ACM Home Page
Please provide us with feedback. Feedback
Digital Library logoTake a look at the new version of this page: [ beta version ]. Tell us what you think.
Mashup-based information retrieval for domain experts
Full text PdfPdf (2.50 MB)
Source
Conference on Information and Knowledge Management archive
Proceeding of the 18th ACM conference on Information and knowledge management table of contents
Hong Kong, China
SESSION: Industry information retrieval table of contents
Pages: 711-720  
Year of Publication: 2009
ISBN:978-1-60558-512-3
Authors
Anand Ranganathan  IBM TJ Watson Research Center, Hawthorne, NY, USA
Anton Riabov  IBM TJ Watson Research Center, Hawthorne, NY, USA
Octavian Udrea  IBM TJ Watson Research Center, Hawthorne, NY, USA
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 23,   Downloads (12 Months): 92,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1645953.1646044
What is a DOI?

ABSTRACT

In this paper, we tackle the problem of helping domain experts to construct, parameterize and deploy mashups of data and code. We view a mashup as a data processing flow, that describes how data is obtained from one or more sources, processed by one or more components, and finally sent to one or more sinks. Our approach allows specifying patterns of flows, in a language called Cascade. The patterns cover different possible variations of the flows, including variations in the structure of the flow, the components in the flow and the possible parameterizations of these components. We present a tool that makes use of this knowledge of flow patterns and associated metadata to allow domain experts to explore the space of possible flows described in the pattern. The tool uses an AI planning approach to automatically build a flow, belonging to the flow pattern, from a high-level goal, specified as a set of tags. We describe examples from the financial services domain to show the use of flow patterns in allowing domain experts to construct a large variety of mashups rapidly.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
L. Cherbakov, A. J. F. Bravery, and A. Pandya. SOA meets situational applications, 2007.
3
 
4
5
 
6
IBM Infosphere Streams. http://www-01.ibm.com/software/data/infosphere/streams/.
 
7
IBM Mashup Center. http://www-01.ibm.com/software/info/mashup-center/.
 
8
9
 
10
S. Krishnan, P. Wagstrom, and G. V. Laszewski. GSFL: A workflow framework for grid services. Technical report, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, 2002.
 
11
 
12
Microsoft, Inc. http://www.popfly.com.
13
 
14
 
15
 
16
A. Riabov and Z. Liu. Scalable planning for distributed stream processing systems. In ICAPS, 2006.
17
 
18
 
19
Streambase. http://www.streambase.com/.
 
20
P. Traverso and M. Pistore. Automated composition of semantic web services into executable processes. In ISWC'04.
 
21
 
22
Yahoo, Inc. pipes.yahoo.com.

Collaborative Colleagues:
Anand Ranganathan: colleagues
Anton Riabov: colleagues
Octavian Udrea: colleagues