SOC Hw 4
by Juan Pablo Lozano

SOC Hw 4

Publications
GET /api/publications

Responses

200 OK
Body
Array
Object
id
integer
Example:
1000
publtype
string
Example:
inproceedings
title
string
Example:
BizCast: Business Process Performance Model with Workload Overlap Analysis.
booktitle
string
Example:
ICWS
pages
string
Example:
445-452
year
integer
Example:
2006
address
unknown nullable
volume
unknown nullable
journal
unknown nullable
number
integer
url
string
Example:
db/conf/icws/icws2006.html#KoizumiHF06
crossref
string
Example:
conf/icws/2006
mdate
unknown nullable
cdate
unknown nullable
publisher
unknown nullable
isbn
unknown nullable
publicationchannel
string
Example:
Conference book
abstract_text
string
Example:
In today's fast and ever changing business environments, it is very important to quickly adapt business processes to new business requirements, and to optimize their performance. From this viewpoint, business process performance prediction plays a key role in directing the business. Previous studies have shown that a network queuing model is able to predict the business process performance. In order to do this, the model requires details of the service's inside behavior. This paper presents a business process performance model called `BizCast', which enables us to estimate business process execution time under any given condition and not to require details of the service's inside behavior. We especially focus on the overlap between business process instances to predict the performance based on the outside behavior. Prototype evaluation of the service model for supply chain services shows high accuracy, which is, at least, almost 0.9 correlation coefficient
authors
Array
Example: 
["Satoru Fujita","Seiichi Koizumi","Shigeru Hosono"]
string
Example:
Satoru Fujita
categories
Array
Example: 
["topic8"]
string
Example:
topic8
Publication
GET /api/publications/:id

Responses

200 OK
Body
Object
id
integer
Example:
1000
publtype
string
Example:
inproceedings
title
string
Example:
BizCast: Business Process Performance Model with Workload Overlap Analysis.
booktitle
string
Example:
ICWS
pages
string
Example:
445-452
year
integer
Example:
2006
address
unknown nullable
volume
unknown nullable
journal
unknown nullable
number
integer
url
string
Example:
db/conf/icws/icws2006.html#KoizumiHF06
crossref
string
Example:
conf/icws/2006
mdate
unknown nullable
cdate
unknown nullable
publisher
unknown nullable
isbn
unknown nullable
publicationchannel
string
Example:
Conference book
abstract_text
string
Example:
In today's fast and ever changing business environments, it is very important to quickly adapt business processes to new business requirements, and to optimize their performance. From this viewpoint, business process performance prediction plays a key role in directing the business. Previous studies have shown that a network queuing model is able to predict the business process performance. In order to do this, the model requires details of the service's inside behavior. This paper presents a business process performance model called `BizCast', which enables us to estimate business process execution time under any given condition and not to require details of the service's inside behavior. We especially focus on the overlap between business process instances to predict the performance based on the outside behavior. Prototype evaluation of the service model for supply chain services shows high accuracy, which is, at least, almost 0.9 correlation coefficient
authors
Array
Example: 
["Satoru Fujita","Seiichi Koizumi","Shigeru Hosono"]
string
Example:
Satoru Fujita
categories
Array
Example: 
["topic8"]
string
Example:
topic8
show more
Categories
GET /api/categories

Responses

200 OK
Body
Array
Object
id
integer
Example:
1
name
string
Example:
topic0
num_publications
integer
Example:
103
num_articles
integer
Example:
22
num_inproceedings
integer
Example:
81
keywords
string
Example:
data , storage , processing , big , system , query , queries , analysis , database , paper , large , information , sources , present , applications , stream , analytics , databases , efficient , performance , systems , results , real-time , mining , high