FIRST CALL FOR PAPERS
Workshop on Data Science for Macro-Modeling DSMM2014
dsmm2014.org
Held in conjunction with ACM SIGMOD 2014
Friday June 27, 2014
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 macromodeling with financial and/or economic datasets. 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:
Notification to authors:
Camera-ready due:
Workshop:
Friday
Monday
Friday
Friday
March 21, 2014.
April 28, 2014.
May 16, 2014.
June 27, 2014.
SUBMISSION FORMAT
We will accept the following types of papers in the SIGMOD format:
• 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.
SUBMISSION SITE
https://cmt.research.microsoft.com/DSMM2014/
PROGRAM CHAIRS
Rajasekar Krishnamurthy IBM Research
Louiqa Raschid
University of Maryland
Shiv Vaithyanathan
IBM Research
rajase@us.ibm.com
louiqa@umiacs.umd.edu
vaithyan@us.ibm.com
STEERING COMMITTEE
Lise Getoor
Laura Haas
H.V. Jagadish
getoor@soe.ucsc.edu
lmhaas@us.ibm.com
jag@umich.edu
UC Santa Cruz
IBM Research
University of Michigan
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 U
vhonavar@ist.psu.edu
Joe Langsam
University of Maryland jlangsam@rhsmith.umd.edu
Shawn Mankad
University of Maryland smankad@rhsmith.umd.edu
Felix Naumann
Hasso Plattner Institute, Germany
felix.naumann@hpi.uni-potsdam.de
Frank Olken
National Science Foundation
folken@nsf.gov
Christopher Re
Stanford University
chrismre@cs.stanford.edu
WEBMASTER
Peratham Wiriyathammabhum University of Maryland
peratham@cs.umd.edu