Difference between revisions of "DSMM: Data Science for Macro-Modeling with Financial and Economic Datasets"

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==Overview==
 
==Overview==
  
'''Focus of the Workshop''':  
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'===Focus of the Workshop===:  
 
The increasing availability of Open Data from a variety of sources including the Web, social media and the government, in conjunction with the growth of Big Data infrastructures and analytics tools, provides the ability to model complex ecosystems enabling cyber-human decision making. While data-driven models have emerged for a range of challenges from climate modeling to systems biology to personalized medicine, there has been relatively, little activity in macro-modeling using multiple heterogeneous financial and economic datasets.
 
The increasing availability of Open Data from a variety of sources including the Web, social media and the government, in conjunction with the growth of Big Data infrastructures and analytics tools, provides the ability to model complex ecosystems enabling cyber-human decision making. While data-driven models have emerged for a range of challenges from climate modeling to systems biology to personalized medicine, there has been relatively, little activity in macro-modeling using multiple heterogeneous financial and economic datasets.
  

Revision as of 19:54, 10 June 2014

DSMM 2014 Schedule

8:30 a.m. to 10 a.m. - Welcome and KEYNOTE and Opening Session on Financial Analytics
Session1
 
10:30 a.m. to 12 noon - Financial Data Integration Tools and Methods and POSTER SLAM
Session 2
 
1:30 p.m. to 3 p.m. - Financial Networks and Games and POSTER STROLL
Session 3
 
3:30 p.m. to 5 p.m. - Financial Data Challenges and Wrap-up
Session 4

Accepted Papers

list-of-papers

Accepted Posters

list-of-posters

DSfin Financial Entity Resolution At Scale Challenge

Overview

'===Focus of the Workshop===: The increasing availability of Open Data from a variety of sources including the Web, social media and the government, in conjunction with the growth of Big Data infrastructures and analytics tools, provides the ability to model complex ecosystems enabling cyber-human decision making. While data-driven models have emerged for a range of challenges from climate modeling to systems biology to personalized medicine, there has been relatively, little activity in macro-modeling using multiple heterogeneous financial and economic datasets.

The real promise of Open Data and Big Data lies in the dramatically increased value gained from integrating data from multiple sources, as illustrated by the following example: The systemic risks associated with the subprime lending market and the crash of the housing market in 2007 could have been modeled through a comprehensive integration and analysis of available public datasets. For example, the datasets relevant to the home mortgage supply chain include the following: (a) regulatory documents made available by MBS issuers, publicly traded financial institutions and mutual funds; (b) subscription-based third party datasets on underlying mortgages; (c) individual home transaction data such as sales, foreclosure and tax records; (d) local economic data such as employment and income-levels; (e) financial news articles. Integrating these datasets may have provided financial analysts, regulators and academic researchers, with comprehensive models to enable risk assessment.

Economists have been the leaders in creating longitudinal panel datasets and have had a successful history of using national datasets from the Census Bureau, the Department of Labor, etc., and global datasets from the UN, World Bank, etc. Here, too, there has been much less activity in modeling that integrated multiple heterogeneous datasets. While integrating datasets may pose technical, policy and privacy challenges, the potential benefits are immense. For example, social media data often contains features that could enhance macroeconomic statistics derived from traditional survey-driven datasets. Enriching longitudinal panel datasets with social media could explore hypotheses with a different focus or level of granularity; for example, one could study the decision making of individuals whose social media profiles would reflect their beliefs, intent, interests, sentiments, opinions, and state of mind.

This workshop will explore the challenges of data science for macro-modeling with financial and/or economic datasets. Two workshops, in 2010 and 2012, brought together a diverse community of academic researchers, regulators and practitioners who articulated the range of multi-disciplinary research challenges for macro-prudential modeling of financial systemic risk. The National Bureau of Economic Research Summer Institute in 2012 offered a workshop on novel data-centric techniques that attracted both economists and computer scientists. The workshop will target attendees of these prior meetings and will build upon the solid foundation established at these prior events.

Targeted Audience: We expect a mix of paper submissions and attendees with an interest in information integration, data mining, knowledge representation, stream data processing, etc. A small number of domain specialists from finance and economics are also expected to attend.

Important Dates

Submission deadline:     EXTENDED!!! Monday March 31, 2014. EXTENDED!!!
Notification to authors: Friday May 2, 2014.
Camera-ready due:        Friday May 23, 2014.
Registration deadline: 
Workshop:                Friday June 27, 2014.

Submission Format: Authors are invited to submit original, unpublished research papers that are not being considered for publication in any other forum. We will accept the following types of papers:

* Regular papers that are a maximum of 6 pages will have a presentation slot.
* Extended abstracts of up to 2 pages will have a poster presentation and a short presentation slot 
   if time permits.

Manuscripts should be submitted electronically as PDF files and be formatted using the SIGMOD camera-ready templates templates. Authors are allowed to include extra material beyond the six pages as a clearly marked appendix, which reviewers are not obliged to read.

Submission Site

https://cmt.research.microsoft.com/DSMM2014/

Organization

Program Chairs

Rajasekar Krishnamurthy IBM Research rajase@us.ibm.com
Louiqa Raschid University of Maryland louiqa@umiacs.umd.edu
Shiv Vaithyanathan IBM Research vaithyan@us.ibm.com

Steering Committee

Lise Getoor University of California Santa Cruz getoor@soe.ucsc.edu
Laura Haas IBM Research lmhaas@us.ibm.com
H.V. Jagadish University of Michigan jag@umich.edu

Program Committee

Richard Anderson Lindenwood University rganderson.stl@gmail.com
Michael Cafarella University of Michigan michjc@umich.edu
Sanjiv Das Santa Clara University srdas@scu.edu
Amol Deshpande University of Maryland amol@cs.umd.edu
Mark Flood Office of Financial Research mark.flood@treasury.gov
Juliana Freire New York University juliana.freire@nyu.edu
Gerard Hoberg University of Maryland ghoberg@rhsmith.umd.edu
Vasant Honavar Pennsylvania State University vhonavar@ist.psu.edu
Joe Langsam University of Maryland jlangsam@rhsmith.umd.edu
Shawn Mankad University of Maryland smankad@rhsmith.umd.edu
Frank Olken National Science Foundation folken@nsf.gov
Felix Naumann Hasso Plattner Institute, Germany felix.naumann@hpi.uni-potsdam.de
Christopher Ré Stanford University chrismre@cs.stanford.edu
Webmaster
Peratham Wiriyathammabhum University of Maryland peratham@cs.umd.edu

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