interactive Computing ◽

Programming Paradigms

Data analysis in HEP has often relied on batch systems and event loops; users are given a non-interactive interface to computing resources and consider data event-by-event. The “Coffea-casa” prototype analysis facility is an effort to provide users with alternate mechanisms to access computing resources and enable new programming paradigms. Instead of the command-line interface and asynchronous batch access, a notebook-based web interface and interactive computing is provided. Instead of writing event loops, the columnbased Coffea library is used. In this paper, we describe the architectural components of the facility, the services offered to end users, and how it integrates into a larger ecosystem for data access and authentication.

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ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN SALAK PONDOH DI PASAR BUAH KOTA LHOKSEUMAWE

Jurnal Ekonomi Pertanian Unimal ◽

10.29103/jepu.v2i2.1693 ◽

2019 ◽

Vol 2 (2) ◽

pp. 93

Author(s):

Ita Puspita Sari ◽

Cut Putri Mellita Sari

Keyword(s):

Data Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Income Level ◽

Primary Data ◽

Analysis Method ◽

Income Levels ◽

On Demand ◽

Negative Effect ◽

Positive Effect

The purpose of this study was to know the effect of Salak Pondok Prices, Medan Salak Prices and Income Levels on the Demand for Salak Pondok in the Fruit Market of Lhokseumawe City. This study used primary data sourced from 100 respondents. The data analysis method used in this study was multiple linear regression with the help of Eviews 10. The results of the study showed that Salak Pondok prices had a positive effect on demand, while the Salak Medan prices had a negative effect on demand, and the income levels had an effect positively on demand, but simultaneously, Salak Pondok prices and Salak Medan prices, and income level had a positive effect on the demand for Salak Pondok in the fruit market of Lhokseumawe City and the magnitude of the effect of Salak Pondok prices, Salak Medan Prices and income levels on demand (R2) was 0.5340 (53.40%).

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Dynamic and on demand data streams

EPJ Web of Conferences ◽

10.1051/epjconf/201921404030 ◽

2019 ◽

Vol 214 ◽

pp. 04030

Author(s):

Matteo Duranti ◽

Valerio Formato ◽

Valerio Vagelli

Keyword(s):

Data Analysis ◽

Data Streams ◽

Original Data ◽

End Users ◽

Experiment Data ◽

On Demand ◽

Analysis Strategy ◽

Computing Centers ◽

Definition Of ◽

High Level

Replicability and efficiency of data processing on the same data samples are a major challenge for the analysis of data produced by HEP experiments. High level data analyzed by end-users are typically produced as a subset of the whole experiment data sample to study interesting selection of data (streams). For standard applications, streams may be eventually copied from servers and analyzed on local computing centers or user machine clients. The creation of streams as copy of a subset of the original data results in redundant information stored in filesystems and may be not efficient: if the definition of streams changes, it may force a reprocessing of the low-level files with consequent impact on the data analysis efficiency. We propose an approach based on a database of lookup tables intended for dynamic and on-demand definition of data streams. This enables the end-users, as the data analysis strategy evolves, to explore different definitions of streams with minimal cost in computing resources. We also present a prototype demonstration application of this database for the analysis of the AMS-02 experiment data.

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